СПОСОБ АНАЛИЗА ПОВЕРХНОСТИ РАЗРЫВА ДЕТАЛИ ТУРБОМАШИНЫ

Изобретение относится к анализу поверхности разрыва или трещины металлической детали турбомашины. Представлен способ анализа поверхности разрыва или трещины металлической детали турбомашины, при котором указанная поверхность соответствует плоскости разрыва или плоскости трещинообразования перед открытием в лаборатории для треснувшей, но не разорванной детали, включающий по меньшей мере один из следующих этапов, на которых: а) определяют на поверхности положение и ориентацию граней спайности, чтобы идентифицировать зону начала разрыва или трещины и определить направление распространения этого разрыва или трещины, b) исследуют поверхность и выявляют зоны присутствия равноосных зерен и/или пластинчатых зерен, чтобы оценить температуру, при которой произошел разрыв или трещина, и с) сравнивают цвет или цвета побежалости поверхности с цветами побежалости образцов из альбома цветов побежалости, причем эти образцы выполнены из такого же материала, что и деталь, и были подвергнуты окисляющим термическим ...

СИСТЕМЫ И СПОСОБЫ ДЛЯ ИСПОЛЬЗОВАНИЯ ИММУНООКРАШИВАЮЩЕЙ МАСКИ ДЛЯ СЕЛЕКТИВНОГО УЛУЧШЕНИЯ РЕЗУЛЬТАТОВ ISH-АНАЛИЗА

System zum patientenspezifischen Modellieren von Blutfluss

System zum Bestimmen von kardiovaskulären Informationen für einen Patienten, wobei das System Folgendes umfasst: wenigstens ein Computersystem, das konfiguriert ist, um: patientenspezifische Daten bezüglich einer Geometrie einer anatomischen Struktur des Patienten zu empfangen, wobei die anatomische Struktur wenigstens einen Abschnitt einer Mehrzahl an Koronararterien, die von einer Aorta ausgehen, beinhaltet; basierend auf den patientenspezifischen Daten ein dreidimensionales Modell zu erzeugen, das einen ersten Abschnitt der anatomischen Struktur repräsentiert, wobei der erste Abschnitt der anatomischen Struktur wenigstens den Abschnitt der Mehrzahl an Koronararterien beinhaltet; wenigstens teilweise basierend auf einer Masse oder einem Volumen des Myokardgewebes ein physikbasiertes Modell bezüglich einer Blutflusseigenschaft im ersten Abschnitt der anatomischen Struktur zu erzeugen; und wenigstens teilweise basierend auf dem dreidimensionalen Modell und dem physikbasierten Modell eine ...

VERFAHREN UND SYSTEM ZUM LERNEN VON VISUELLEM PIXELKONTEXT AUS OBJEKTEIGENSCHAFTEN ZUR ERZEUGUNG REICHER SEMANTISCHER BILDER

Verfahren, das Folgendes aufweist:Aufteilen eines digitalen Bildes in Kacheln;Bestimmen eines Grades von lokalem Kontrast in jeder der Kacheln;Auswählen einer ersten Vielzahl der Kacheln, die den größten Grad an lokalem Kontrast aufweist;Bestimmen einer durchschnittlichen Farbe jeder von der ersten Vielzahl von Kacheln;Aufteilen der ersten Vielzahl von Kacheln in Cluster von Kacheln mit ähnlichen Farben;Auswählen einer Lernkachel aus jedem Cluster von Kacheln, wobei jede Lernkachel den größten Grad an lokalem Kontrast unter den Kacheln des Clusters aufweist, zu dem die Lernkachel gehört;Segmentieren der Lernkacheln in Datenobjekte unter Verwendung objektorientierter Bildanalyse;Klassifizieren der Datenobjekte in Klassen von Objekten;Zuordnen einer Farbe zu jeder Klasse von Objekten;Bestimmen der Eigenschaften der Datenobjekte, die zu unterschiedlichen Klassen von Objekten gehören;Erzeugen von pixelweisen Deskriptoren, die die Klasse von Objekten angeben, zu der jedes Pixel der Lernkacheln ...

MYOKARDIALER BLUTFLUSS MIT ZUVERLÄSSIGKEITSMERKMAL

Verfahren, umfassend: Scannen eines Patienten mit einem PET-Scanner; Berechnen eines Patienten-myokardialen Blutfluss(MBF)-Parameterwerts und eines Patienten-MBF-Variationswertes des Patienten basierend auf dem Scannen; Vergleichen des Patienten-MBF-Variationswertes mit einem MBF-Variationsschwellenwert; und Bestimmen, dass der Patienten-MBF-Parameterwert unzuverlässig ist, als Reaktion auf ein Bestimmen, dass der Patienten-MBF-Variationswert größer als der MBF-Variationsschwellenwert ist.

Verfahren und Mikroskopiersystem zum Scannen einer Probe

Verfahren zum Scannen einer Probe mittels eines elektrisch und/oder elektronisch steuerbaren Mikroskops, wobei eine Vielzahl von Bildern, insbesondere digitalen Bildern, an unterschiedlichen Stellen der Probe und/oder zu unterschiedlichen Zeiten erzeugt wird und wobei das Mikroskop während des Scanvorgangs durch einen Steuerrechner gesteuert wird, dadurch gekennzeichnet, dass ein von dem Mikroskop erzeugtes Bild an mindestens einen von mehreren weiteren Rechnern übertragen wird, die untereinander und mit dem Steuerrechner über ein Netzwerk oder Teile eines Netzwerks verbunden sind, dass eine automatische Analyse übertragener Bilder durchgeführt wird, dass bedarfsgerecht zur Laufzeit des Scanvorgangs weitere Rechner zugeschaltet oder getrennt werden, so dass die erzeugten Bilder parallel und im Wesentlichen zeitgleich oder zeitnah zum Scanvorgang analysiert werden, und dass basierend auf den Ergebnissen der Analyse eine Klassifikation der Bilder vorgenommen und der Scanvorgang beeinflusst ...

Embryo Assessment

Characterizing biological stimuli by response curves

A method for calculating distances between stimulus response curves (e.g., dose response curves) allows classification of stimuli, The response curves show how the phenotype of one or more cells changes in response to varying levels of the stimulus. Each "point" on the curve represents quantitative phenotype or signature for cell(s) at a particular level of stimulus (e.g., dose of a therapeutic). The signatures are multivariate phenotypic representations of the cell (s). They include various features of the cell(s) obtained by image analysis. To facilitate the comparison of stimuli, distances between points on the response curves are calculated. First, the response curves may be aligned on a coordinate representing a separate distance, r, from a common point of negative control (e.g., the point where no stimulus is applied). Integration on r may be used to compute the distance between two response curves. The distance between response curves is used to classify stimuli.

Cellular predictive models for steatosis

Methods for generating models for predicting biological activity of a stimulus on a test population of cells are provided. In particular computer-implemented methods for producing models for classifying a hepatocyte or population of hepatocytes according to whether it exhibits steatosis and also cholestasis or phospholipidosis are presented. Also models are produced for classifying stimuli based on hepatoxicity. The methods may involve receiving a set of phenotypic features of the cells or population of cells that have been exposed to stimuli and treated with one or more markers for particular cellular components by automated image analysis. A subset of the cell populations may be identified to be used in generating a model from data associated with the subset.

Cellular predictive models for toxicities

Normalizing cell assay data for models

COMPOSITIONS AND METHODS FOR ALTERING SECOND MESSENGER SIGNALING

COMPOSITIONS AND METHODS FOR ALTERING SECOND MESSENGER SIGNALING

COMPOSITIONS AND METHODS FOR ALTERING SECOND MESSENGER SIGNALING

PROCEDURE AND DEVICE FOR THE DETECTION OF MALIGNITÄT ASSOCIATING CHANGES

FORCE OF GRAVITY ATTRACTION MACHINE TO the FLEXIBLE AUTOCCLUSTERING n of DIMENSIONAL DATA STREAMS

Methods for identifying biological material by microscopy

The present invention relates generally to the field of computer-based image recognition. More particularly, the invention relates to methods and systems for the identification, and optionally the quantitation of, discrete objects of biological origin such as cells, cytoplasmic structures, parasites, parasite ova, and the like which are typically the subject of microscopic analysis. The invention may be embodied in the form of a method for training a computer to identify a target biological material in a sample. The method may include accessing a plurality of training images, the training images being obtained by light microscopy of one or more samples containing a target biological material and optionally a non-target biological material. The training images are cropped by a human or a computer to produce cropped images, each of which shows predominantly the target biological material. A human then identifies the target biological material in each of the cropped images where identification ...

Systems and methods for using an immunostaining mask to selectively refine ISH analysis results

A computer-implemented method of processing image data representing biological units in a tissue sample includes receiving a first image of the tissue sample containing signals from an immunofluorescent (IF) morphological marker, wherein the tissue sample is stained with the IF morphological marker, and receiving a second image of the same tissue sample containing signals from a fluorescent probe, wherein the tissue sample is hybridized in situ with the fluorescent probe. The method further includes classifying each biological unit in the tissue sample into one of at least two classes based on a mean intensity of the signals from the IF morphological marker in the first image, performing a fluorescence in situ hybridization (FISH) analysis of the tissue sample in the second image to obtain results therefrom, and filtering the results of the FISH analysis to produce a subset of the results pertaining to biological units classified in one class.

Method for classifying slides using scatter plot distributions

Method and system for patient-specific modeling of blood flow

C:\Users\kll\AppData\Local\Temp12713 IDAOBAFC.DOCX-22 12/2015 Embodiments include a system for determining cardiovascular information for a patient. The system may include at least one computer system configured to receive patient-specific data regarding a geometry of the patient's heart, and create a three dimensional model representing at least a portion of the patient's heart based on the patient-specific data. The at least one computer system may be further configured to create a physics-based model relating to a blood flow characteristic of the patient's heart and determine a fractional flow reserve within the patient's heart based on the three-dimensional model and the physics-based model.

Methods for preparing and analyzing tumor tissue samples for detection and monitoring of cancers

The present invention provides methods for processing and analyzing large intact biological samples, including tumor tissue samples. The methods have a variety of uses, including for the diagnosis and monitoring of tumors and tumor metastasis.

Systems, media, methods, and apparatus for enhanced digital microscopy

Described herein are improvements in digital microscopy and telepathology. The disclosed technologies enable users to configure digital microscopes remotely.

Methods and apparatus for detecting an entity in a bodily sample

Apparatus and methods are described including a microscope system (11) configured to acquire one or more microscope images of a bodily sample, an output device (34), and at least one computer processor (28). The computer processor identifies, in the one or more images, at least one element as being a pathogen candidate, and extracts, from the one or more images, at least one candidate-informative feature associated with the pathogen candidate. The compute processor extracts, from the one or more images, at least one sample-informative feature that is indicative of contextual information related to the bodily sample. The computer processor classifies a likelihood of the bodily sample being infected with a pathogenic infection, by processing the candidate-informative feature in combination with the sample- informative feature, and generates an output upon the output device, in response thereto. Other applications are also described.

Method and system for patient-specific modeling of blood flow

Abstract A system for determining cardiovascular information for a patient, the system comprising: at least one computer system configured to: receive patient specific data regarding a geometry of an anatomical structure of the patient; create a three-dimensional model representing at least a portion of the anatomical structure of the patient based on the patient-specific data, the three-dimensional model representing at least one fluid flow inlet and at least one fluid flow outlet; create at least one boundary condition model representing fluid flow through at least one of the at least one inlet or the at least one outlet, based at least in part on modeling a condition of hyperemia; and determine first information regarding a blood flow characteristic within the anatomical structure of the patient based on the three dimensional model and the at least one boundary condition model. cCD Co co VC C C) C) f I co C, Loij 0~ u~ EtIt C3oI ...

METHOD OF CLASSIFYING PLANT EMBRYOS USING PENALIZED LOGISTIC REGRESSION

A method is disclosed for classifying plant embryos according to their quality using a penalized logistic regression (PLR) model. First, sets of image or spectral data are acquired from plant embryos of known quality, respectively. Second, each of the acquired sets of image or spectral data is associated with one of multiple class labels according to the corresponding embryo's known quality. Third, sets of metrics are calculated based on the acquired sets of image or spectral data, respectively. Fourth, a penalized logistic regression (PLR) analysis is applied to the sets of metrics and their corresponding class labels to develop a PLR-based classification model. Fifth, image or spectral data are acquired from a plant embryo of unknown quality, and metrics are calculated based therefrom. Sixth, the PLR-based classification model is applied to the metrics calculated for the plant embryo of unknown quality to classify the same.

IMAGING AND EVALUATING EMBRYOS, OOCYTES, AND STEM CELLS

Methods, compositions and kits for determining the developmental potential of one or more embryos or pluripotent cells and/or the presence of chromosomal abnormalities in one or more embryos or pluripotent cells are provided. These methods, compositions and kits find use in identifying embryos and oocytes in vitro that are most useful in treating infertility in humans.

A METHOD FOR AUTOMATED NON-INVASIVE MEASUREMENT OF SPERM MOTILITY AND MORPHOLOGY AND AUTOMATED SELECTION OF A SPERM WITH HIGH DNA INTEGRITY

A method of automated measurement of motility and morphology parameters of the same single motile sperm. Automated motility and morphology measurements of the same single sperm are performed under different microscope magnifications. The same single motile sperm is automatically positioned and kept inside microscope field of view and in focus after magnification switch. A method of automated non-invasive measurement of sperm morphology parameters under high magnification of imaging. Sperm morphology parameters including subcellular structures are automatically measured without invasive sample staining. A method of automatically selecting sperms with normal motility and morphology and DNA integrity for infertility treatment.

USING MACHINE LEARNING AND/OR NEURAL NETWORKS TO VALIDATE STEM CELLS AND THEIR DERIVATIVES FOR USE IN CELL THERAPY, DRUG DISCOVERY, AND DIAGNOSTICS

A method is provided for non-invasively predicting characteristics of one or more cells and cell derivatives. The method includes training a machine learning model using at least one of a plurality of training cell images representing a plurality of cells and data identifying characteristics for the plurality of cells. The method further includes receiving at least one test cell image representing at least one test cell being evaluated, the at least one test cell image being acquired non-invasively and based on absorbance as an absolute measure of light, and providing the at least one test cell image to the trained machine learning model. Using machine learning based on the trained machine learning model, characteristics of the at least one test cell are predicted. The method further includes generating, by the trained machine learning model, release criteria for clinical preparations of cells based on the predicted characteristics of the at least one test cell.

SCREENING DEMULSIFIERS FOR CRUDE LIVE OIL-WATER EMULSIONS

Certain implementations of the subject matter can be implemented to screen demulsifiers. A live emulsion of a live hydrocarbon sample and a water sample is flowed through a capillary viscometer. The live hydrocarbon sample includes dissolved gases retrieved from a hydrocarbon-carrying reservoir. While flowing the live emulsion through the capillary viscometer, a demulsifier sample is flowed through the capillary viscometer. The demulsifier sample is capable of causing breakdown of the live emulsion. Using the capillary viscometer, change in a viscosity of the live emulsion over time resulting from the breakdown of the live emulsion due to the demulsifier sample is measured. Multiple images of the breakdown of the live emulsion over time are captured. A strength of the live emulsion is classified based, in part, on the change in the viscosity of the live emulsion over time and on the plurality of images.

SYSTEM FOR CONTROLLING AN EMULSIFICATION PROCESS

A system (1A) and method (IB) for controlling an emulsification process including the steps of acquiring (9) images (3) such as micrographs (2) of an emulsification process at preset intervals between a start and an end of the emulsification process; detecting (10) selected droplet characteristics such as size and count using image segmentation such as a histogram-based technique (5); analysing (11) the measured droplet characteristics (6); comparing (12) the measured droplet characteristics with a desired droplet characteristic specification(S); and terminating the emulsification process when said desired droplet characteristic is achieved.

METHODS AND SYSTEMS FOR 3D STRUCTURE ESTIMATION USING NON-UNIFORM REFINEMENT

There is provided systems and methods for generating 3D structure estimation of at least one target from a set of 2D Cryo-electron microscope particle images. The method includes: receiving the set of 2D particle images of the target from a Cryo-electron microscope; splitting the set of particle images into at least a first half-set and a second half-set; iteratively performing: determining local resolution estimation and local filtering on at least a first half-map associated with the first half-set and a second half-map associated with the second half-set; aligning 2D particles from each of the half-sets using at least one region of the associated half-map; for each of the half-maps, generating an updated half-map using the aligned 2D particles from the associated half-set; and generating a resultant 3D map using all the half-maps.

SYSTEMS AND METHODS FOR SEGMENTATION AND PROCESSING OF TISSUE IMAGES AND FEATURE EXTRACTION FROM SAME FOR TREATING, DIAGNOSING, OR PREDICTING MEDICAL CONDITIONS

REDUCED FALSE POSITIVE IDENTIFICATION FOR SPECTROSCOPIC CLASSIFICATION

A device may receive information identifying results of a set of spectroscopic measurements of a training set of known samples and a validation set of known samples. The device may generate a classification model based on the information identifying the results of the set of spectroscopic measurements, wherein the classification model includes at least one class relating to a material of interest for a spectroscopic determination, and wherein the classification model includes a no-match class relating to at least one of at least one material that is not of interest or a baseline spectroscopic measurement. The device may receive information identifying a particular result of a particular spectroscopic measurement of an unknown sample. The device may determine whether the unknown sample is included in the no-match class using the classification model. The device may provide output indicating whether the unknown sample is included in the no-match class.

DOSIMETERS INCLUDING LENSLESS IMAGING SYSTEMS

MULTI-SAMPLE WHOLE SLIDE IMAGE PROCESSING IN DIGITAL PATHOLOGY VIA MULTI-RESOLUTION REGISTRATION AND MACHINE LEARNING

When reviewing digital pathology tissue specimens, multiple slides may be created from thin, sequential slices of tissue. These slices may then be prepared with various stains and digitized to generate a Whole Slide Image (WSI). Review of multiple WSIs is challenging because of the lack of homogeneity across the images. In embodiments, to facilitate review, WSIs are aligned with a multi -resolution registration algorithm, normalized for improved processing, annotated by an expert user, and divided into image patches. The image patches may be used to train a Machine Learning model to identify features useful for detection and classification of regions of interest (ROIs) in images. The trained model may be applied to other images to detect and classify ROIs in the other images, which can aid in navigating the WSIs. When the resulting ROIs are presented to the user, the user may easily navigate and provide feedback through a display layer.

MEDICAL IMAGE DETECTION

The present invention relates to detecting objects in medical images. In order to provide an improved detection of objects in medical images, a medical image detection device (10) is provided that comprises an image data input (12) and a processing unit (14). The image data input is configured to receive image data of a biological sample. The processing unit comprises a detector (16) and a classifier (18). The detector is configured to detect objects of interest in the sample by a detection in the image data of at least one predetermined object feature. The detected objects being candidate objects, wherein the candidate objects comprise true positives and possible false positives. Further, the classifier is configured to classify the possible false positives as false positives or as true positives. The classifier is a trained classifier, trained specifically to recognize the false positives of the detector.

METHOD AND APPARATUS FOR MEASURING MEAN CELL VOLUME OF RED BLOOD CELLS

METHOD AND SYSTEM FOR ANALYZING BIOLOGICAL SPECIMENS BY SPECTRAL IMAGING

The methods, devices, and systems may allow a practitioner to obtain information regarding a biological sample, including analytical data, a medical diagnosis, and/or a prognosis or predictive analysis. The method, devices, and systems may provide a grade or level of development for identified diseases. In addition, the methods, devices and systems may generate a confidence value for the predictive classifications generated, which may, for example be generated in a format to show such confidence value or other feature in a graphical representation (e.g., a color code). Further, the methods, devices and system may aid in the identification and discovery of new classes and tissue sub-types.

ADAPTIVE CLASSIFICATION FOR WHOLE SLIDE TISSUE SEGMENTATION

A method of segmenting images of biological specimens using adaptive classification to segment a biological specimen into different types of tissue regions. The segmentation is performed by, first, extracting features from the neighborhood of a grid of points (GPs) sampled on the WS image and classifying them into different tissue types. Secondly, an adaptive classification procedure is performed where some or all of the GPs in a WS image are classified using a pre-built training database, and classification confidence scores for the GPs are generated. The classified GPs with high confidence scores are utilized to generate an adaptive training database, which is then used to re-classify the low confidence GPs. The motivation of the method is that the strong variation of tissue appearance makes the classification problem more challenging, while good classification results are obtained when the training and test data origin from the same slide.

CLASSIFYING NUCLEI IN HISTOLOGY IMAGES

Disclosed is a computer device (14) and computer-implemented method of classifying cells within an image of a tissue sample comprising (1) providing the image of the tissue sample as input; (2) computing (111) nuclear feature metrics from features of nuclei within the image; (3) computing (112) contextual information metrics based on nuclei of interest with the image; (4) classifying (113) the cells within the image using a combination of the nuclear feature metrics and contextual information metrics.

METHOD FOR PROVIDING IMAGES OF A TISSUE SECTION

A method for differentiating areas in a series of digital images, the method comprising the steps of: providing a series of images comprising undetermined marker areas; evaluating every image ln for 1=n=N according to predetermined selection criteria and defining image marker areas as undetermined marker areas fulfilling the predetermined selection criteria; providing a new image lnew; and inserting new image marker areas in the new image lnew, said new image marker areas having the same shape and location as image marker areas present in image ln but not in image ln-1, and said new image marker areas being identifiable in lnew by a unique feature. Further, the application discloses a method for visualizing cell populations in tissue sections of a histological sample. Further, the application discloses a method for visualizing three-dimensional distribution of multiple cell populations in a histological sample.

SYSTEMS AND METHODS FOR USING AN IMMUNOSTAINING MASK TO SELECTIVELY REFINE ISH ANALYSIS RESULTS

A computer-implemented method of processing image data representing biological units in a tissue sample includes receiving a first image of the tissue sample containing signals from an immunofluorescent (IF) morphological marker, wherein the tissue sample is stained with the IF morphological marker, and receiving a second image of the same tissue sample containing signals from a fluorescent probe, wherein the tissue sample is hybridized in situ with the fluorescent probe. The method further includes classifying each biological unit in the tissue sample into one of at least two classes based on a mean intensity of the signals from the IF morphological marker in the first image, performing a fluorescence in situ hybridization (FISH) analysis of the tissue sample in the second image to obtain results therefrom, and filtering the results of the FISH analysis to produce a subset of the results pertaining to biological units classified in one class.

ВИЗУАЛИЗАЦИЯ И ОЦЕНКА ЭМБРИОНОВ, ЯЙЦЕКЛЕТОК И СТВОЛОВЫХ КЛЕТОК

Предложены способы, композиции и наборы для определения способности к развитию одного или нескольких эмбрионов или плюрипотентных клеток и/или наличия хромосомных нарушений в одном или нескольких эмбрионах или плюрипотентных клетках. Данные способы, композиции и наборы находят применение при идентификации in vitro эмбрионов и яйцеклеток, которые наиболее полезны для применения при лечении бесплодия у человека.

COMPUTER PROCESSES BEHIND AN ENHANCED VERSION OF AQUA

IMAGE ANALYSIS SYSTEMS AND RELATED METHODS

METHOD FOR IDENTIFYING BIOLOGICAL PARTICLES USING STACKS OF DEFOCUSED HOLOGRAPHIC IMAGES

Cell image display apparatus, cell image display method, and control system

A cell image display apparatus comprising: a parameter value obtainer for obtaining characteristic parameter values based on a plurality of cell images obtained by imaging a sample including the plurality of cells, wherein each of the characteristic parameter values respectively indicates characteristic of each of the cells; a type determiner for determining types of the cells based on the characteristic parameter values obtained by the parameter value obtainer; a display; and a display controller for controlling the display so as to display the cell images in a sequence based on the types ofthe cells obtained by the type determiner and the characteristic parameter values obtained by the parameter value obtainer. A method and a computer program product are also disclosed.

METHOD FOR DETECTING MICROORGANISMS IN A SAMPLE

METHOD FOR SAMPLE ANALYSIS WITH AUTOMATIC RECOGNITION OF NANNOFOSSILES SEDIMENTRAY

IMAGING AND EVALUATING EMBRYOS, OOCYTES, AND STEM CELLS

APPARATUS, METHOD AND PROGRAM FOR 3D DATA ANALYSIS, AND MICROPARTICLE ANALYSIS SYSTEM

In an example embodiment, may be embodied in a a data analysis apparatus comprises a control unit configured to provide data representative of a three dimensional image, the three dimensional image including at least a three dimensional coordinate space which includes at least one plane that divides the three dimensional coordinate space into at least two regions, a display unit configured to produce the three dimensional image based on the data representative of the three dimensional image, and an input unit configured to provide data representative of at least one of a movement and a position of the at least one plane. In other example embodiments, the present disclosure may be embodied in a data analysis server, a data analysis system, and/or a computer readable medium.

NORMALIZING CELL ASSAY DATA FOR MODELS

Methods for generating models for predicting biological activity of a stimulus test population of cells are provided. The models may be used to classify or predict the effect of stimuli on cells. In certain embodiments, the methods involve receiving data comprising values for dependent variables associated with stimuli as applied to cell populations; preparing a set of cell populations based on the data received; identifying a subset of the cell populations to be used in generating a model from data associated with the subset, wherein the model is provided to predict activity of a test population.

METHOD AND SYSTEM FOR WILDFIRE DETECTION USING A VISIBLE RANGE CAMERA

Wildfires are detected by controlling image scanning within the viewing range of a video camera to generate digital images that are analyzed to detect gray colored regions, and then to determine whether a detected gray colored region is smooth. Further analysis to determine movement in a gray colored smooth region uses a past image which is within a slow moving time range, as determined by a strategy for controlling the image scanning. Additional analysis connects a candidate region to a land portion of the image, and a support vector machine is applied to a covariance matrix of the candidate region to determine whether the region shows smoke from a wildfire.

METHOD OF, AND APPARATUS AND COMPUTER SOFTWARE FOR, PERFORMING IMAGE PROCESSING

A computer-implemented method of performing image processing for images of biological objects includes: storing definitions of a plurality of descriptors; receiving image data relating to an image of a reference population of biological objects; receiving image data relating to an image of a target population of biological objects; processing the reference population image data to obtain a reference set of measurements, containing data for each of the descriptors; processing the target population image data to obtain a target set of measurements, containing data for each of the descriptors; and selecting a combination of the descriptors on the basis of comparing the reference set with the target set to define a preferred combination of the descriptors for use in identifying characteristics of a further population of biological objects which are similar to those of the target population.

ANALYSIS AND CLASSIFICATION, IN PARTICULAR OF BIOLOGICAL OR BIOCHEMICAL OBJECTS, ON THE BASIS OF TIME-LAPSE IMAGES, APPLICABLE IN CYTOMETRIC TIME-LAPSE CELL ANALYSIS IN IMAGE-BASED CYTOMETRY

Among the proposals provided is a method for the analysis and classification of objects of interest, for example biological or biochemical objects, on the basis of time-lapse images, for example for use in time-lapse analysis in image-base cytometry. Images of the objects of interest, for example cells, are recorded at different moments in time and these images are subjected to a segmentation process to identify image elements as object representations or sub-object representations of objects or sub-objects of interest of objects of interest. Identified object representations or sub-object representations are then associated with one another in images of the time series and are identified as representations of the same object or sub-object or as the result of an object or sub-object. First features manifesting themselves in individual images are detected and second features manifesting themselves in a plurality of images recorded at different times are detected. The individual objects or ...

Analysis, secure access to, and transmission of array images

Systems and methods are provided the autocentering, autofocusing, acquiring, decoding, aligning, analyzing and exchanging among various parties, images, where the images are of arrays of signals associated with ligand-receptor interactions, and more particularly, ligand-receptor interactions where a multitude of receptors are associated with microparticles or microbeads. The beads are encoded to indicate the identity of the receptor attached, and therefore, an assay image and a decoding image are aligned to effect the decoding. The images or data extracted from such images can be exchanged between de-centralized assay locations and a centralized location where the data are analyzed to indicate assay results. Access to data can be restricted to authorized parties in possession of certain coding information, so as to preserve confidentiality.

LEARNING ANNOTATION OF OBJECTS IN IMAGE

A system and method are provided which use a machine learning algorithm to obtain a learned annotation of objects in one or more scales of a multiscale image. A viewing window (300) is provided for viewing the multiscale image. The viewing window is configurable on the basis of a magnification factor, which selects one of the plurality of scales for viewing, and a spatial offset parameter. A user may provide a manual annotation of an object in the viewing window, which is then used as training feedback in the learning of the machine learning algorithm. To enable the user to more effectively provide the manual annotation, the magnification factor and the spatial offset parameter for the viewing window may be automatically determined, namely by the system and method determining where in the multiscale image the manual annotation of the object would have sufficient influence on the learned annotation provided by the machine learning algorithm. The determined influence may be shown in the form ...

OBJECT DETECTION APPARATUS AND METHOD THEREFOR, AND IMAGE RECOGNITION APPARATUS AND METHOD THEREFOR

An object detection apparatus includes an extraction unit configured to extract a plurality of partial areas from an acquired image, a distance acquisition unit configured to acquire a distance from a viewpoint for each pixel in the extracted partial area, an identification unit configured to identify whether the partial area includes a predetermined object, a determination unit configured to determine, among the partial areas identified to include the predetermined object by the identification unit, whether to integrate identification results of a plurality of partial areas that overlap each other based on the distances of the pixels in the overlapping partial area, and an integration unit configured to integrate the identification results of the plurality of partial areas determined to be integrated to detect a detection target object from the integrated identification result of the plurality of partial areas.

Early detection of hepatocellular carcinoma in high risk populations using MALDI-TOF Mass Spectrometry

Hepatocellular carcinoma (HCC) is detected in a patient with liver disease. Mass spectrometry data from a blood-based sample from the patient is compared to a reference set of mass-spectrometry data from a multitude of other patients with liver disease, including patients with and without HCC, in a general purpose computer configured as a classifier. The classifier generates a class label, such as HCC or No HCC, for the test sample. A laboratory system for early detection of HCC in patients with liver disease is also disclosed. Alternative testing strategies using AFP measurement and a reference set for classification in the form of class-labeled mass spectral data from blood-based samples of lung cancer patients are also described, including multi-stage testing.

Image acquisition system and image acquisition method

In an image acquisition system, a distortion distribution is easily measured in a wide range. A standard image of magnetic domain of a sample serving as a standard is acquired by radiation of light using a standard external magnetic field which serves as a standard, a plurality of magnetic domain images are acquired in a state where an external magnetic field is applied while being changed, a plurality of subtraction images obtained by subtracting the standard image of magnetic domain from each of the plurality of magnetic domain images are acquired, a magnetization reversal area in which a magnetic domain is reversed is extracted from each of the plurality of subtraction images, and a composite image having a plurality of magnetization reversal areas is acquired by compositing the plurality of subtraction images each having the magnetization reversal area.

IMAGE PROCESSING APPARATUS AND IMAGE PROCESSING METHOD

An image processing apparatus includes: a basic shape matching section that extracts, as a structure region, a predetermined structural object included in an image obtained by picking up an image of a mucosal surface of a living body, and matches each of regions resulting from the structure region being divided, the regions each including at least one pixel, with a first region having a first basic shape or a second region having a second basic shape; a feature value calculating section that sequentially sets regions of interest from among the regions matched by the basic shape matching section, and calculates counts of the first regions and the second regions adjacent to each of the regions of interest; and a classification section that classifies the structure region based on a result of the calculation by the feature value calculating section. 1. An image processing apparatus comprising:a basic shape matching section that extracts, as a structure region, a predetermined structural object in an image obtained by picking up an image of a mucosal surface of a living body, the image including at least one pixel, and matches each of regions resulting from the structure region being divided, the regions each including at least one pixel, with a first region having a first basic shape or a second region having a second basic shape that is different from the first basic shape;a feature value calculating section that sequentially sets regions of interest from among the regions matched by the basic shape matching section, and calculates counts of the first regions and the second regions adjacent to each of the sequentially set regions of interest; anda classification section that classifies the structure region based on a result of the calculation by the feature value calculating section.2. The image processing apparatus according to claim 1 , wherein the first basic shape is any one of four shapes that are a circle claim 1 , a straight line claim 1 , a curve and a ...

IMAGE PROCESSING AND PATIENT-SPECIFIC MODELING OF BLOOD FLOW

Embodiments include a system for determining cardiovascular information for a patient. The system may include at least one computer system configured to receive patient-specific data regarding a geometry of the patient's heart, and create a three-dimensional model representing at least a portion of the patient's heart based on the patient-specific data. The at least one computer system may be further configured to create a physics-based model relating to a blood flow characteristic of the patient's heart and determine a fractional flow reserve within the patient's heart based on the three-dimensional model and the physics-based model.

ADAPTIVE CLASSIFICATION FOR WHOLE SLIDE TISSUE SEGMENTATION

A method of segmenting images of biological specimens using adaptive classification to segment a biological specimen into different types of tissue regions. The segmentation is performed by, first, extracting features from the neighborhood of a grid of points (GPs) sampled on the whole-slide (WS) image and classifying them into different tissue types. Secondly, an adaptive classification procedure is performed where some or all of the GPs in a WS image are classified using a pre-built training database, and classification confidence scores for the GPs are generated. The classified GPs with high confidence scores are utilized to generate an adaptive training database, which is then used to re-classify the low confidence GPs. The motivation of the method is that the strong variation of tissue appearance makes the classification problem more challenging, while good classification results are obtained when the training and test data origin from the same slide.

CELL IMAGE EVALUATION DEVICE, METHOD, AND PROGRAM

There is provided a cell image evaluation device, method, and program to appropriately evaluate the state of a stem cell colony according to different changes in form of respective local regions of the cell colony. There are included a low magnification image acquisition unit 20 that acquires a cell image by imaging cells; a cell evaluation unit 23 that evaluates the cell image; and a local region information acquisition unit 21 that acquires the specific information of a local region in a colony region of the cells in the cell image. The cell evaluation unit 23 determines, for each local region in the colony region, an evaluation method for a cell image in the local region based on the specific information of the local region, and evaluates the cell image of the local region using the determined evaluation method.

RECONSTRUCTION METHOD OF BIOLOGICAL TISSUE IMAGE, APPARATUS THEREFOR, AND IMAGE DISPLAY APPARATUS USING THE BIOLOGICAL TISSUE IMAGE

The present invention provides a method for classifying biological tissues with high precision compared to a conventional method. When measuring a spectrum which has a two-dimensional distribution that is correlated with a slice of a biological tissue, and acquiring a biological tissue image from the two-dimensional measured spectrum, the method includes dividing an image region into a plurality of small blocks, and then reconstructing the biological tissue image by using the measured spectrum and a classifier corresponding to each of the regions.

Dosimeters including lensless imaging systems

Among other things, a method comprises imaging a sample displaced between a sensor surface and a surface of a microscopy sample chamber to produce an image of at least a part of the sample. The image is produced using lensless optical microscopy, and the sample contains at least blood from a subject. The method also comprises automatically differentiating cells of different types in the image, generating a count of one or more cell types based on the automatic differentiation, and deriving a radiation dose the subject has absorbed based on the count.

SYSTEMS AND METHODS FOR IMAGE PREPROCESSING

A method and apparatus of a device that classifies an image is described. In an exemplary embodiment, the device segments the image into a region of interest that includes information useful for classification and a background region by applying a first convolutional neural network. In addition, the device tiles the region of interest into a set of tiles. For each tile, the device extracts a feature vector of that tile by applying a second convolutional neural network, where the features of the feature vectors represent local descriptors of the tile. Furthermore, the device processes the extracted feature vectors of the set of tiles to classify the image.

Systems and methods for segmentation and processing of tissue images and feature extraction from same for treating, diagnosing, or predicting medical conditions

Apparatus, methods, and computer-readable media are provided for segmentation, processing (e.g., preprocessing and/or postprocessing), and/or feature extraction from tissue images such as, for example, images of nuclei and/or cytoplasm. Tissue images processed by various embodiments described herein may be generated by Hematoxylin and Eosin (H&E) staining, immunofluorescence (IF) detection, immunohistochemistry (IHC), similar and/or related staining processes, and/or other processes. Predictive features described herein may be provided for use in, for example, one or more predictive models for treating, diagnosing, and/or predicting the occurrence (e.g., recurrence) of one or more medical conditions such as, for example, cancer or other types of disease.

Imaging and evaluating embryos, oocytes, and stem cells

Methods, compositions and kits for determining the developmental potential of one or more embryos or pluripotent cells and/or the presence of chromosomal abnormalities in one or more embryos or pluripotent cells are provided. These methods, compositions and kits find use in identifying embryos and oocytes in vitro that are most useful in treating infertility in humans.

Quantitative structural assay of a nerve graft

Techniques are described for determining the quality of a nerve graft by assessing quantitative structural characteristics of the nerve graft. Aspects of the techniques include obtaining an image identifying laminin-containing tissue in the nerve graft; creating a transformed image using a transformation function of an image processing application on the image; using an analysis function of the image processing application, analyzing the transformed image to identify one or more structures in accordance with one or more recognition criteria; and determining one or more structural characteristics of the nerve graft derived from a measurement of the one or more structures.

Assessing risk of breast cancer recurrence

The subject disclosure presents systems and computer-implemented methods for assessing a risk of cancer recurrence in a patient based on a holistic integration of large amounts of prognostic information for said patient into a single comparative prognostic dataset. A risk classification system may be trained using the large amounts of information from a cohort of training slides from several patients, along with survival data for said patients. For example, a machine-learning-based binary classifier in the risk classification system may be trained using a set of granular image features computed from a plurality of slides corresponding to several cancer patients whose survival information is known and input into the system. The trained classifier may be used to classify image features from one or more test patients into a low-risk or high-risk group.

АВТОМАТИЗИРОВАННАЯ СИСТЕМА И СПОСОБ КЛАССИФИКАЦИИ ЦИТОЛОГИЧЕСКИХ ОБРАЗЦОВ, ОСНОВАННЫЙ НА НЕЙРОННЫХ СЕТЯХ

Изобретение относится к области классификации клеток, в частности, к использованию нейронных сетей и/или нейрокомпьютеров для повышения скорости и точности классификации клеток. В способе классификации цитологических образцов и в реализующей данный способ системе нейронная сеть используется для выполнения функций классификации. В систему входят автоматический микроскоп, устройство вывода информации, видеокамера, цифровой преобразователь изображения, первичный статистический классификатор, выполненный в виде аморитмического обрабатывающего устройства для детектирования объектов, вероятно являющихся предзлокачественными или злокачественными клетками, вторичный классификатор, выполненный в виде нейронного компьютера. 2 с. и. 8 з.п. ф-лы, 7 ил.

Verfahren und Mikroskopiersystem zum Scannen einer Probe

Ein Verfahren zum Scannen einer Probe mittels eines elektrisch und/oder elektronisch steuerbaren Mikroskops (1), wobei eine Vielzahl von Bildern, insbesondere digitalen Bildern, an unterschiedlichen Stellen der Probe und/oder zu unterschiedlichen Zeiten erzeugt werden und wobei das Mikroskop (1) wäh(2) gesteuert wird, ist im Hinblick auf einen möglichst schnellen und präzisen Scanvorgang mit möglichst geringem Datenaufkommen auch bei einer hohen Anzahl von Bildern dadurch gekennzeichnet, dass eine Beobachtung und/oder Analyse der erzeugten Bilder bei mindestens einem über ein Netzwerk (4) verbundenen weiteren Rechner (7) durchgeführt wird und dass, basierend auf deren Ergebnissen, eine Klassifikation der Bilder vorgenommen und/oder der Scanvorgang beeinflusst wird. Ein entsprechendes Mikroskopiersystem ist angegeben.

Mustererkennungssystem

System zum patientenspezifischen Modellieren von Blutfluss

System zum Ermöglichen der Bewertung einer Koronararterie eines Patienten basierend auf einem berechneten fraktionellen Flussreservewert, wobei das System Folgendes umfasst: wenigstens ein Computersystem, das konfiguriert ist, um: auf einem digitalen Anzeigegerät ein dreidimensionales Modell wenigstens eines Teils mehrerer Koronararterien, die von einer Aorta des Patienten abzweigen, anzuzeigen; auf dem Anzeigegerät eine erste fraktionelle Flussreserve anzuzeigen, die vom Computersystem für wenigstens eine der Koronararterien in dem dreidimensionalen Modell berechnet wurde; eine Eingabe hinsichtlich eines Behandlungsplans für die wenigstens eine der Koronararterien zu empfangen, wobei der Behandlungsplan ein Erweitern eines durch die wenigstens eine der Koronararterien definierten Lumens beinhaltet; und eine zweite fraktionelle Flussreserve für die wenigstens eine der Koronararterien zu berechnen und auf dem Anzeigegerät anzuzeigen, wobei das Berechnen der zweiten fraktionellen Flussreserve ...

System und Verfahren für automatische Zellenklassifizierung, basierend auf ein Neuronalnetzwerk

Method and apparatus for determining temporal behaviour of an object

Device, microscope device, method, and program

This device has: an image processing unit that, in an image having been captured, calculates color information about one or more cells; and a determination unit that, on the basis of the color information calculated by the image processing unit, determines a culture status of the cells.

Gerät und Verfahren zur Messung einer Suspension und zur Steuerung eines Prozesses einer Suspension

Ein Gerät zum Messen einer Suspension umfasst eine Bildaufnahmevorrichtung (100) zum Aufnehmen von zumindest einem Messbild (200) der Suspension (102), wobei das zumindest eine Messbild zumindest einen Feststoffpartikel (202) präsentiert; eine Informationsverarbeitungseinheit (112), die das zumindest einen Messwert (200) empfängt, und einen Bindungszustand der Festplatte (202) der Suspension (102) untereinander auf der Grundlage der auf das zumindest eine Messbild (200) angewandten Mustererkennung bestimmt. Die Informationsverarbeitungseinheit (112) bestimmt auf der Grundlage des Bindungszustands der Festplatte (202) der Suspension (102) Suspensionsdaten, die mit zumindest einem der folgenden in Verbindung stehen: zumindest einer Prozesssteuerchemikalie, zumindest einer Fasereigenschaft, zumindest einer Feinstoffeigenschaft, einer Beziehung zwischen Feststoffpartikeln verschiedener Größen, einer Formierung und einem Wassergehalt.

AUTOMATED SEGMENTING OF PICTURE STRUCTURES

IN-VITRO-DIAGNOSTIKVERFAHREN FOR HUMAN ONES OR ANIMAL FABRIC AFFECTING DISEASES

"In vitro" diagnostic method for diseases affecting human or animal tissues

Computer processes behind an enhanced version of aqua

The invention relates, in part, to systems and methods for scoring a sample containing tumor tissue from a cancer patient. The score obtained from these methods can be indicative of a likelihood that a patient may respond positively to immunotherapy. The invention also relates, in part, to methods of deriving a value for % biomarker positivity (PBP) for all cells or optionally, one or more subsets thereof, present in a field of view of a tissue sample from a cancer patient. The values for PBP can be indicative of a patient's response to immunotherapy.

Method and system for patient-specific modeling of blood flow

C:\Users\kIl\AppData\Locl\Temp12713_1 DAOBAFC.DOCX-22 12 2015 Embodiments include a system for determining cardiovascular information for a patient. The system may include at least one computer system configured to receive patient-specific data regarding a geometry of the patient's heart, and create a three dimensional model representing at least a portion of the patient's heart based on the patient-specific data. The at least one computer system may be further configured to create a physics-based model relating to a blood flow characteristic of the patient's heart and determine a fractional flow reserve within the patient's heart based on the three-dimensional model and the physics-based model.

Method and system for patient-specific modeling of blood flow

C:\Users\kll\AppData\Local\Temp12713 IDAOBAFC.DOCX-22 12/2015 Embodiments include a system for determining cardiovascular information for a patient. The system may include at least one computer system configured to receive patient-specific data regarding a geometry of the patient's heart, and create a three dimensional model representing at least a portion of the patient's heart based on the patient-specific data. The at least one computer system may be further configured to create a physics-based model relating to a blood flow characteristic of the patient's heart and determine a fractional flow reserve within the patient's heart based on the three-dimensional model and the physics-based model.

Bone marrow cell marking method and system

Disclosed in the present invention are a bone marrow cell marking method and system. The method comprises: processing and marking specimen images by using an image processing algorithm to obtain contour cell images; inputting the contour cell images into a preset classification model to obtain classified cell images and corresponding classified cell information; classifying obtained color information according to preset classes to obtain classified color information; and finally, extracting, according to the classified cell information, corresponding name information and classified color information to mark the classified cell images in combined fashion, and displaying a visual diagram after the combined marking. According to the present invention, classified cell images obtained by processing are marked in combined fashion according to corresponding name information and classified color information. That is, during marking of name information of cells, the classified color information ...

Method for quantification of purity of sub-visible particle samples

The method is for quantification of purity of sub-visible particle samples. A sample to be analyzed is place in an electron microscope to obtain an electron microscopy image (100) of the sample. The sample contains objects (114). The objects (114) that have sizes being different from a size range of primary particles (120) and sizes being within the size range of primary particles (120) are enhanced. The objects (114) are detected as being primary particles (120) or debris (106). The detected primary particles (120) are excluded from the objects (114) so that the objects (114) contain debris (106) but no primary particles (120). A first total area (T1) of the detected debris (106) is measured. A second total area (T2) of the detected primary particles (120) is measured.

Immune cell organoid co-cultures

The present invention provides co-cultures of organoids and immune cells, and methods of using these to identify agents for treating diseases.

Method for providing images of a tissue section

A method for differentiating areas in a series of digital images, the method comprising the steps of: providing a series of images comprising undetermined marker areas; evaluating every image l ...

Method and software for analysing microbial growth

A method for analysing microbial growth on a solid culture medium, the method including obtaining image data of the solid culture medium and any microbial growth, generating an associated feature vector of values obtained by applying one or more filters to the image data, using a classifier to classify each pixel in a plurality of pixels in the image data based on the associated feature vector, analysing results of pixel classifications of each said pixel to derive a microbiological assessment of the solid culture medium and any microbial growth, and outputting the microbiological assessment.

SYSTEMS AND METHODS FOR IMAGE PATTERN RECOGNITION

Systems and methods for image pattern recognition comprise digital image capture and encoding using vector quantization ("VQ") of the image. A vocabulary of vectors is built by segmenting images into kernels and creating vectors corresponding to each kernel. Images are encoded by creating a vector index file having indices that point to the vectors stored in the vocabulary. The vector index file can be used to reconstruct an image by looking up vectors stored in the vocabulary. Pattern recognition of candidate regions of images can be accomplished by correlating image vectors to a pre-trained vocabulary of vector sets comprising vectors that correlate with particular image characteristics. In virtual microscopy, the systems and methods are suitable for rare-event finding, such as detection of micrometastasis clusters, tissue identification, such as locating regions of analysis for immunohistochemical assays, and rapid screening of tissue samples, such as histology sections arranged as tissue ...

SYSTEMS AND METHODS FOR FACILITATING CLONE SELECTION

A method for facilitating clone selection includes generating time-sequence images of a well containing a medium, including a first image and a later, second image. The method also includes detecting, by one or more processors analyzing the first image, one or more candidate objects depicted in the first image, and, for each of the candidate objects, determining whether the object is a single cell by analyzing an image of the object using a convolutional neural network. The method further includes detecting, by analyzing the second image with the processor(s), a cell colony depicted in the second image, and determining, by the processor(s), whether the colony was formed from only one cell based at least on whether each candidate object was determined to be a single cell. The method further includes generating, by the processor(s), output data indicating whether the colony was formed from only one cell.

SYSTEMS AND METHODS FOR PLATFORM AGNOSTIC WHOLE BODY IMAGE SEGMENTATION

Presented herein are systems and methods that provide for automated analysis of three-dimensional (3D) medical images of a subject in order to automatically identify specific 3D volumes within the 3D images that correspond to specific anatomical regions (e.g., organs and/or tissue). Notably, the image analysis approaches described herein are not limited to a single particular organ or portion of the body. Instead, they are robust and widely applicable, providing for consistent, efficient, and accurate detection of anatomical regions, including soft tissue organs, in the entire body. In certain embodiments, the accurate identification of one or more such volumes is used to automatically determine quantitative metrics that represent uptake of radiopharmaceuticals in particular organs and/or tissue regions. These uptake metrics can be used to assess disease state in a subject, determine a prognosis for a subject, and/or determine efficacy of a treatment modality.

COMPUTATIONAL SYSTEMS PATHOLOGY SPATIAL ANALYSIS PLATFORM FOR IN SITU OR IN VITRO MULTI-PARAMETER CELLULAR AND SUBCELLULAR IMAGING DATA

A computational systems pathology spatial analysis platform includes: (i) a spatial heterogeneity quantification component configured for generating a global quantification of spatial heterogeneity among cells of varying phenotypes in multi-parameter cellular and subcellular imaging data; (ii) a microdomain identification component configured for identifying a plurality of microdomains for tissue samples based on the global quantification, each microdomain being associated with a a tissue sample; and (iii) a weighted graph component configured for constructing a weighted graph for the multi-parameter cellular and subcellular imaging data, the weighted graph having a plurality of nodes and a plurality of edges each being located between a pair of the nodes, wherein in the weighted graph each node is a particular one of the microdomains and the edge between each pair of microdomains in the weighted graph is indicative of a degree of similarity between the pair of the microdomains.

DISPLAY CONTROL DEVICE, DISPLAY CONTROL METHOD, AND DISPLAY CONTROL PROGRAM

Provided are a display control device and a display control program with which it is possible to present the performance of class determination by an artificial neural network in a format created for humans to understand easily. The display control device comprises a certainty factor acquisition unit for acquiring a certainty factor calculated by a certainty factor calculation unit that calculates the certainty factor of the result of input image class determination, and a display control unit for causing the distribution per input image of a certainty factor acquired by the certainty factor acquisition unit to be displayed such that at least one of display axes of a graph is used as a certainty factor axis that indicates the certainty factor.

DETECTING CELLS OF INTEREST IN LARGE IMAGE DATASETS USING ARTIFICIAL INTELLIGENCE

A method for selecting a final model for detecting cells of interest in image datasets includes dividing a curated image dataset into a training set, a validation set, and a testing set where each image in the curated image dataset has been labeled as positive or negative for a cell of interest. The method trains each model of an ensemble of neural networks using the training and validation sets. Next, each model of the ensemble is tested using the testing set and the predictions of the ensemble are combined. The combined prediction is compared to the label and the method determines whether the combined prediction satisfies a pre-determined level of detection (LOD). If so, the method outputs the ensemble as a final ensemble. If not, the method modifies a hyperparameter of at least one of the models of the ensemble until the combined prediction satisfies the pre-determined LOD.

A METHOD FOR DISTINGUISHING BETWEEN MORE THAN ONE FLUORESCENT SPECIES PRESENT IN A SAMPLE

Methods and systems are provided for distinguishing between more than one fluorescent species present in a sample in fluorescence microscopy. The method involves illuminating the sample with at least one light source. More than two images of the illuminated sample are recorded over a period of time, each image comprising a plurality of pixels, wherein each pixel corresponds to a location in the sample and records a degree of fluorescence at the location in the sample at a particular point in time. A photostability characteristic of the degree of fluorescence at each pixel over the period of time over which the more than two images were recorded is determined and used to distinguish between the more than one fluorescent species present in the sample.

THREE-DIMENSIONAL CELL AND TISSUE IMAGE ANALYSIS FOR CELLULAR AND SUB-CELLULAR MORPHOLOGICAL MODELING AND CLASSIFICATION

The ability to automate the processes of specimen collection, image acquisition, data pre-processing, computation of derived biomarkers, modeling, classification and analysis can significantly impact clinical decision-making and fundamental investigation of cell deformation. This disclosure combine 3D cell nuclear shape modeling by robust smooth surface reconstruction and extraction of shape morphometry measure into a highly parallel pipeline workflow protocol for end-to-end morphological analysis of thousands of nuclei and nucleoli in 3D. This approach allows efficient and informative evaluation of cell shapes in the imaging data and represents a reproducible technique that can be validated, modified, and repurposed by the biomedical community. This facilitates result reproducibility, collaborative method validation, and broad knowledge dissemination.

APPLICATION DEVELOPMENT ENVIRONMENT FOR BIOLOGICAL SAMPLE ASSESSMENT PROCESSING

A system and method for developing applications (Apps) for automated assessment and analysis of processed biological samples. Such samples are obtained, combined with nutrient media and incubated. The incubated samples are imaged and the image information is classified according to predetermined criteria. The classified image information is then evaluated according to Apps derived from classified historical image information in a data base. The classified historical image information is compared with the classified image information to provide guidance on further processing of the biological sample through Apps tailored to process provide sample process guidance tailored to the classifications assigned to the image information.

Image analysis

A first aspect of the invention relates to an apparatus 100 for genotoxicological screening. The apparatus 100 comprises a processor 114 for analysing images. The processor 114 is configured to provide an identifier module 115 for identifying target cells in an image and a dynamically modifiable classifier module 116 for classifying the identified cells in accordance with one or more phenotype, such as micronuclei, for example. The processor 114 is also configured to provide a scoring module 117 for assigning respective confidence measurements to the classified cells. Various aspects and embodiments of the invention may be used, for example, to provide for improved reliability and accuracy when performing automated high-throughput screening (HTS) drug assays.

MOLECULAR BLOCK-MATCHING METHOD FOR GEL IMAGE ANALYSIS

A method for analysis of 2-D gel images obtained using electrophoresis. More particularly, a molecular block-matching method for establishing the correspondence between protein spots in a diagnostic-test image and protein spots in a reference image. Individual protein spot matching is performed, thereby removing the need for alignment of the entire reference and test images and permitting automatic labeling of individual protein spots. The method for analysis of 2-D gel images is fully automated, thus making it ideally suited for protein information retrieval systems. 1. A molecular-block-matching method for gel image analysis implemented in a medical system with one or more processors , said method comprising:(a) centering a first block on a first gel image at the location of a first protein spot;(b) centering a second block on a second gel image at an initial location corresponding to the location of said first protein spot on said first gel image;(c) shifting said second block by increments up to a maximum displacement from said initial location;(d) comparing the images within said first and second blocks prior to each incremental shift of said second block, in order to determine the closest matching second block to said first block; and(e) assigning the center location of said closest matching second block to a second protein spot on said second gel image.2. The method of claim 1 , wherein comparing said images uses Pearson's correlation as a block-matching criterion.3. The method of claim 2 , wherein shifting said second block is performed in single pixel increments in a widening spiral around said initial location.4. The method of claim 3 , wherein the location of said first protein spot is represented by the center coordinates of said first protein spot.5. A method for gel image analysis implemented in a medical system with one or more processors claim 3 , said method comprising:(a) receiving an indication of the location of a first protein spot on a first ...

SYSTEM AND METHOD FOR AUTOMATED BIOLOGICAL CELL ASSAY DATA ANALYSIS

In one aspect, the present invention relates to a system for automated cellular assay data analysis. The system comprises a virtual assay module (VAM) operable to generate simulated images of cell responses to one or more stimuli. The system also comprises a comparator module operable to compare the actual and simulated images, and an analysis module operable to quantify the differences between phenotypes represented by the actual and simulated images. Various aspects and embodiments of present invention may account for stochastic variations in the response of single cells, to provide additional useful information relating to, for example, toxological effects and/or for use as part of a feedback mechanism to refine dynamically a virtual assay model such that it is not limited by way of there being only inadequate static fitting expressions available. 1100100. A system () for automated cellular assay data analysis , the system () comprising:{'b': '115', 'a virtual assay module (VAM) () operable to generate simulated images of cell responses to one or more stimuli;'}{'b': '116', 'a comparator module () operable to compare the actual and simulated images; and'}{'b': '117', 'an analysis module () operable to quantify the differences between phenotypes represented by the actual and simulated images.'}2100117115. The system () of claim 1 , wherein the analysis module () is further operable to provide feedback to adjust the VAM () in accordance with the quantified differences such that the phenotypes of the actual and simulated images converge.3100117. The system () of claim 1 , wherein the analysis module () is further operable to quantify temporally the differences between the phenotypes represented by the actual and simulated images.4100117. The system () of claim 1 , wherein the analysis module () is further operable to apply stochastical fitting to quantify one or more response properties of a single cell's response to said one or more stimuli.5100117. The system () ...

COMPACT DARK FIELD LIGHT SOURCE AND DARK FIELD IMAGE ANALYSIS AT LOW MAGNIFICATION

The invention relates to image analysis of dark field images obtained at low magnification below 10:1. Image analysis of dark field images obtained at low magnification can be combined with analyses of images obtained in respect of the same section of a sample and same magnification but with other techniques such as fluorescent microscopy. The system and method can be used e.g. for particle counting, particle size measurement, particle size distribution, morphology measurement, where the particles can be cells and/or cell parts. The invention also relates to a compact dark field light source unit, a system or apparatus including a microscope which by itself is compact and comprises the mentioned dark field light source unit. 176.-. (canceled)77. An apparatus for analysing a sample comprising particles and wherein said analysis is performed at low magnification , said apparatus comprisesAt least one dark field light source,At least one other light source,An image sensor for obtaining images of a sample, andMagnifying means capable of projecting an image of the particles on the image sensor at a magnification below 10:1,wherein light in said at least one dark field light source and said at least one other light source is obtained from LED or a laser diode.78. The apparatus according to claim 77 , wherein said at least one other light source is used in combination with a spectral filter resulting in a fluorescence image.79. The apparatus according to claim 77 , wherein said apparatus comprises or is connected to a system capable of performing image analysis claim 77 , wherein said image analysis is performed in respect ofat least two images obtained by dark field analysis orat least one image obtained by dark field analysis and at least one image obtained by fluorescence analysis.80. The apparatus according to claim 79 , wherein said apparatus is operative so that at least one image obtained by dark field analysis and at least one image obtained by fluorescence ...

System For Detecting Infectious Agents Using Computer-Controlled Automated Image Analysis

A method for providing quantitative information regarding the extent of infection of host cells by an infectious agent. A microscope image of a specimen of a bodily fluid is analyzed using image processing techniques to quantify the percentage of the area of the specimen that is infected. 1. A computer software product , comprising a computer-readable storage medium having fixed therein a sequence of instructions which , when executed by a computer , direct the performance of a method which comprises:a) acquiring a microscope image of an optical field of a substrate having fixed thereon a monolayer of animal cells which either produce or are treated to produce a first signal specific to an animal cell of interest and treated to produce a second signal specific to an infectious agent of interest, if present, and transferring the image to an ROB image;b) transferring the Red component of the RGB image to a new monochrome grey image;c) transforming the grey level image to a binary image using a cut off point set to a value indicative of the expected size of the animal cells of interest;d) operating on the binary image to remove noise and fill holes;e) measuring size of the image of step d), selecting and recording areas representative of the animal cells of interest;f) transferring the Red component of the original ROB image to a second binary image using an expected value indicative of the infectious agent of interest;g) transferring the Green component of the original RGB image to grey level;h) forming a new grey level image using all pixels having a value equal to a set value M and any grey level value in the Green component in the original RGB image equal to a set value N;i) transforming the new grey level images to a third binary image;j) operating on the third binary image to remove noise and fill holes;k) recording the area of the third binary image after step j); andl) determining whether or not, each identified animal cell is occupied by an infectious agent of ...

IMAGE PROCESSING DEVICE, IMAGING DEVICE, MICROSCOPE DEVICE, IMAGE PROCESSING METHOD, AND IMAGE PROCESSING PROGRAM

The image processing device includes a template preparation unit for preparing, from a template included in pixels of M rows and M columns (M is an integer not less than 3) corresponding to a molecular model, a partial template corresponding to a shape for which a shape of the molecular model is divided, an evaluation value calculation unit for evaluating, in the optical image, by use of the partial template, matching between the optical image and the partial template to calculate an evaluation value for every plurality of the attention pixels, and a molecular location identification unit for identifying the molecular location in the optical image based on the evaluation value. 1. An image processing device for identifying a molecular location based on an image picture of a sample obtained by an imaging element , comprising:template preparing means for preparing, from a template included in pixels of M rows and M columns (M is an integer not less than 3) corresponding to a molecular model, a partial template corresponding to a shape for which a shape of the molecular model is divided into a predetermined ratio;evaluation value calculating means for evaluating, in the image picture, by use of the partial template corresponding to shapes rotated by a predetermined angle each about a selected attention pixel, matching between the image picture and the partial template to calculate an evaluation value for every plurality of the attention pixels; andmolecular location identifying means for identifying the molecular location in the image picture based on the evaluation value calculated for a plurality of the attention pixels in the image picture.2. The image processing device according to claim 1 , whereinthe template has pixels corresponding to a circular image, andthe partial template has pixels corresponding to a fan shape having a central angle for which a central angle of the circular image is multiplied by α (α is a number less than 1 and more than 0).3. The image ...

Interactive and automated tissue image analysis with global training database and variable-abstraction processing in cytological specimen classification and laser capture microdissection applications

A system and method for performing tissue image analysis and region of interest identification for further processing applications such as laser capture microdissection is provided. The invention provides three-stage processing with flexible state transition that allows image recognition to be performed at an appropriate level of abstraction. The three stages include processing at one or more than one of the pixel, subimage and object levels of processing. Also, the invention provides both an interactive mode and a high-throughput batch mode which employs training files generated automatically.

METHOD FOR IMAGING AND DIFFERENTIAL ANALYSIS OF CELLS

Provided are methods for determining and analyzing photometric and morphometric features of small objects, such as cells to, for example, identify different cell states. In particularly, methods are provided for identifying apoptotic cells, and for distinguishing between cells undergoing apoptosis versus necrosis. 1. A method for distinguishing late apoptotic cells from necrotic cells , comprising:based on images of a population of cells, identifying a group of cells that are either of a necrotic type or a late apoptotic type; andgrouping cells in the population into a group of late apoptotic cells and a group of necrotic cells based on a brightfield image area of the cells and a darkfield peak intensity area of the cells, as determined from the images of the cells, where cells are identified as being in the group of late apoptotic cells if the cells have both a low brightfield area and a high darkfield peak intensity, and as being in the group of necrotic cells if the cells have both a high brightfield area and a low darkfield peak intensity.2. The method of claim 1 , wherein there is relative motion between the cells and a detector used to produce images of the population of cells.3. The method of claim 1 , wherein the cells in the group of late apoptotic cells are characterized as having a high texture in the brightfield image area claim 1 , while the cells in the group of necrotic cells are characterized as having a low texture in the brightfield image area.4. The method of claim 1 , further comprising determining a spatial frequency in the darkfield peak intensity area for the population of cells claim 1 , to further assist in grouping the cells in the population of cells into either the group of late apoptotic cells or the group of necrotic cells.5. The method of claim 4 , further comprising using the spatial frequency darkfield peak intensity area is an indicator of internal cell complexity or cell granularity of the cells in the population of cells claim 4 , ...

Systems and methods for segmentation and processing of tissue images and feature extraction from same for treating, diagnosing, or predicting medical conditions

Apparatus, methods, and computer-readable media are provided for segmentation, processing (e.g., preprocessing and/or postprocessing), and/or feature extraction from tissue images such as, for example, images of nuclei and/or cytoplasm. Tissue images processed by various embodiments described herein may be generated by Hematoxylin and Eosin (H&E) staining, immunofluorescence (IF) detection, immunohistochemistry (IHC), similar and/or related staining processes, and/or other processes. Predictive features described herein may be provided for use in, for example, one or more predictive models for treating, diagnosing, and/or predicting the occurrence (e.g., recurrence) of one or more medical conditions such as, for example, cancer or other types of disease.

Image classification apparatus and recording medium having program recorded therein

An image-classification apparatus includes: a first feature extraction unit to acquire a feature value of each of block images obtained by segmenting an input image; an area-segmentation unit to assign each of the block images to any one of K areas based on the feature value; a second feature extraction unit to acquire, based on an area-segmentation result, a feature vector whose elements including, the number of adjacent spots, each including two block images adjacent to each other in the input image, for each combination of the areas whereto the two block images are assigned; or a ratio of the number of block images assigned to each of the K areas to all the number of block images adjacent to a block image assigned to each of the K areas; and a classification unit to classify to which of a plurality of categories the input image belong.

INFORMATION PROCESSING SYSTEM, INFORMATION PROCESSING METHOD, INFORMATION PROCESSING APPARATUS, CONTROL METHOD THEREFOR, AND STORAGE MEDIUM STORING CONTROL PROGRAM THEREFOR

This invention relates to an information processing apparatus which assists diagnosis based on a tissue sample image obtained by staining and capturing a tissue. The information processing apparatus receives and analyzes lower magnification image data among a plurality of image data obtained at different magnifications for an area image selected in the tissue sample image. Based on the analysis result, the information processing apparatus determines whether analysis based on higher magnification image data is necessary. When analysis based on the higher magnification image data is necessary, the information processing apparatus notifies a request of transmitting the higher magnification image data for the area image, receives and analyzes the higher magnification image data transmitted in response to the transmission request, and transmits the analysis result. This arrangement can quickly provide high-accuracy diagnosis assistance for a tissue sample image from a pathologist regardless of the restriction of the transmission capacity. 1. An information processing apparatus which assists diagnosis based on a tissue sample image obtained by staining and capturing a tissue , comprising:a first receiver that receives lower-magnification image data among a plurality of image data obtained at different magnifications for an area image selected in the tissue sample image;a first analyzer that analyzes the area image based on the lower-magnification image data received by said first receiver, and generates first feature information;a determination unit that determines whether analysis based on higher-magnification image data is necessary for the area image, based on the first feature information generated by said first analyzer;a notification unit that notifies a request of transmitting the higher-magnification image data for the area image, when said determination unit determines that analysis based on the higher-magnification image data is necessary;a second receiver that ...

MICROSCOPE APPARATUS AND CONTROL METHOD FOR SAME

A microscope apparatus which captures images of an object by image sensors having different focusing positions in an optical axis direction and acquires image data of plural layers of the object, includes: a judgment unit which divides a whole region of the image data obtained from the image sensors into plural blocks and judges whether or not each block includes an object image; and a data reducing unit which reduces a data volume of the image data of all of the layers in a block which is judged not to include an object image. The judgment unit selects two or more layers from a block which is being subjected to judgment, respectively evaluates whether or not the image data of the selected layers includes the object image, and judges whether or not the block includes the object image on the basis of the evaluation results. 1. A microscope apparatus which captures images of an object by a plurality of image sensors having different focusing positions in an optical axis direction and acquires image data of a plurality of layers of the object , comprising:a judgment unit which divides a whole region of the image data obtained from the image sensors into a plurality of blocks and judges whether or not each block includes an object image; anda data reducing unit which reduces a data volume of the image data of all of the layers in a block which is judged by the judgment unit not to include an object image,wherein the judgment unit selects two or more layers from a plurality of layers of a block which is being subjected to judgment, respectively evaluates whether or not the image data of the two or more selected layers includes the object image, and judges whether or not the block includes the object image on the basis of the evaluation results.2. The microscope apparatus according to claim 1 , wherein the judgment unit judges that the block does not include the object image if none of the image data of the two or more selected layers includes an object image.3. The ...

COMPUTER-ASSISTED KARYOTYPING

A system and method for computer-assisted karyotyping includes a processor which receives a digitized image of metaphase chromosomes for processing in an image processing module and a classifier module. The image processing module may include a segmenting function for extracting individual chromosome images, a bend correcting function for straightening images of chromosomes that are bent or curved and a feature selection function for distinguishing between chromosome bands. The classifier module, which may be one or more trained kernel-based learning machines, receives the processed image and generates a classification of the image as normal or abnormal. 1. A method for computer-assisted karyotyping , comprising: a segmenting function adapted for segmenting the digitized image for extracting individual chromosome images;', 'a bend correcting function adapted to straightening images of chromosomes that are bent or curved;', 'a feature selection function adapted for distinguishing between chromosome bands;, 'inputting a digitized image of metaphase chromosomes into a processor comprising an image processing module and a classifier module, wherein the image processing module compriseswherein the classifier module generates a classification of the image as normal or abnormal and generates an output therefrom.2. The method of claim 1 , wherein the classifier module comprises at least one kernel-based learning machine.3. The method of claim 2 , wherein the classifier module comprises a plurality of kernel-based learning machines claim 2 , wherein each kernel-based learning machine classifies a different group of chromosomes.4. The method of claim 3 , wherein the classifier module further comprises a combined kernel-based learning machine for receiving an output of each of the plurality of kernel-based learning machines to generate a combined result for all chromosomes.5. The method of claim 2 , wherein the kernel-based learning machine is a support vector machine.6. The ...

CELL ANALYSIS METHOD, CELL ANALYSIS DEVICE, AND CELL ANALYSIS PROGRAM

Provided is a cell analysis method in a cell analysis device D that uses an optical path length image of a cell colony formed of a large number of cells to analyze the cell colony, the method comprising: acquiring the optical path length image of the cell colony by an acquisition unit of the cell analysis device; extracting a circular shape corresponding to a cell nucleus of the cell in the acquired optical path length image by an extraction unit of the cell analysis device extracts; comparing an inner optical path length of the extracted circular shape and an outer optical path length of the extracted circular shape by a comparison unit of the cell analysis device extracts; and analyzing the cell colony based on the comparison result by analysis unit of the cell analysis device. 1: A cell analysis method in a cell analysis device that uses an optical path length image of a cell colony formed of a large number of cells to analyze the cell colony , the method comprising:acquiring the optical path length image of the cell colony by an acquisition unit of the cell analysis device;extracting a circular shape corresponding to a cell nucleus of the cell in the acquired optical path length image by an extraction unit of the cell analysis device extracts;comparing an inner optical path length of the extracted circular shape and an outer optical path length of the extracted circular shape by a comparison unit of the cell analysis device extracts; andanalyzing the cell colony based on the comparison result by analysis unit of the cell analysis device.2: The cell analysis method according to claim 1 , wherein in the comparing and the analyzing claim 1 , when the outer optical path length of the circular shape is larger than the inner optical path length of the circular shape claim 1 , the cell is determined as a cell with good quality.3: The cell analysis method according to claim 2 , wherein in the analyzing claim 2 , when the number of cells per unit claim 2 , which are ...

Automated fish reader using learning machines

An automated reader for reading fluorescence in-situ hybridization signals includes one or more computer processors for receiving a digitized FISH image and executing the steps of converting colors within the image to a hue value, separately for each color extracting quantitative values to detect the presence of signals corresponding to spots and applying a plurality of algorithms to extract features from the signals to determine cell shapes and segment cells within the FISH image. After recombining the signals, the extracted features for the colors learning machines are used to classify the spots according to the color and separate merged signals of classified spots that are in close proximity to each other within the image. The classified spots are counted to determine relative frequency of colors and a report is generated providing the number of classified spots of each color.

METHOD AND SYSTEM FOR IMAGE PROCESSING TO DETERMINE BLOOD FLOW

Embodiments include a system for determining cardiovascular information for a patient. The system may include at least one computer system configured to receive patient-specific data regarding a geometry of the patient's heart, and create a three-dimensional model representing at least a portion of the patient's heart based on the patient-specific data. The at least one computer system may be further configured to create a physics-based model relating to a blood flow characteristic of the patient's heart and determine a fractional flow reserve within the patient's heart based on the three-dimensional model and the physics-based model. 1184-. (canceled)185. A method for processing images to determine cardiovascular information , comprising the steps of:receiving image data including a plurality of coronary arteries originating from an aorta;processing the image data to generate three-dimensional shape models of the coronary arteries;simulating a blood flow for the generated three-dimensional shape models of the coronary arteries; anddetermining a fractional flow reserve (FFR) of the coronary arteries based on a blood flow simulation result, wherein in the step of simulating the blood flow, a computational fluid dynamics model is applied to the three-dimensional shape models of the coronary arteries, a lumped parameter model is combined with the computational fluid dynamics model, and a simplified coronary artery circulation model including coronary arteries, capillaries of the coronary arteries and coronary veins is used as the lumped parameter model.186. The method of claim 185 , wherein claim 185 , when simulating the blood flow claim 185 , when applying the computational fluid dynamics model to the three-dimensional shape models of the coronary arteries claim 185 , using an aorta blood pressure pattern as an inlet boundary condition.187. The method of claim 185 , wherein simulating the blood flow comprises determining lengths of centerlines of the three- ...

SYSTEM AND METHOD FOR DETECTION AND SORTING OF CELLS

A system and method for detection of cells and sorting of cells are disclosed. Target cells, such as circulating tumor cells (CTCs) or antigen-specific antibody producing circulating memory B cells from COVID-19 patients, may be of interest. Magnetic beads may be bound to the target cells. After which, the bead-bound target cells may be identified using an applied magnetic field. In one example, magnetic sensors may be used to detect movement of the bead-bound target cells responsive to an applied magnetic field. In another example, an optical sensor may be used to detect movement of the bead-bound target cells responsive to an applied magnetic field. Further, separate from identification of the target cells, the bead-bound target cells may be sorted using an applied magnetic field. In this way, a magnetic field may be used for target cell identification and target cell sorting in order to detect and collect target cells of interest at the single-cell resolution. 1. An apparatus configured to determining whether a magnetic bead-labeled target cell is present in a fluid , the apparatus comprising:a well configured to house the fluid containing particles and including at least one outlet;at least one magnetic field generator configured to generate a magnetic field to at least a part of the well;one or more sensors configured to generate sensor data; and control the magnetic field generator to generate the magnetic field to the at least a part of the well;', 'identify, based on the sensor data responsive to the magnetic field, the magnetic bead-labeled target cell and an associated location within the well; and', 'control the magnetic field generator, based on the associated location within the well of the magnetic bead-labeled target cell and the at least one outlet, in order to move the magnetic bead-labeled target cell toward the at least one outlet, thereby sorting the magnetic bead-labeled target cell, in order to remove the magnetic bead-labeled target cell from ...

HOMOGENOUS ASSAY (II)

Among other things, the present disclosure is related to devices and methods of performing biological and chemical assays, such as but not limited to immunoassays and nucleic assay acid, particularly a homogeneous assay that does not use a wash step and that is fast (e.g., 60 seconds from dropping a sample to displaying results). The present disclosure is related to both competitive and non-competitive homogeneous assays. 1. A method for performing a competitive assay of an analyte in a liquid sample , comprising:(a) providing a liquid sample that contains or is suspected of containing an analyte;(b) providing one or more beads that have a capture agent attached onto the surface of the one or more beads, wherein the capture agent specifically binds to the analyte;(c) providing a labeled detection agent, wherein the labeled detection agent that binds with the analyte or the capture agent;(d) providing a sample holder that is configured to make the liquid sample into a thin layer;(e) having the liquid sample in the sample holder and making the liquid sample forming form a thin layer having a thickness of 200 um or less, wherein the one or more beads and the labeled competitive detection agent are mixed with the liquid sample;(f) taking, after step (e), without washing the liquid sample, at least two images, including a first image and a second image, of a common area of the sample layer, wherein the common area of the sample layer is an area of the liquid sample that contains at least one bead, wherein the first image is a direct image for measuring a position of a bead in the common area; and the second image is a signal image for measuring a signal from the labeled competitive detection agent; and(g) after (f), comparing and analyzing the first image and the second image to identify the signal at the one or more beads;wherein the beads have various shape and have a dimension in the range of 0.05 um to 50 um, wherein the spacing in the sample holder is such that in ...

Laser Capture Microdissection Systems and Method for Image Analysis

A system and method for performing tissue image analysis and region of interest identification for further processing using laser capture microdissection is provided. The invention provides both an interactive mode and a high-throughput batch mode. 1. A computer-implemented method for image analysis , the computer-implemented method comprising:receiving a first image of a first tissue sample using laser capture microdissection;selecting a database containing parameters;classifying the first image into at least one region of interest employing the parameters from the database;updating the parameters of the database with data from the first image to produce updated parameters;capturing a second image of a tissue sample using laser capture microdissection, classifying the second image into regions of interest employing the updated parameters from the database based; andupdating the parameters of the database a second time with data from the second image.2. The computer-implemented method for image analysis of claim 1 , further comprising claim 1 , classifying at least one of the first image or the second image into at least one non-region of interest employing the parameters from the database.3. The computer-implemented method for image analysis of claim 1 , wherein the first image and the second image are captured from the same tissue sample.4. The computer-implemented method for image analysis of claim 1 , wherein the first image and the second image are captured from the different tissue samples. This application claims priority to U.S. Provisional Application Ser. No. 60/410,433, entitled “INTERACTIVE AND AUTOMATED TISSUE IMAGE ANALYSIS WITH GLOBAL TRAINING DATABASE AND VARIABLE-ABSTRACTION PROCESSING IN CYTOLOGICAL SPECIMEN CLASSIFICATION AND LASER CAPTURE MICRODISSECTION APPLICATIONS”, filed on Sep. 13, 2002 which is incorporated herein by reference in its entirety.The invention relates generally to automated tissue image analysis, and in particular, to image ...

HISTOLOGY RECOGNITION TO AUTOMATICALLY SCORE AND QUANTIFY CANCER GRADES AND INDIVIDUAL USER DIGITAL WHOLE HISTOLOGICAL IMAGING DEVICE

Digital pathology is the concept of capturing digital images from glass microscope slides in order to record, visualize, analyze, manage, report, share and diagnose pathology specimens. The present disclosure is directed to a desktop slide scanner, which enables pathologists to scan slides at a touch of a button. Included is a workflow for reliable imaging, diagnosis, quantification, management, and sharing of a digital pathology library. Also disclosed herein is an analysis framework that provides for pattern recognition of biological samples represented as digital images to automatically quantitatively score normal cell parameters against disease state parameters. The framework provides a pathologist with an opportunity to see what the algorithm is scoring, and simply agree, or edit the result. This framework offers a new tool to enhance the precision of the current standard of care. 1. A computer-implemented method for determining and grading of features of a biological sample represented by a digital image , comprising:performing an initial region classification to classify cells within the biological sample;surveying a tumor region to assess disease state to perform a cancer cell classification;grading the cancer cell classification of the biological sample; andgenerating a report of the graded biological sample.2. The method of claim 1 , performing the initial region classification further comprising:applying a pattern recognition algorithm to the digital image to identify tumor cells.3. The method of claim 2 , further comprising:determining a number of tumor cells in the biological sample;determining an 2D area of the tumor cells; anddetermining a ratio of tumor cells to non-tumor cells in the biological sample.4. The method of claim 1 , performing the initial region classification further comprising performing one of a Hematoxylin and Eosin (H&E) nucleus identification claim 1 , an Eosin cytoplasm identification claim 1 , a multispectral analysis and a ...

INFORMATION PROCESSING UNIT, INFORMATION PROCESSING METHOD, AND PROGRAM

An information processing unit includes: a diagnostic image input section that inputs the diagnostic image; an operation information obtaining section that obtains display operation history information representing an operation history of a user who controls displaying of the diagnostic image; a query image generation section that extracts a predetermined region of the input diagnostic image to generate a query image; a diagnosed image obtaining section that supplies the generated query image and the display operation history information to a diagnosed image search unit and obtains the diagnosed image obtained as a search result by the diagnosed image search unit; and a display control section that displays the diagnostic image and the obtained diagnosed image for comparison. 1. An information processing unit that displays a diagnostic image serving as a diagnosis target and a diagnosed image similar to the diagnostic image for comparison , the information processing unit comprising:a diagnostic image input section that inputs the diagnostic image;an operation information obtaining section that obtains display operation history information representing an operation history of a user who controls displaying of the diagnostic image;a query image generation section that extracts a predetermined region of the input diagnostic image to generate a query image;a diagnosed image obtaining section that supplies the generated query image and the display operation history information to a diagnosed image search unit and obtains the diagnosed image obtained as a search result by the diagnosed image search unit; anda display control section that displays the diagnostic image and the obtained diagnosed image for comparison,wherein the diagnosed image search unit includesan image feature amount extraction section that extracts an image feature amount of the query image,a search section that retrieves diagnosed images each of which includes a sub-image with an image feature amount ...

Methods and Systems for Assessing Histological Stains

The present disclosure includes methods of assessing a histologically stained specimen based on a determined color signature of a region of interest of the specimen. Such assessments may be performed for a variety of purposes including but not limited to assessing the quality of the histological stain, as part of identifying one or more biologically relevant features of the image, as part of differentiating one feature of the image from other features of the image, identifying an anomalous area of the stained specimen, classifying cells of the specimen, etc. Also provided are systems configured for performing the disclosed methods and computer readable medium storing instructions for performing steps of the disclosed methods. 167-. (canceled)68. A system for assessing a histologically stained specimen , the system comprising:a) a microscope;b) a digital color camera attached to the microscope and configured to obtain a digital color image of the specimen;c) a library comprising a plurality of reference color signatures specific to biological features of histologically stained reference specimens;d) image processing circuitry configured to:i) define on the digital color image a region of interest (ROI) based on a biological feature of the specimen;ii) separate the digital color image into individual color channels; andiii) determine a color signature for the ROI, wherein the color signature comprises quantification of one or more color parameters over the ROI for one or more of the individual color channels; andiv) compare the determined color signature to one or more reference color signatures of the plurality of reference color signatures of the library to assess the histologically stained specimen.69. The system of claim 68 , wherein the system comprises a single memory connected to the image processing circuitry that stores the library and is configured to receive the digital color image.70. The system of claim 68 , wherein the system comprises a first memory ...

SYSTEMS, METHODS, AND APPARATUS FOR IN VITRO SINGLE-CELL IDENTIFICATION AND RECOVERY

Described herein are systems, methods, and apparatus for automatically identifying and recovering individual cells of interest from a sample of biological matter, e.g., a biological fluid. Also described are methods of enriching a cell type of interest. These systems, methods, and apparatus allow for coordinated performance of two or more of the following, e.g., all with the same device, thereby enabling high throughput: cell enrichment, cell identification, and individual cell recovery for further analysis (e.g., sequencing) of individual recovered cells. 1. A multiscale deposition-well plate (e.g. for use with a system for automated identification and/or recovery of individual cells of interest as described herein) comprising one or more sample wells (e.g. , from three to twenty , or from three to twelve) and zero or more recovery wells (e.g. , 24 , 48 , 96 , at least 24 , at least 48 , at least 96 , etc.).2. The multiscale deposition-well plate of claim 1 , comprising a plurality (e.g. claim 1 , an array) of macro-scale wells (e.g. claim 1 , each macro-scale well with any one or more of length claim 1 , width claim 1 , and/or depth of at least 1 mm claim 1 , at least 3 mm claim 1 , at least 5 mm claim 1 , or at least 8 mm claim 1 , and/or with any one or more of length claim 1 , width claim 1 , and/or depth no greater than about 100mm claim 1 , no greater than about 50 mm claim 1 , or no greater than about 25 mm) claim 1 , wherein each of the macro-scale wells comprise a plurality of micro-scale and/or nano-scale wells (e.g. claim 1 , each micro-scale well with any one or more of length claim 1 , width claim 1 , and/or depth of at least 1μm claim 1 , at least 5μm claim 1 , or at least 10 μm claim 1 , and/or with any one or more of length claim 1 , width claim 1 , and/or depth no greater than about 1000 μm claim 1 , no greater than about 500 μm claim 1 , no greater than about 250 μm claim 1 , or no greater than about 100 μm) (e.g. claim 1 , each nano-scale well ...

Computer Device for Detecting an Optimal Candidate Compound and Methods Thereof

The invention relates to a method for a computer device, for detecting an optimal candidate compound based on a plurality of samples comprising a cell line and one or more biomarkers, and a plate map configuration, wherein the plate map configuration is providing locations of samples comprising cell lines exposed to one or more biomarkers and different concentrations of a candidate compound forming at least one concentration gradient, the candidate compound being comprised in a plurality of candidate compounds, said method comprising generating () phenotypic profiles of each concentration gradient of each of the plurality of candidate compounds at a plurality of successive points in time to form a plurality of compound profiles, wherein generating phenotypic profiles comprises the steps obtaining () image data depicting each sample comprised in the concentration gradient, generating () a class-label and a class for each cell of the samples based on the image data, detecting () the optimal candidate compound by evaluating a comparison criterion on the plurality of compound profiles. Furthermore, the invention also relates to corresponding computer device, a computer program, and a computer program product. 1. A method for a computer device , for detecting an optimal candidate compound based on a plurality of samples comprising a cell line and one or more biomarkers , and a plate map configuration , wherein the plate map configuration is providing locations of samples comprising cell lines exposed to one or more biomarkers and different concentrations of a candidate compound forming at least one concentration gradient , the candidate compound being comprised in a plurality of candidate compounds , said method comprising:generating phenotypic profiles of each concentration gradient of each of the plurality of candidate compounds at a plurality of successive points in time to form a plurality of compound profiles, wherein generating phenotypic profiles comprises the ...

Detecting a defect within a bodily sample

Apparatus and methods are described for analyzing a bodily sample. One or more microscope images of the bodily sample are acquired. Using at least one computer processor at least one sample-informative feature that is indicative of a characteristic of the bodily sample is extracted from the images. Based upon the sample-informative feature, the computer processor determines that there is a defect associated with the bodily sample, and determines a source of the defect. Other applications are also described.

Image Processing Method And Apparatus

The invention is an image processing method and an apparatus for automatic categorisation of elements in an image of a body fluid sample, the apparatus comprising 2. The method according to claim 1 , characterised by{'b': 13', '11, 'positioning an examination area () for each presumably present element on the probability map () associated with the category of the element, and'}{'b': 16', '13', '13', '11, 'regarding the presence of the element () associated with the examination area (), an identically positioned examination area () of at least one further probability map () is taken into account in making the decision.'}3151613. The method according to claim 2 , characterised by taking also into account statistical data () related to the elements in determining the presence of the element () associated with the examination area ().4151413111311. The method according to claim 3 , characterised in that the statistical data () are local statistical data relating to a distribution of probability values () in the examination area () of the actual probability map () and in the identically positioned examination area () of at least one further probability map ().5151413111311. The method according to claim 3 , characterised in that the statistical data () are global statistical data relating to a distribution of probability values () outside the examination area () of the actual probability map () and outside the identically positioned examination area () of at least one further probability map ().61510. The method according to claim 3 , characterised in that the statistical data () are comprehensive statistical data relating to information from further images () belonging together.71112141112141212. The method according to claim 1 , characterised by carrying out the examination relating to the presumably present elements for each probability map () in a way that contiguous groups () of probability values () above a threshold level are found in the probability map () and ...

TECHNIQUES FOR ANALYZING AND DETECTING EXECUTIONAL ARTIFACTS IN MICROWELL PLATES

In various embodiments, an experiment analysis application detects executional artifacts in experiments involving microwell plates. The experiment analysis application computes one or more sets of spatial features based on one or more heat maps associated with a microwell plate. The experiment analysis application then aggregates the set(s) of spatial features to generate a feature vector. The experiment analysis application inputs the feature vector into a trained classifier. In response, the trained classifier generates a label indicating that the microwell plate is associated with a first executional artifact. 1. A computer-implemented method for detecting executional artifacts in experiments involving microwell plates , the method comprising:computing one or more sets of spatial features based on one or more heat maps associated with a first microwell plate;generating a first feature vector based on the one or more sets of spatial features; andinputting the first feature vector into a trained classifier that, in response, generates a first label indicating that the first microwell plate is associated with a first executional artifact.2. The computer-implemented method of claim 1 , further comprising computing an anomaly score based on the first feature vector and a first cluster of feature vectors associated with the first label.3. The computer-implemented method of claim 1 , further comprising:computing a second feature vector that is associated with an experiment based on a plurality of heat maps, wherein the plurality of heat maps includes the one or more heat maps associated with the first microwell plate; andinputting the second feature vector into the trained classifier that, in response, generates a second label that classifies the experiment with respect to a plurality of labels that includes the first label.4. The computer-implemented method of claim 1 , further comprising:computing a second feature vector that is associated with an experiment based on ...

METHODS FOR QUANTITATIVE ASSESSMENT OF MUSCLE FIBERS IN MUSCULAR DYSTROPHY

The disclosure concerns a method for assessing muscular dystrophy-linked protein expression in muscle fibers using digital image analysis of tissue. The method relates to assessing disease severity in individuals with muscular dystrophy. Muscle tissue samples are obtained from patients submitted for evaluation and processed to produce tissue sections mounted on glass slides which have been stained for a muscular dystrophy-linked protein. Digital images of the stained tissue sections are generated and analyzed by applying an algorithm process implemented by a computer to the images. The algorithm process extracts the morphometric and staining features of the muscular dystrophy-linked protein staining in the tissue, and parameters relating to these features are used to score the disease status for each patient submitted for evaluation. The score of disease status is ultimately used to infer disease severity, monitor the efficacy of a therapeutic approach, or select patients as candidates for a therapeutic approach. 1. A method comprising:capturing at least one digital image of at least one stained muscle tissue section;extracting at least one image analysis feature from each muscle fiber in the at least one digital image, wherein the at least one image analysis feature is selected from the group consisting of staining features and morphometric features;combining at least one staining and morphometric feature to derive a score of disease status; andinterpreting the score of disease status to draw inferences associated with the severity of disease.2. The method of claim 1 , wherein the at least one tissue section is stained for at least one muscular marker selected from the group consisting of a muscular dystrophy-linked protein and a muscle fiber membrane biomarker.3. The method of claim 2 , wherein the muscular dystrophy-linked protein is a protein product of a gene that when mutated claim 2 , or otherwise disrupted claim 2 , gives rise to at least one muscular ...

METHOD AND APPARATUS FOR AUTOMATED PLATELET IDENTIFICATION WITHIN A WHOLE BLOOD SAMPLE FROM MICROSCOPY IMAGES

A method and apparatus for identifying platelets within a whole blood sample. The method includes the steps of: a) adding at least one colorant to the whole blood sample, which colorant is operable to tag platelets; b) disposing the blood sample into a chamber defined by at least one transparent panel; c) imaging at least a portion of the sample quiescently residing within the chamber to create one or more images; and d) identifying one or more platelets within the sample using an analyzer adapted to identify the platelets based on quantitatively determinable features within the image using a analyzer, which quantitatively determinable features include intensity differences. 1. A method for identifying platelets within a biologic fluid sample , comprising:adding at least one colorant to the sample which colorant is operable to tag the platelets;imaging at least a portion of the sample quiescently residing within a chamber to create one or more images; andidentifying one or more platelets within the sample using an analyzer adapted to identify the platelets within the one or more images, which identifying includes comparing an image intensity value of plasma to an image intensity value of the one or more platelets.2. The method of claim 1 , wherein the identifying includes comparing the image intensity value of plasma to the image intensity of the one or more platelets from one or more local regions within the one or more images.3. The method of claim 2 , wherein the image intensity value of plasma is an intensity of fluorescent light emitted from the plasma and the image intensity value of the one or more platelets is an intensity of fluorescent light emitted from the one or more platelets.4. The method of claim 1 , further comprising identifying anomaly image portions and estimating the number of platelets in the anomaly image portions.5. The method of claim 1 , wherein the identifying includes evaluating platelet candidates using a directional contrast of an ...

Field-invariant quantitative magnetic-resonance signatures

A system that determines an invariant magnetic-resonance (MR) signature of a biological sample is disclosed. During operation, the system determines a magnetic-resonance (MR) model of voxels in a biological sample based on differences between MR signals associated with the voxels in multiple scans and simulated MR signals. The MR signals are measured or captured by an MR scanner in the system during multiple MR scans, and based on scanning instructions, and the simulated MR signals for the biological sample are generated using the MR model and the scanning instructions. Moreover, the system iteratively modifies the scanning instructions (including a magnetic-field strength and/or a pulse sequence) in the MR scans based on the differences until a convergence criterion is achieved. Then, the system stores, in memory, an identifier of the biological sample and a magnetic-field-strength-invariant MR signature of the biological sample that is associated with the MR model.

Imaging Blood Cells

This document describes methods, systems and computer program products directed to imaging blood cells. The subject matter described in this document can be embodied in a method of classifying white blood cells (WBCs) in a biological sample on a substrate. The method includes acquiring, by an image acquisition device, a plurality of images of a first location on the substrate, and classifying, by a processor, objects in the plurality of images into WBC classification groups. The method also includes identifying, by a processor, objects from at least some classification groups, as unclassified objects, and displaying, on a user interface, the unclassified objects and at least some of the classified objects. 1. (canceled)2. A method of classifying white blood cells (WBCs) in a biological sample on a substrate , the method comprising:acquiring, by an image acquisition device, a plurality of images of a first location on the substrate;identifying one or more non-WBC objects in the plurality of images, the non-WBC objects including one of more of platelets, clumps, giant platelets, and micromegakaryocytes;determining, by one or more processing devices using a first classification process, that a given object in the plurality of images is associated with a first classification group selected from a set of multiple WBC classification groups, wherein the first classification process is based on a first set of features, and wherein the one or more non-WBC objects are excluded from the first classification process;determining, by the one or more processing devices using a second classification process, that the given object is associated with a second classification group, wherein the second classification process is based on a second set of features different from the first set of features;in response to determining that the given object is associated with the second classification group, changing a classification of the given object; andpresenting, on a user interface, a ...

APPARATUS, METHOD AND PROGRAM FOR 3D DATA ANALYSIS, AND MICROPARTICLE ANALYSIS SYSTEM

In an example embodiment, may be embodied in a data analysis apparatus comprises a control unit configured to provide data representative of a three dimensional image, the three dimensional image including at least a three dimensional coordinate space which includes at least one plane that divides the three dimensional coordinate space into at least two regions, a display unit configured to produce the three dimensional image based on the data representative of the three dimensional image, and an input unit configured to provide data representative of at least one of a movement and a position of the at least one plane. In other example embodiments, the present disclosure may be embodied in a data analysis server, a data analysis system, and/or a computer readable medium. 1. A data analysis apparatus to analyze microparticle data measured from a flow cytometer comprising:a processor configured to produce a three dimensional image based on data representative of the three dimensional image, and the three dimensional image represents a characteristic distribution of microparticles in a coordinate space,wherein the three dimensional image includes graphics corresponding to the microparticles in the coordinate space, andwherein the graphics are displayed in at least one of a different color, size, shape, and mass.2. The data analysis apparatus of claim 1 , further comprising a display configured to produce the three dimensional image based on the data representative of the three dimensional image.3. The data analysis apparatus of claim 1 , an input unit configured to provide data representative of at least one of a movement and a position of at least one plane within the coordinate space.4. The data analysis apparatus of claim 1 , wherein the graphics include a plurality of regions.5. The data analysis apparatus of claim 4 , wherein a first distribution frequency is calculated for a first region and a second distribution frequency is calculated for a second region.6. The ...

Method for Microscopic Image Acquisition Based on Sequential Section

A method for microscopic image acquisition based on a sequential slice. The method includes; acquiring a sample of the sequential slice and a navigation image thereof; identifying and labeling the sample of the sequential slice in the navigation image by utilizing methods of image processing and machine learning; placing the sample of the sequential slice in a microscope, establishing a coordinate transformation matrix for a navigation image-microscope actual sampling space coordinate, and navigating and locating a random pixel point in the navigation image to a center of the microscope's visual field; locating the sample of the sequential slice under a low resolution visual field, binding a sample acquisition parameter; based on the binding of the sample acquisition parameter, recording a relationship of relative of locations between a center point of a high resolution acquisition region and a center point after being matched with a sample template.

System and Method for Automatically Analyzing Phenotypical Responses of Cells

A system and a method to analyze a phenotypical response of cells to a treatment are disclosed in which a model development module receives images of a plurality of reference cell carriers and treatment information associated with the plurality of reference cell carriers, identifies parameters of cells in the image that distinguish those reference cell carriers to which the treatment has been applied from other reference cell carriers, and trains a model using the identified parameters. A high-content imaging system includes an image capture device, and the image acquisition module receives from the image capture device a plurality of images of cell carriers to be evaluated. The model application module applies the trained model to the plurality of images of the cell carriers to be evaluated to predict a concentration of the treatment applied to each of the cell carriers evaluated. 1. A system to analyze a phenotypical response of cells to a treatment , comprising:a high-content analysis system, wherein the high-content analysis system includes an image capture device;a model development module that receives images of a plurality of reference cell carriers and treatment information associated with the plurality of reference cell carriers, identifies parameters of cells in the image that distinguish those reference cell carriers to which the treatment has been applied from other reference cell carriers, and trains a model using the identified parameters;an image acquisition module that receives from the image capture device a plurality of images of cell carriers to be evaluated; anda model application module that applies the trained model to the plurality of images of the cell carriers to be evaluated to indicate a response level of cells in each of the cell carriers to be evaluated.2. The system of claim 1 , wherein the images of the plurality of reference cell carriers are one of images of wells of a reference tray claim 1 , images of a set of slides claim 1 , and ...

RECONFIGURABLE INTEGRATED CIRCUITS FOR ADJUSTING CELL SORTING CLASSIFICATION

Aspects of the present disclosure include reconfigurable integrated circuits for characterizing particles of a sample in a flow stream. Reconfigurable integrated circuits according to certain embodiments are programmed to calculate parameters of a particle in a flow stream from detected light; compare the calculated parameters of the particle with parameters of one or more particle classifications; classify the particle based on the comparison between the parameters of the particle classifications and the calculated parameters of the particle; and adjust one or more parameters of the particle classifications based on the calculated parameters of the particle. Methods for characterizing particles in a flow stream with the subject integrated circuits are also described. Systems and integrated circuit devices programmed for practicing the subject methods, such as on a flow cytometer, are also provided. 1. A reconfigurable integrated circuit programmed to:calculate parameters of a particle in a flow stream from detected light;compare the calculated parameters of the particle with parameters of one or more particle classifications;classify the particle based on the comparison between the parameters of the particle classifications and the calculated parameters of the particle; andadjust one or more parameters of the particle classifications based on the calculated parameters of the particle.2. The reconfigurable integrated circuit according to claim 1 , wherein the particle classifications comprise a sort classification.3. The reconfigurable integrated circuit according to any one of - claim 1 , wherein classifying the particle comprises generating a particle sort decision.4. The reconfigurable integrated circuit according to claim 3 , wherein the integrated circuit is programmed to generate the particle sorting decision based on a threshold between the calculated parameters of the particle and the parameters of the particle classifications.5. The reconfigurable ...

Method for detection of cells in a cytological sample having at least one anomaly

Disclosed is a method for detecting cells having at least one anomaly in a cytological sample on the basis of at least one first digitised digitised-electron-microscopy image of the sample.

Image processing method and computer-readable recording medium having recorded thereon image processing program

An image processing method that includes obtaining an original image including a cultured cell image with a background image, dividing the original image into blocks, each composed of a predetermined number of pixels, and obtaining a spatial frequency component of an image in each block for each block, and classifying each block as the one belonging to a cell cluster corresponding to the cell or the one belonging to other than the cell cluster in a two-dimensional feature amount space composed of a first feature amount which is a total of intensities of low frequency components having a frequency equal to or lower than a predetermined frequency and a second feature amount which is a total of intensities of high frequency components having a higher frequency than the low frequency component, and segmenting the original image into an area occupied by the blocks classified as the cell cluster and another area.

METHOD AND SYSTEM FOR AUTOMATIC CHROMOSOME CLASSIFICATION

Method and system for automatic chromosome classification is disclosed. The system, alternatively referred as a Residual Convolutional Recurrent Attention Neural Network (Res-CRANN), utilizes property of band sequence of chromosome bands for chromosome classification. The Res-CRANN is end-to-end trainable system, in which a sequence of feature vectors are extracted from the feature maps produced by convolutional layers of a Residual neural networks (ResNet), wherein the feature vectors correspond to visual features representing chromosome bands in an chromosome image. The sequence feature vectors are fed into Recurrent Neural Networks (RNN) augmented with an attention mechanism. The RNN learns the sequence of feature vectors and the attention module concentrates on a plurality of Regions-of-interest (ROIs) of the sequence of feature vectors, wherein the ROIs are specific to a class label of chromosomes. The Res-CRANN provides higher classification accuracy as compared to the state-of the-art methods for chromosome classification. 1. A processor implemented method for chromosome classification , the method comprising:{'b': '202', 'receiving, via one or more hardware processors, a chromosome image comprising a chromosome with a plurality of chromosome bands ();'}{'b': '204', 'extracting, via one or more hardware processors, visual features associated with the chromosome bands of the chromosome by generating a plurality of feature maps with dimension G×H×K();'}{'sub': 'g', 'b': '206', 'obtaining, via one or more hardware processors, a plurality of feature vectors from the plurality of feature maps, with each feature vector Fof dimension H*K, by applying horizontal slicing on the plurality of feature maps ();'}{'b': '208', 'concatenating, via one or more hardware processors, the plurality of feature vectors in sequence from a top chromosome band to a bottom chromosome band among the plurality of chromosome bands to generate a feature sequence (Si=G×H*K) (); and'}{'b': ' ...

Tissue potency determination through quantitative histomorphology analysis

Systems and methods for performing quantitative histopathology analysis for determining tissue potency are disclosed. According to some embodiments, a method training a tissue classifier is provided. According to the method, training the tissue classifier includes generating feature fingerprints of detected nuclei within slide images in a control library and clustering the slide images based on their corresponding feature fingerprints. According to some embodiments, a method for utilizing the trained tissue classifier is provided. According to the method, the trained tissue classifier determines whether tissue in an unknown slide image corresponds to slide images clustered during the training of the tissue classifier.

Analysis device

An analysis device includes an analysis unit configured to receive scattered light, transmitted light, fluorescence, or electromagnetic waves from an observed object located in a light irradiation region light-irradiated from a light source and analyze the observed object on the basis of a signal extracted on the basis of a time axis of an electrical signal output from a light-receiving unit configured to convert the received light or electromagnetic waves into the electrical signal.

SYSTEM AND METHOD FOR IMPROVED DETECTION OF OBJECTS OF INTEREST IN IMAGE DATA BY MANAGEMENT OF FALSE POSITIVES

A system and method for improved detection of objects of interest in image data using adaptive stepwise classification and hierarchical decision diagrams to manage false positives is provided. The present invention uses an adaptive stepwise classification approach, preferably based on a hierarchical binary decision diagram (BDD), to enable the efficient management of false positive objects to improve detection performance. The present invention is particularly suited for the reduction of false positives during the detection of acid fast bacilli associated with tuberculosis. 1. An image analysis system , comprising:a computer aided detection (CAD) unit for detecting objects of interest; anda stepwise classification unit for managing a number of false positives generated by the CAD unit.2. The system of claim 1 , wherein the objects of interest comprise acid fast bacilli. This application claims priority to U.S. Provisional Patent Application No. 61/409,776, filed Nov. 3, 2010, which is incorporated herein by reference in its entirety.1. Field of the InventionThis invention relates to image analysis and, more specifically, to a system and method for improved detection of objects of interest in image data using adaptive stepwise classification and hierarchical decision diagrams to manage false positives.2. Background of the Related ArtTuberculosis (TB) is the main cause of deaths due to infectious disease. According to the World Health Organization (WHO), one-third of the world's population are carriers of these TB bacteria, originating about 10 million cases of active tuberculosis worldwide and approximately 3 million deaths annually. TB infection is currently spreading at the rate of one person per second. Bacteria of the mycobacterium family produce a positive stain with special dyes and are referred to as acid-fast bacteria (AFB). The presence of AFB on a sputum smear or other specimen often indicates the TB disease.Routine visual slide screening for identification ...

Imaging Blood Cells

This document describes methods, systems and computer program products directed to imaging blood cells. The subject matter described in this document can be embodied in a method of classifying white blood cells (WBCs) in a biological sample on a substrate. The method includes acquiring, by an image acquisition device, a plurality of images of a first location on the substrate, and classifying, by a processor, objects in the plurality of images into WBC classification groups. The method also includes identifying, by a processor, objects from at least some classification groups, as unclassified objects, and displaying, on a user interface, the unclassified objects and at least some of the classified objects. 142.-. (canceled)43. A method of classifying white blood cells (WBCs) in a biological sample on a substrate , the method comprising:acquiring, by an image acquisition device, a plurality of images of a first location on the substrate;classifying, by a processor, objects in the plurality of images into WBC classification groups;identifying, by a processor, objects from at least some classification groups, as unclassified objects; anddisplaying, on a user interface, the unclassified objects and at least some of the classified objects.44. The method of claim 43 , wherein acquiring the plurality of images includes acquiring the plurality of images using a 50× objective lens.45. The method of claim 43 , comprising determining at least one of a value for a nuclear complexity of neutrophils in the WBC classification groups and a value for an atypicality of lymphocytes in the WBC classification groups.46. The method of claim 45 , comprising displaying at least one of the value for the nuclear complexity and the value for the atypicality on the user interface.47. The method of claim 43 , comprising removing non-WBC objects from the images prior to classifying the objects.48. The method of claim 47 , wherein the non-WBC objects include one or more of platelets claim 47 , ...

COMPUTATIONAL MICROSCOPY BASED-SYSTEM AND METHOD FOR AUTOMATED IMAGING AND ANALYSIS OF PATHOLOGY SPECIMENS

Described herein are systems and methods for assessing a biological sample. The methods include: characterizing a speckled pattern to be applied by a diffuser; positioning a biological sample relative to at least one coherent light source such that at least one coherent light source illuminates the biological sample; diffusing light produced by the at least one coherent light source; capturing a plurality of illuminated images with the embedded speckle pattern of the biological sample based on the diffused light; iteratively reconstructing the plurality of speckled illuminated images of the biological sample to recover an image stack of reconstructed images; stitching together each image in the image stack to create a whole slide image, wherein each image of the image stack at least partially overlaps with a neighboring image; and identifying one or more features of the biological sample. The methods may be performed by a near-field Fourier Ptychographic system. 134-. (canceled)35. A method performed by a far-field Fourier ptychographic system for assessing a biological sample , comprising:positioning a biological sample relative to an illumination source such that the biological sample is backlit;applying light to the biological sample from the illumination source in rapid succession, wherein the illumination source is configured to generate incident rays of light when applied to the biological sample;projecting the diffraction pattern of the incident rays of light onto a sensor;collecting one or more diffraction patterns generated from an optical transmission function of the biological sample to reconstruct the original optical transmission function of the biological sample;stitching images together by matching key points across the overlapped regions of the sample images; andidentifying one or more features of the biological sample, wherein the one or more features are selected from a group consisting of: cell count, nucleus, edges, groupings, clump size, and a ...

SYSTEMS AND METHODS FOR SEGMENTATION AND PROCESSING OF TISSUE IMAGES AND FEATURE EXTRACTION FROM SAME FOR TREATING, DIAGNOSING, OR PREDICTING MEDICAL CONDITIONS

Apparatus, methods, and computer-readable media are provided for segmentation, processing (e.g., preprocessing and/or postprocessing), and/or feature extraction from tissue images such as, for example, images of nuclei and/or cytoplasm. Tissue images processed by various embodiments described herein may be generated by Hematoxylin and Eosin (H&E) staining, immunofluorescence (IF) detection, immunohistochemistry (IHC), similar and/or related staining processes, and/or other processes. Predictive features described herein may be provided for use in, for example, one or more predictive models for treating, diagnosing, and/or predicting the occurrence (e.g., recurrence) of one or more medical conditions such as, for example, cancer or other types of disease. 1117-. (canceled)118. A system for predicting the occurrence of a medical condition , the system comprising:(1) a database configured to store patient data, including at least one sample image of a tissue sample treated with a plurality of flurochrome labeled antibodies; and (a) generate a patient image dataset, using the at least one sample image, that includes values for one or more texture features selected from a group of features consisting of (i) homogeneity and (ii) correlation;', '(b) evaluate at least the patient image dataset with a Support Vector Regression for Censored Data (SVRc) algorithm executed as code by the processor, where the SVRc algorithm is configured to output a value corresponding to a risk score for a medical condition occurrence based on the patient image dataset, wherein the SVRc algorithm is generated by performing regression, using code executed in the processor, on a population dataset, where each member of the population has measurement values corresponding to each feature of the patient image dataset;', '(c) assign the patient to a high probability of a medical condition occurrence where the output value is below a pre-determined threshold and assign the patient to a low probability ...

Crowdsourcing and deep learning based segmenting and karyotyping of chromosomes

The most challenging problems in karyotyping are segmentation and classification of overlapping chromosomes in metaphase spread images. Often chromosomes are bent in different directions with varying degrees of bend. Tediousness and time consuming nature of the effort for ground truth creation makes it difficult to scale the ground truth for training phase. The present disclosure provides an end-to-end solution that reduces the cognitive burden of segmenting and karyotyping chromosomes. Dependency on experts is reduced by employing crowdsourcing while simultaneously addressing the issues associated with crowdsourcing. Identified segments through crowdsourcing are pre-processed to improve classification achieved by employing deep convolutional network (CNN).

OPTICAL DETECTING SYSTEM

According to an embodiment of the present disclosure, provided is an optical detection system for detecting a laser speckle generated by multiple scattering of a wave irradiated toward a sample from a wave source, and based on a change in the laser speckle over time, detecting the presence of microbes in the sample in real time. 1. An optical measurement apparatus comprising:a wave source;an optical unit configured to transfer a wave generated in the wave source to a first path or a second path;a first speckle generation unit disposed on the first path and including a static scattering medium to scatter the first wave incident along the first path and generate a first speckle;a first image sensor configured to detect the first speckle in time series order;a sample accommodation unit disposed on the second path and including a sample to be measured;a second image sensor configured to detect an optical signal generated in the sample in time series order; anda controller configured to obtain a temporal correlation of the first speckle using the detected first speckle and control an operation of the second image sensor based on the obtained temporal correlation of the first speckle.2. The optical measurement apparatus of claim 1 , wherein the sample accommodation unit comprises a second speckle generation unit configured to scatter a second wave incident along the second path and generate a second speckle.3. The optical measurement apparatus of claim 2 , wherein the controller obtains a temporal correlation of a detected second speckle using the detected second speckle claim 2 , and estimates the presence or concentration of microbe in the sample based on the obtained temporal correlation of the second speckle.4. The optical measurement apparatus of claim 1 , wherein the controller determines a change in the property of the first wave based on the temporal correlation of the first speckle claim 1 , and controls an operation of the second image sensor according to the ...

Cell Imaging Systems and Methods

Disclosed herein are systems and methods for imaging cells. Quantitative phase imaging uses variations in the index of refraction of a sample as a source of endogenous contrast, providing label-free information of sub-cellular structures and allowing for the reconstruction of valuable biophysical parameters, such as cell dry-mass at femtogram scales, mass transport, and sample thickness and fluctuations at nanometer scales. As a result, QPI has become a valuable tool in biology and medicine. However, QPI has suffered from the need for trans-illumination through relatively thin objects in order to gain access to the forward-scattered field, which carries crucial low spatial frequency information of a sample and avoid contributions from multiple scattered light or out-of-focus planes. The disclosed methods and systems can provide for reconstruction of QPI and corresponding analysis for imaging samples of cells in thick samples using an epi-illumination configuration. 1. A method comprising:cross-correlating a sample model of a desired cell with a quantitative phase image of cells to compare cells of the quantitative phase image with the sample model of the desired cell;indicating a cell from the cells similar to the sample model as a first desired cell candidate;indicating a cell from the cells having a light frequency absorption outside of a threshold standard deviation from the cells as a second desired cell candidate; anddetermining, based on the quantitative phase image of cells and a distribution of light frequency absorption data for the cells, if the first desired cell candidate and the second desired cell candidate are the same cell.2. The method of claim 1 , wherein the distribution of light frequency absorption data for the cells is obtained by:illuminating the cells with light at a first frequency;illuminating the cells with light at a second frequency; andreceiving two or more illuminated images of the cells.3. The method of further comprising comparing a ...

Automated parasite analysis system

A parasite analysis system includes a pressure vessel configured to store a biological sample, an imaging cell connected to the pressure vessel, and a waste depository connected to the imaging cell. An input valve controls whether biological sample can flow from the pressure vessel into the imaging cell and an output valve controls whether biological sample can flow from the imaging cell into the waste depository. The parasite analysis system also includes a camera that captures a chronological set of images of a portion of the biological sample in the imaging cell and an image analysis system that analyzes the chronological set of images to generate an estimate of a number of parasites in the portion of the biological sample. Estimates for multiple portions of the biological sample may be generated and sampling techniques used to estimate the number of parasites in the entire biological sample.

Hough transform-based vascular network disorder features on baseline fluorescein angiography scans predict response to anti-vegf therapy in diabetic macular edema

Embodiments facilitate prediction of anti-vascular endothelial growth (anti-VEGF) therapy response in DME or RVO patients. A first set of embodiments discussed herein relates to training of a machine learning classifier to determine a prediction for response to anti-VEGF therapy based on a vascular network organization via Hough transform (VaNgOGH) descriptor generated based on FA images of tissue demonstrating DME or RVO. A second set of embodiments discussed herein relates to determination of a prediction of response to anti-VEGF therapy for a DME or RVO patient (e.g., non-rebounder vs. rebounder, response vs. non-response) based on a VaNgOGH descriptor generated based on FA imagery of the patient.

Methods of implementing an artificial intelligence based neuroradiology platform for neurological tumor identification and for t-cell therapy initiation and tracking and related precision medical treatment predictive modeling platforms

A method of implementing an artificial intelligence based neuroradiology platform for neurological tumor identification comprises providing a multilayer convolutional network for neurological tumor identification configured for segmenting data sets of full neurologic scans into resolution voxels; supervised learning and validation of the platform by classification of tissue within classification voxels of a specific given training and validation data sets by the multilayer convolutional network for neurological tumor identification with each classification voxel of the training and validation data sets having a predetermined ground truth; and implementing the platform by classification of tissue within classification voxels of a specific given patient data sets by the multilayer convolutional network for neurological tumor identification with each classification voxel of each data set assigned a label. The platform may be used for T-cell therapy initiation and tracking. An artificial intelligence based neuroradiology platform implemented according to the method is disclosed.

AUGMENTED DIGITAL MICROSCOPY FOR LESION ANALYSIS

Systems and methods are provided for augmenting digital analysis of lesions. An image of tissue having a glandular epithelial component is generated. The image represents a plurality of medium-scale epithelial components. For each of a plurality of cells within the image, a representative point is identified to provide a plurality of representative points for each of the plurality of medium-scale epithelial components. For each of a subset of the plurality of medium-scale epithelial components, a graph connecting the plurality of representative points is constructed. A plurality of classification features is extracted for each of the subset of medium-scale epithelial components from the graph constructed for the medium-scale epithelial component. A clinical parameter is assigned to each medium-scale epithelial component according to the extracted plurality of classification features. 1. A system comprising:an imager that provides an image of tissue having a glandular epithelial component, the image representing a plurality of medium-scale epithelial components;a processor; and{'claim-text': ['a cell identification component that identifies, for each of a plurality of cells within the image, a representative point to provide a plurality of representative points for each of the plurality of medium-scale epithelial components;', 'a graph constructor that constructs, for each of a subset of the plurality of medium-scale epithelial components, a graph connecting the plurality of representative points;', 'a feature extractor that determines, for each of the subset of medium-scale epithelial components, a plurality of classification features from the graph constructed for the medium-scale epithelial component; and', 'a machine learning model that assigns a clinical parameter to each medium-scale epithelial component according to the extracted plurality of classification features.'], '#text': 'a non-transitory computer readable medium storing instructions executable by the ...

PATHOLOGICAL SECTION IMAGE PROCESSING METHOD AND APPARATUS, SYSTEM, AND STORAGE MEDIUM

This application provides a pathological section image processing method performed by a computer device. The method includes: obtaining stained images of a pathological section after cell membrane staining; determining cell nucleus positions of cancer cells in a stained image under an ifield of view in the n fields of view; generating a cell membrane description result of the stained image under the ifield of view, the cell membrane description result being used for indicating completeness and staining intensity of the cell membrane staining; determining quantities of cells of types in the stained image under the ifield of view according to the cell nucleus positions and the cell membrane description result; and determining an analysis result of the pathological section according to quantities of the cells of types in the stained images under the n fields of view. 1. A pathological section image processing method performed by a computer device , the method comprising:obtaining stained images of a pathological section, wherein each stained image is generated from a respective one of n fields of view of the pathological section under a microscope after cell membrane staining, n being a positive integer;{'sup': 'th', '#text': 'determining cell nucleus positions of cancer cells in a stained image under an ifield of view in then fields of view, i being a positive integer less than or equal to n;'}{'sup': 'th', '#text': 'generating a cell membrane description result of the stained image under the ifield of view, the cell membrane description result being used for indicating completeness and staining intensity of the cell membrane staining;'}{'sup': 'th', '#text': 'determining quantities of cells of types in the stained image under the ifield of view according to the cell nucleus positions and the cell membrane description result; and'}determining an analysis result of the pathological section according to the quantities of the cells of types in the stained images under ...

CHARACTERIZING DISEASE AND TREATMENT RESPONSE WITH QUANTITATIVE VESSEL TORTUOSITY RADIOMICS

Methods, apparatus, and other embodiments associated with classifying a region of tissue using quantified vessel tortuosity are described. One example apparatus includes an image acquisition logic that acquires an image of a region of tissue demonstrating cancerous pathology, a delineation logic that distinguishes nodule tissue within the image from the background of the image, a perinodular zone logic that defines a perinodular zone based on the nodule, a feature extraction logic that extracts a set of features from the image including a set of tortuosity features, a probability logic that computes a probability that the nodule is benign, and a classification logic that classifies the nodule tissue based, at least in part, on the set of features or the probability. A prognosis or treatment plan may be provided based on the classification of the image. 1. A non-transitory computer-readable storage device storing computer executable instructions that when executed by a computer control the computer to perform a method for characterizing a nodule in a region of tissue , the method comprising:accessing an image of a region of tissue demonstrating cancerous pathology;segmenting a lung region from surrounding anatomy in the region of tissue;segmenting a nodule from the lung region by defining a nodule boundary;defining a perinodular zone in the image based, at least in part, on the nodule boundary;generating a three dimensional (3D) segmented vasculature by segmenting a vessel from the perinodular zone;identifying a center line of the 3D segmented vasculature;extracting a set of perinodular tortuosity features based, at least in part, on the center line;computing a probability that the nodule is benign based, at least in part, on the set of perinodular tortuosity features; andcontrolling a computer aided diagnosis (CADx) system to generate a classification of the nodule based, at least in part, on the set of perinodular tortuosity features, or the probability.2. The non- ...

Tool that Analyzes Image Data and Generates and Displays a Confidence Indicator Along with a Cancer Score

A novel cancer scoring tool not only generates a score, but it also generates and confidence number. The tool receives a digital image of tissue of a patient. The tool identifies cell objects in the image and from that determines a first score. The magnitude of this first score is indicative of the severity of cancer in the tissue of the patient. The tool uses an overall false negative rate value and an overall false positive rate value to generate a set of second scores. The rate values are determined from training information. From the second scores, the tool determines the confidence number. The confidence number indicates the confidence the tool has in the first score being correct. The first score and an indication of the confidence number and the digital image are all displayed together on the display of the tool. 1. A method involving a cancer scoring tool , the method comprising:(a) storing a false negative rate value and a false positive rate value for cell objects in the tool;(b) receiving a digital image into the tool, wherein the digital image is an image of a tissue sample of a cancer patient;(c) analyzing the digital image using a computerized cell identification procedure and thereby identifying a plurality of cell objects;(d) storing first information, wherein the first information stored in (d) is indicative of the plurality of cell objects identified in (c);(e) applying a scoring procedure on the first information thereby determining a first score;(f) adding a first number of cell objects to the plurality of cell objects identified in (c), wherein the first number is determined based on the false negative rate value;(g) subtracting a second number of the cell objects identified in (c), wherein the second number is determined based on the false positive rate value;(h) storing second information, wherein the second information stored in (h) is indicative of the plurality of cell objects identified in (c) as added to in (f) and as subtracted from in ( ...

Group sparsity model for image unmixing

Systems and methods described herein relate, among other things, to unmixing more than three stains, while preserving the biological constraints of the biomarkers. Unlimited numbers of markers may be unmixed from a limited-channel image, such as an RGB image, without adding any mathematical complicity to the model. Known co-localization information of different biomarkers within the same tissue section enables defining fixed upper bounds for the number of stains at one pixel. A group sparsity model may be leveraged to explicitly model the fractions of stain contributions from the co-localized biomarkers into one group to yield a least squares solution within the group. A sparse solution may be obtained among the groups to ensure that only a small number of groups with a total number of stains being less than the upper bound are activated.

REDUCED FALSE POSITIVE IDENTIFICATION FOR SPECTROSCOPIC CLASSIFICATION

A device may receive information identifying results of a set of spectroscopic measurements of a training set of known samples and a validation set of known samples. The device may generate a classification model based on the information identifying the results of the set of spectroscopic measurements, wherein the classification model includes at least one class relating to a material of interest for a spectroscopic determination, and wherein the classification model includes a no-match class relating to at least one of at least one material that is not of interest or a baseline spectroscopic measurement. The device may receive information identifying a particular result of a particular spectroscopic measurement of an unknown sample. The device may determine whether the unknown sample is included in the no-match class using the classification model. The device may provide output indicating whether the unknown sample is included in the no-match class. 120-. (canceled)21. A method , comprising:determining, by a device, that an unknown sample is not included in a no-match class relating to at least one of at least one material that is not of interest or a baseline spectroscopic measurement;performing, by the device, one or more spectroscopic determinations based on determining that the unknown sample is not included in the no-match class;determining, by the device and based on performing the one or more spectroscopic determinations, a classification failure or a classification success for the unknown sample; andperforming, by the device, one or more actions based on determining the classification failure or the classification success for the unknown sample.22. The method of claim 21 , further comprising:determining that a spectroscopic measurement of the unknown sample was performed accurately before determining that the unknown sample is not included in the no-match class.23. The method of claim 22 , wherein determining that the spectroscopic measurement of the ...

Method To Combine Brightfield And Fluorescent Channels For Cell Image Segmentation And Morphological Analysis Using Images Obtained From Imaging Flow Cytometer (IFC)

A classifier engine provides cell morphology identification and cell classification in computer-automated systems, methods and diagnostic tools. The classifier engine performs multispectral segmentation of thousands of cellular images acquired by a multispectral imaging flow cytometer. As a function of imaging mode, different ones of the images provide different segmentation masks for cells and subcellular parts. Using the segmentation masks, the classifier engine iteratively optimizes model fitting of different cellular parts. The resulting improved image data has increased accuracy of location of cell parts in an image and enables detection of complex cell morphologies in the image. The classifier engine provides automated ranking and selection of most discriminative shape based features for classifying cell types. 119-. (canceled)20. A system comprising:(i) an imaging flow cytometer configured to acquire a plurality of images of a cell, the plurality of images being acquired across multiple imaging modes and being spatially well aligned with each other; and (A) select a first image from the plurality of images of the cell;', '(B) segment the first image into subcomponents representing parts of the cell;', '(C) segment at least one other image from the plurality of images of the cell into at least one subcomponent representing a part of the cell to generate a subcomponent mask, wherein the at least one other image is of a different imaging mode than the first image;', '(D) spatially correlate the subcomponent mask to the segmented first image;', '(E) apply a graph cut segmentation using the subcomponent mask as a foreground object marker for the segmented first image to generate improved image data having increased location accuracy of the subcomponents of the cell; and', '(F) reprocess the first image using the improved image data generated in (E) to identify cell morphology of the cell in the first image., '(ii) a processor configured to receive the plurality of ...

Tissue Object-Based Machine Learning System for Automated Scoring of Digital Whole Slides

A facility includes systems and methods for providing a learning-based image analysis approach for the automated detection, classification, and counting of objects (e.g., cell nuclei) within digitized pathology tissue slides. The facility trains an object classifier using a plurality of reference sample slides. Subsequently, and in response to receiving a scanned image of a slide containing tissue data, the facility separates the whole slide into a background region and a tissue region using image segmentation techniques. The facility identifies dominant color regions within the tissue data and identifies seed points within those regions using, for example, a radial symmetry based approach. Based at least in part on those seed points, the facility generates a tessellation, each distinct area in the tessellation corresponding to a distinct detected object. These objects are then classified using the previously-trained classifier. The facility uses the classified objects to score slides. 1. A method , performed by a computing system comprising a processor , for whole slide interpretation of digitized images of tissue data , the method comprising:receiving a plurality of digitized images of tissue samples, each tissue sample corresponding to a ground truth slide;receiving, for each of the plurality of digitized images, at least one classification associated with the digitized image;training a tissue-object classifier using the received digitized images of tissue samples;receiving a digitized image of data associated with a first slide, wherein the first slide is not a ground truth slide;automatically identifying tissue within the digitized image of data associated with the first slide;identifying or estimating dominant stain colors within all of the identified tissue in the whole first slide;receiving an indication of a plurality of annotated regions within the identified tissue in the whole first slide; and detecting nuclei seed points within the annotated region,', ' ...

CELL DETECTION USING SEGMENTATION BASED ON NUCLEAR STAINING AND MFISH IMAGES

Detecting cells depicted in an image using RNA segmentation can include obtaining a FISH image of a tissue that depicts multiple cells, obtaining a nuclear stained image of the tissue, and generating a mask that includes multiple areas that each have a position with respect to the tissue by enhancing structures depicted in the FISH image. Edges depicted in the enhanced FISH image are detected to use for the mask, and positions are determined for a first plurality of regions that fit potential nuclei depicted in the nuclear stained image. A second plurality of regions are selected from the first plurality by determining, using the mask, which regions from the first plurality overlap with the position of an area from multiple areas in the mask. Unique nuclei in the tissue are labelled using the second plurality of regions that each indicate a. potential nuclei in the tissue. 1. A computer-implemented method comprising:obtaining a fluorescent in-situ hybridization image of a tissue that depicts a plurality of cells;obtaining a nuclear stained image of the tissue;{'claim-text': ['enhancing, in the fluorescent in-situ hybridization image, structures depicted in the fluorescent in-situ hybridization image; and', 'detecting edges depicted in the enhanced fluorescent in-situ hybridization image to use for the mask;'], '#text': 'generating a mask that includes a plurality of areas that each have a position with respect to the tissue by:'}determining positions for a first plurality of regions that fit potential nuclei depicted in the nuclear stained image;selecting a second plurality of regions from the first plurality of regions by determining, using the mask, which regions from the first plurality of regions overlap with the position of an area from the plurality of areas in the mask; andlabelling unique nuclei in the tissue using the second plurality of regions that each indicate a potential nuclei in the tissue.2. The method of claim 1 , wherein labelling the unique ...

CLASSIFICATION OF CELL NUCLEI

The present invention relates to a system that can be used to accurately classify objects in biological specimens. The user firstly classifies manually an initial set of images, which are used to train a classifier. The classifier then is run on a complete set of images, and outputs not merely the classification but the probability that each image is in a variety of classes. Images are then displayed, sorted not merely by the proposed class but also the likelihood that the image in fact belongs in a proposed alternative class. The user can then reclassify images as required. 1. A method of classifying a set of images of cell nuclei into a plurality of classes , comprising:accepting input classifying each of an initial training set of images taken from the set of images of cell nuclei into a user-selected class among the plurality of classes;calculating a plurality of classification parameters characterising the image and/or the shapes of the individual nuclei of the initial training set of images;training a classification algorithm using the user-selected class and the plurality of classification parameters of the initial training set of images;running the trained classification algorithm on each of the set of images to output a set of probabilities that each of the set of images are in each of the plurality of classes;outputting on a user interface images of cell nuclei of the set of images which the set of probabilities indicates are in a likely class of the plurality of classes and also have a potential alternative class being a different class to the likely class of the plurality of classes;accepting user input to select images out of the output images that should be reclassified to the potential alternative class to obtain a final class for each of the set of images; andretraining the classification algorithm using the final class and the plurality of classification parameters of each of the complete set of images.2. A method according to further comprising: ...

Screening kit and method

Methods are provided for the large-scale high-content analysis of biological samples. In some embodiments, the methods are implemented in a reversed open microwell system that includes an array of open microwells, at least one microchannel, at least one input port and at least one output port In certain embodiments, the reversed open microwell system can be inserted in an automated management system which includes an incubator at controlled temperature, humidity and CO2 levels, a fluid dispensing system, and is capable of phase-contrast and fluorescence image acquisition. 1. A kit comprising:a tip;a microfluidic device comprising a reversed open microwell system, the reversed open microwell system comprising an array of open microwells, at least one microchannel, at least one input port for at least one reagent and/or for one or more biological samples, and at least one output port for the at least one reagent and/or one or more biological samples, said input and output ports being in microfluidic communication with one or more of said at least one microchannels, wherein said at least one microchannel has a cross-section area of micrometric dimensions and is configured to provide a fluid to microwells of said array of open microwells;{'b': 3', '4', '2', '3', '2', '3', '2', '1', '1, 'wherein said tip comprises a proximal portion intended to cooperate with a fluid dispensing system and a distal portion, said proximal portion of generally tubular configuration and said distal portion is open tapered wherein the terminal base of said distal portion has an outer diameter of dimension d, and the upper base of said distal portion has an outer diameter of dimension d, wherein a distance between said upper base and said terminal base of said distal portion is h, the half-opening of a truncated cone formed by said distal portion is (90°-β), and said proximal portion has a height of h; wherein said input port comprises a vertical channel leading into said at least one ...

Systems and methods for classifying activated t cells

Systems and methods for classifying and/or sorting T cells by activation state are disclosed. The system includes a cell classifying pathway, a single-cell autofluorescence image sensor, a processor, and a non-transitory computer-readable memory. The memory is accessible to the processor and has stored thereon a trained convolutional neural network and instructions. The instructions, when executed by the processor, cause the processor to: a) receive the autofluorescence intensity image; b) optionally pre-process the autofluorescence intensity image to produce an adjusted autofluorescence intensity image; c) input the autofluorescence intensity image or the adjusted autofluorescence intensity image into the trained convolutional neural network to produce an activation prediction for the T cell.

MODEL-BASED METHODS AND APPARATUS FOR CLASSIFYING AN INTERFERENT IN SPECIMENS

A model-based method of inspecting a specimen for presence of one or more interferent, such as Hemolysis, Icterus, and/or Lipemia (HI L) is provided. The method includes generating a pixelated image of the specimen in a first color space, determining color components (e.g., an a-value and a b-value) for pixels in the pixelated image, classifying of the pixels as being either liquid or non-liquid, defining one or more liquid regions based upon the pixels classified as liquid, and determining a presence of one or more interferent within the one or more liquid regions. The liquid classification is based upon a liquid classification model. Pixel classification may be based on a trained multiclass classifier. Interference level for the one or more interferent may be provided. Testing apparatus adapted to carry out the method are described, as are other aspects. 1. A method of determining a characteristic of a specimen contained within a sample container , comprising:generating a pixelated image of the specimen;determining color components for pixels in the pixelated image;classifying of pixels in the pixelated image as being liquid or non-liquid;defining one or more liquid regions based upon the classifying of the pixels; anddetermining a presence of one or more interferent within the one or more liquid regions.2. The method of claim 1 , comprising: determining an a-value and a b-value for the pixels in the pixelated image in L*a*b color space.3. The method of claim 1 , wherein the pixelated image is subjected to gamma correction prior to the classifying.4. The method of claim 1 , wherein the pixelated image is compiled from multiple images.5. The method of claim 1 , wherein the classifying of the pixels in the pixelated image as being liquid or non-liquid is based upon a liquid/non-liquid detector.6. The method of claim 1 , wherein the classifying of the pixels in the pixelated image as being liquid or non-liquid is based upon a liquid classification model generated ...

SYSTEMS AND METHODS FOR PROCESSING LOW CONTRAST IMAGES

A method of classifying, with a computer processor, at least one feature of cells from a low contrast, digital image. The method includes generating a contrast-enhanced image by applying a high-pass filter to the low contrast, digital image. The contrast-enhanced image is smoothed with a first low pass filter. A background image, generated from the low contrast, digital image, is subtracted from the smoothed, contrast-enhanced image to form an analysis image. The at least one feature is identified in analysis image. 1. A method of classifying , with a computer processor , at least one feature of each of a plurality of cells from a low contrast , digital image representing the plurality of cells , the method comprising:generating a contrast-enhanced image by filtering the low contrast, digital image with a high-pass filter;smoothing the contrast-enhanced image with a first low-pass filter;generating a background image from the low contrast, digital image;subtracting the background image from the smoothed, contrast-enhanced image to generate an analysis image; andidentifying the at least one feature in the analysis image.2. The method of claim 1 , wherein generating the background image comprises at least one of smoothing the received low contrast claim 1 , digital image with a second low-pass filter claim 1 , clipping negative pixel values to zero claim 1 , and rescaling image contrast.3. The method of claim 1 , wherein the at least one feature in a bacterial infection claim 1 , the method further comprising:detecting a boundary of each of the plurality of cells;distinguishing an uninfected cellular region from an infected cellular region within the detected boundary of each of the plurality of cells;detecting a plurality of peak fluorescence intensities within the infected cellular region; andcomparing each of the plurality of peak fluorescence intensities to an ideal bacteria fluorescence profile; andidentifying at least one of the plurality of peak fluorescent ...

SYSTEMS, METHODS, AND DEVICES FOR ASSESSING MICROBIOTA OF SKIN

Devices, systems, and methods for assessing microbiota of skin are described which include: a skin-covering material with an inner surface conforming in shape to a topography of a skin surface and including a microbe-capture region; an image-capture device to capture a least one image of the inner surface of the skin-covering material and to transform the captured at least one image into a digital output including information associated with at least one property and a spatial distribution of at least one type of microbe bound to the microbe-capture region; and a computing device including circuitry configured to receive the digital output from the image-capture device, compare the at least one property of the at least one type of microbe with a database of reference microbe properties, and generate a digital profile including the at least one property and the spatial distribution of the at least one type of microbe bound to the microbe-capture region of the skin-covering material. 1. A system for assessing microbiota of skin comprising:a skin-covering material having an inner surface and an outer surface, the inner surface substantially conforming in shape to a topography of a skin surface of an individual and including a microbe-capture region;an image-capture device including circuitry to capture at least one image of the inner surface of the skin-covering material and to transform the captured at least one image into a digital output including information associated with at least one property and a spatial distribution of at least one type of microbe bound to the microbe-capture region; and receive the digital output from the image-capture device including the information associated with the at least one property and the spatial distribution of the at least one type of microbe bound to the microbe-capture region;', 'compare the at least one property of the at least one type of microbe with a database of reference microbe properties; and', 'generate a digital ...

MACHINE LEARNING TECHNIQUE TO IDENTIFY GRAINS IN POLYCRYSTALLINE MATERIALS SAMPLES

A method of identifying grains in polycrystalline materials, the method including (a) identifying local crystal structure of the polycrystalline material based on neighbor coordination or pattern recognition machine learning, the local crystal structure including grains and grain boundaries, (b) pre-processing the grains and the grain boundaries using image processing techniques, (c) conducting grain identification using unsupervised machine learning; and (d) refining a resolution of the grain boundaries. 1. A method of identifying grains in polycrystalline materials , the method comprising:(a) identifying local crystal structure of the polycrystalline material based on neighbor coordination or pattern recognition machine learning, the local crystal structure comprising grains and grain boundaries;(b) pre-processing the grains and the grain boundaries using image processing techniques;(c) conducting grain identification using unsupervised machine learning; and(d) refining a resolution of the grain boundaries.2. The method of claim 1 , wherein the step of identifying local crystal structure is based on neighbor coordination and comprises identifying the atomic structure of a first neighbor of the grains and grain boundaries as at least one of hexagonal close packing (hcp) claim 1 , face-centered cubic (fcc) claim 1 , body-centered cubic (bcc) claim 1 , and icosahedral.3. The method of claim 2 , wherein the step of identifying local crystal structure further comprises identifying the atomic structure of a second neighbor of the grains and grain boundaries as at least one of hexagonal close packing (hcp) claim 2 , face-centered cubic (fcc) claim 2 , body-centered cubic (bcc) claim 2 , and icosahedral.4. The method of claim 3 , wherein the step of identifying local crystal structure generates voxels and a number count of:(a) each type of atomic structure for the first neighbor; and(b) each type of atomic structure for the second neighbor.5. The method of claim 1 , ...

METHODS AND APPARATUS FOR DETECTING AN ENTITY IN A BODILY SAMPLE

Apparatus and methods for analyzing a bodily sample are described. A microscope system acquires one or more microscope images of the bodily sample. A computer processor identifies, in the one or more images, at least one element as being a candidate of a given entity. The computer processor extracts, from the one or more images, at least one candidate-informative feature associated with the candidate of the given entity, and at least one sample-informative feature that is indicative of a characteristic of the bodily sample as a whole. The computer processor processes the candidate-informative feature in combination with the sample-informative feature, and generates an output upon the output device, in response thereto. Other applications are also described. 1. Apparatus for analyzing a bodily sample , the apparatus comprising:a microscope system configured to acquire one or more microscope images of the bodily sample;an output device; and identify, in the one or more images, at least one element as being a candidate of a given entity,', 'extract, from the one or more images, at least one candidate-informative feature associated with the candidate of the given entity,', 'extract, from the one or more images, at least one sample-informative feature that is indicative of a characteristic of the bodily sample as a whole,', 'process the candidate-informative feature in combination with the sample-infonnative feature, and', 'generate an output upon the output device, in response thereto., 'at least one computer processor configured to2. The apparatus according to claim I , wherein the at least one computer processor is configured to extract , from the one or more images , at least one candidate-informative feature associated with the candidate by extracting , from the one or more images , at least one candidate-informative feature associated with the candidate , the candidate-informative feature being a feature selected from the group consisting of: a size of the ...

IDENTIFYING REGIONS OF INTEREST FROM WHOLE SLIDE IMAGES

The present application relates generally to identifying regions of interest in images, including but not limited to whole slide image region of interest identification, prioritization, de-duplication, and normalization via interpretable rules, nuclear region counting, point set registration, and histogram specification color normalization. This disclosure describes systems and methods for analyzing and extracting regions of interest from images, for example biomedical images depicting a tissue sample from biopsy or ectomy. Techniques directed to quality control estimation, granular classification, and coarse classification of regions of biomedical images are described herein. Using the described techniques, patches of images corresponding to regions of interest can be extracted and analyzed individually or in parallel to determine pixels correspond to features of interest and pixels that do not. Patches that do not include features of interest, or include disqualifying features, can be disqualified from further analysis. Relevant patches can analyzed and stored with various feature parameters. 1. A method , comprising:obtaining, by a data processing system, a biomedical image derived from a tissue sample, the biomedical image having a first area corresponding to a presence of the tissue sample and a second area corresponding to an absence of the tissue sample;identifying, by the data processing system, from a plurality of sample types, a sample type for the tissue sample based on a comparison of a first size of the first area and a second size of the second area within the biomedical image;generating, by the data processing system, from at least the first area of the biomedical image, a plurality of patches, each patch of the plurality of patches having a plurality of pixels;identifying, by the data processing system, from a plurality of extraction policies corresponding to the plurality of sample types, an extraction policy for the sample type to apply to each ...

Applying Pixelwise Descriptors to a Target Image that are Generated by Segmenting Objects in Other Images

Both pixel-oriented analysis and the more accurate yet slower object-oriented analysis are used to recognize patterns in images of stained cancer tissue. Images of tissue from other patients that are similar to tissue of a target patient are identified using the standard deviation of color in the images. Object-oriented segmentation is then used to segment small portions of the images of the other patients into object exhibiting object characteristics. Pixelwise descriptors associate each pixel in the remainder of the images with object characteristics based on the color of pixels at predetermined offsets from the characterized pixel. Pixels in the image of the target patient are assigned object characteristics without performing the slow segmentation of the image into objects. A pixel heat map is generated from the target image by assigning pixels the color corresponding to the object characteristic that the pixelwise descriptors indicate is most likely associated with each pixel. 147-. (canceled)48. A method comprising:dividing images of tissue of cancer patients into tiles;separating the tiles into clusters, wherein each of the clusters has tiles with similar colors;identifying a matching cluster whose tiles have colors that most closely match the colors of a target image of tissue of a target patient;segmenting the tiles of the matching cluster into objects;classifying the objects into classes;assigning a color to each object class;generating pixelwise descriptors that associate an object class with each pixel of the tiles of the matching cluster based on colors of other pixels at predetermined offsets from the classified pixel; andgenerating a pixel heat map by applying the pixelwise descriptors to each pixel of the target image without segmenting the target image into objects, wherein each pixel of the target image has the color assigned to the object class associated with that pixel.49. The method of claim 48 , wherein the segmenting the tiles of the matching ...

Smart Microscope System for Radiation Biodosimetry

An automated microscope system is described that detects dicentric chromosomes (DCs) in metaphase cells arising from exposure to ionizing radiation. The radiation dose depends on the accuracy of DC detection. Accuracy is increased using image segmentation methods are used to rank high quality cytogenetic images and eliminate suboptimal metaphase cell data in a sample based on novel quality measures. When a sufficient number of high quality images are detected, the microscope system is directed to terminate metaphase image collection for a sample. The microscope system integrates image selection procedures that control an automated digitally controlled microscope with the analysis of acquired metaphase cell images to accurately determine radiation dose. Early termination of image acquisition reduces sample processing time without compromising accuracy. This approach constitutes a reliable and scalable solution that will be essential for analysis of large numbers of potentially exposed individuals. 1. An automated , digitally-controlled microscopy system for improving accuracy of estimation of radiation exposure by biodosimetry in a sample consisting of cells prepared for cytogenetic analysis from a single individual , said method comprising:(i) using a microscope equipped with a computer controlled digital camera to sequentially acquire images of cells containing metaphase chromosomes,(ii) performing a digital analysis of objects in each image to either select or reject the image for radiation exposure determination, based on one or more properties of segmented objects contained therein, said properties including object count, length, width, contour finite difference, centromere density,(iii) directing the microscope system to discontinue collecting images on a sample of metaphase cells after a sufficient number of images have been captured to determine radiation dose,(iv) classifying likely dicentric chromosomes in the set of selected digital images of cells in the ...

Deep learning automated dermatopathology

Techniques for classifying a human cutaneous tissue specimen are presented. The techniques may include obtaining a computer readable image of the human tissue sample and preprocessing the image. The techniques may include applying a trained deep learning model to the image to label each of a plurality of image pixels with at least one probability representing a particular diagnosis, such that a labeled plurality of image pixels is obtained. The techniques can also include applying a trained discriminative classifier to contiguous regions of pixels defined at least in part by the labeled plurality of image pixels to obtain a specimen level diagnosis, where the specimen level diagnosis includes at least one of: basal cell carcinoma, dermal nevus, or seborrheic keratosis. The techniques can include outputting the specimen level diagnosis.

Mobile Microscope Assembly

A microscope assembly can be used during microbiological processes. The microscope assembly includes a lens assembly for magnified imaging of an object range in an imaging plane along an optical path; a sample receiving unit for a sample arranged in the object range; and a camera receiving unit for receiving a camera in a range of the imaging plane the camera adapted to generate a digital image of the sample; where the lens assembly is a ball lens, a halved ball lens, or a lens in the form of a rotational spheroid; and the camera receiving unit is adapted to receive a customary mobile end device equipped with a camera or a camera cooperating with a mobile end device. 1. A microscope assembly , comprising:a lens assembly for magnified imaging of an object range in an imaging plane along an optical path;a sample receiving unit for a sample arranged in said object range;a stamp configured to apply pressure to an object plate or a sample chamber outside of the sample to move the sample towards an optical axis into a focus of the lens assembly, the stamp being disposed above the object plate or the sample chamber outside of the sample; anda camera receiving unit for receiving a camera in a range of said imaging plane said camera adapted to generate a digital image of said sample;whereinsaid lens assembly is a ball lens, a halved ball lens, or a lens in the form of a rotational spheroid; andsaid camera receiving unit is adapted to receive a customary mobile end device equipped with a camera or a camera cooperating with a mobile end device, andwherein said sample receiving unit comprises a bottom part with a flexible and/or deformable material provided thereon, the bottom part with the flexible and/or deformable material being disposed below the object plate or sample chamber outside of the sample and above the camera receiving unit.2. The assembly of claim 1 , wherein said camera receiving unit is slot adapted to receive a smartphone or any other flat mobile end device.3. ...

Multi-Spectral Imaging Including At Least One Common Stain

A method including: providing a sample with M components to be labeled, where M>2; labeling the components with N stains, where N Подробнее

COMPUTER SUPPORTED REVIEW OF TUMORS IN HISTOLOGY IMAGES AND POST OPERATIVE TUMOR MARGIN ASSESSMENT

A computer apparatus and method for identifying and visualizing tumors in a histological image and measuring a tumor margin are provided. A CNN is used to classify pixels in the image according to whether they are determined to relate to non-tumorous tissue, or one or more classes for tumorous tissue. Segmentation is carried out based on the CNN results to generate a mask that marks areas occupied by individual tumors. Summary statistics for each tumor are computed and supplied to a filter which edits the segmentation mask by filtering out tumors deemed to be insignificant. Optionally, the tumors that pass the filter may be ranked according to the summary statistics, for example in order of clinical relevance or by a sensible order of review for a pathologist. A visualization application can then display the histological image having regard to the segmentation mask, summary statistics and/or ranking. Tumor masses extracted by resection are painted with an ink to highlight its surface region. The CNN is trained to distinguish ink and no-ink tissue as well as tumor and no-tumor tissue. The CNN is applied to the histological image to generate an output image whose pixels are assigned to the tissue classes. Tumor margin status of the tissue section is determined by the presence or absence of tumor-and-ink classified pixels. Tumor margin involvement and tumor margin distance are determined by computing additional parameters based on classification-specified inter-pixel distance parameters. 171-. (canceled)72. A computer apparatus for identifying tumors in a histological image , comprising: a hardware processor in communication with the memory, wherein the computer-executable instructions, when executed by the processor, configure the processor to:', 'generate, from an output image, a segmentation mask having areas occupied by individual tumors in the output image marked in the segmentation mask, wherein the output image is generated by a convolution neural network based ...

Learning Pixel Visual Context from Object Characteristics to Generate Rich Semantic Images

Both object-oriented analysis and the faster pixel-oriented analysis are used to recognize patterns in an image of stained tissue. Object-oriented image analysis is used to segment a small portion of the image into object classes. Then the object class to which each pixel in the remainder of the image most probably belongs is determined using decision trees with pixelwise descriptors. The pixels in the remaining image are assigned object classes without segmenting the remainder of the image into objects. After the small portion is segmented into object classes, characteristics of object classes are determined. The pixelwise descriptors describe which pixels are associated with particular object classes by matching the characteristics of object classes to the comparison between pixels at predetermined offsets. A pixel heat map is generated by giving each pixel the color assigned to the object class that the pixelwise descriptors indicate is most probably associated with that pixel. 1. A method comprising:dividing a digital image into tiles;determining a degree of local contrast in each of the tiles;selecting a first plurality of the tiles that exhibits a greatest degree of local contrast;determining an average color of each of the first plurality of tiles;dividing the first plurality of tiles into clusters of tiles with similar colors;selecting a learning tile from each cluster of tiles, wherein each learning tile has the greatest degree of local contrast from among the tiles of the cluster to which the learning tile belongs;segmenting the learning tiles into objects;classifying the objects into classes of objects;associating a color with each class of objects;determining characteristics of the objects that belong to distinct classes of objects;generating pixelwise descriptors that indicate the class of objects to which each pixel most probably belongs;generating a pixel heat map without again segmenting the digital image into objects by applying the pixelwise ...

Microscope Assembly

A microscope assembly can be used during microbiological processes. The microscope assembly includes a lens assembly for magnified imaging of an object range in an imaging plane along an optical path; a sample receiving unit for a sample arranged in the object range; and a camera receiving unit for receiving a camera in a range of the imaging plane the camera adapted to generate a digital image of the sample; where the lens assembly is a ball lens, a halved ball lens, or a lens in the form of a rotational spheroid; and the camera receiving unit is adapted to receive a customary mobile end device equipped with a camera or a camera cooperating with a mobile end device.

METHOD FOR PROVIDING IMAGES OF A TISSUE SECTION

A method for differentiating areas in a series of digital images, the method comprising the steps of: providing a series of images comprising undetermined marker areas; evaluating every image 1for 1≦n≦N according to predetermined selection criteria and defining image marker areas as undetermined marker areas fulfilling the predetermined selection criteria; providing a new image 1; and inserting new image marker areas in the new image 1, said new image marker areas having the same shape and location as image marker areas present in image 1but not in image 1, and said new image marker areas being identifiable in 1by a unique feature. Further, the application discloses a method for visualizing cell populations in tissue sections of a histological sample. Further, the application discloses a method for visualizing three-dimensional distribution of multiple cell populations in a histological sample. 1. A method of differentiating areas in a series of N primary digital images wherein N is an integer>1 , thereby creating a new image , said method comprising:{'sub': n+i', 'n, 'a) providing a series of N primary digital images comprising undetermined marker areas, wherein an image Icomprises at least the same amount of undetermined marker areas as a primary digital image Ifor 2 Подробнее

LIVE CELL VISUALIZATION AND ANALYSIS

Systems and methods are provided for automatically imaging and analyzing cell samples in an incubator. An actuated microscope operates to generate images of samples within wells of a sample container across days, weeks, or months. A plurality of images is generated for each scan of a particular well, and the images within such a scan are used to image and analysis metabolically active cells in the well. Tins analysis includes generating a “range image” by subtracting the minimum intensity value, across the scan, for each pixel from the maximum intensity value. This range image thus emphasizes cells or portions of cells that exhibit changes in activity over a scan period (e.g., neurons, myocytes, cardiomyocytes) while de-emphasizing regions that exhibit consistently high intensities when images (e.g., regions exhibiting a great deal of autofluorescence unrelated to cell activity). 1. A method comprising:capturing a movie of a cell culture vessel; andgenerating a static range image from the movie, wherein the range image is composed of pixels representing the minimum fluorescence intensity subtracted from the maximum fluorescence intensity at each pixel location over a complete scan period.2. The method of claim 1 , further comprising:defining objects by segmenting the range image.3. The method of claim 2 , further comprising:from the objects, deriving average object mean intensities from the complete scan period.4. The method of any of claims 2 , further comprising:from the objects, deriving a pairwise correlation analysis of all object traces over the complete scan period.5. The method of claim 2 , further comprising:from the objects, deriving a mean of all objects mean burst duration from the complete scan period.6. The method of claim 2 , further comprising:from the objects, deriving a strength of each burst of all objects from the complete scan period.7. The method of claim 6 , further comprising:deriving an overall burst strength metric as a mean of all objects ...

COMPOSITIONS AND METHODS FOR ALTERING SECOND MESSENGER SIGNALING

The invention relates to compositions, methods, kits, and assays related to the use and/or exploitation of isomers of cGAMP as well as the structure of the enzyme cGAS. 1. A modulator of cGAS having a structure comprising the following features:{'br': None, 'A-L-B'}wherein:A is or comprises a moiety that fits in the cGAS adenosine binding site;B is or comprises a moiety that fits in the cGAS guanosine binding site; andL is a linker moiety linking A and B in a manner which allows A and B to adopt appropriate interactions to bind cGAS.9. The compound of any one of the preceding claims , wherein each Xis —N—.10. The compound of any one of preceding claims , wherein each Xis —N—.11. The compound of any one of the preceding claims , wherein Xis —NR—.12. The compound of any one of the preceding claims , wherein Xis —O—.13. The compound of any one of the preceding claims , wherein Xis —O—.14. The compound of any one of the preceding claims , wherein Xand Xare —C(R)—.15. The compound of any one of the preceding claims , wherein Xand Xare both oxygen.16. The compound of any one of the preceding claims , wherein Xand Xare both oxygen.17. The compound of any one of the preceding claims , wherein Ris selected from the group consisting of hydrogen , halogen , —OR , —SR , —N(R) , and optionally substituted Caliphatic or Calkoxy-Calkyl.18. The compound of claim 17 , wherein Ris —OH.19. The compound of any one of the preceding claims claim 17 , wherein Ris selected from the group consisting of hydrogen claim 17 , halogen claim 17 , —OR claim 17 , —SR claim 17 , —N(R) claim 17 , and optionally substituted Caliphatic or Calkoxy-Calkyl.20. The compound of claim 19 , wherein Ris —OH.21. The compound of or claim 19 , wherein Ris R.22. The compound of any one of the preceding claims claim 19 , wherein each R claim 19 , R claim 19 , and Ris independently selected from the group consisting of hydrogen claim 19 , halogen claim 19 , and optionally substituted Caliphatic.23. The compound of ...

Digital image classification method for cervical fluid-based cells based on a deep learning detection model

The present invention relates to the field of medical technology, and more particularly, to a digital image classification method for cervical fluid-based cells based on a deep learning detection model. The method comprises the following steps: selecting and labeling positions and categories of abnormal cells or biological pathogens in a digital image of cervical liquid-based smears; performing data normalization processing on the digital image of the cervical liquid-based smears; performing model training to obtain a trained Faster-RCNN model by taking the normalized digital image of the cervical liquid-based smears as an input, and the labeled position and category of each abnormal cell or biological pathogen as an output; and inputting an image to be recognized into the trained model and outputting a classification result. The method provided by the embodiment of the present invention can achieve the following advantages: abnormal cells or biological pathogens in a cervical cytological image are positioned; the abnormal cells or biological pathogens in the cervical cytological image are classified; and slice-level diagnostic recommendations are derived by recognizing the positioned abnormal cells or biological pathogens. 1. A digital image classification method for cervical fluid-based cells based on a deep learning detection model , comprising the following steps:a data preparation phase: selecting and labeling positions and categories of abnormal cells or biological pathogens in a digital image of cervical liquid-based smears;a data processing phase: performing data normalization processing on the digital image of the cervical liquid-based smears;a model training phase: performing model training to obtain a trained Faster-RCNN model by taking the normalized digital image of the cervical liquid-based smears as an input, and the labeled position and category of each abnormal cell or biological pathogen in the digital image of the cervical liquid-based smears as ...

METHOD AND SYSTEM FOR DETECTION AND CLASSIFICATION OF CELLS USING CONVOLUTIONAL NEURAL NETWORKS

An artificial neural network system implemented on a computer for cell segmentation and classification of biological images. It includes a deep convolutional neural network as a feature extraction network, a first branch network connected to the feature extraction network to perform cell segmentation, and a second branch network connected to the feature extraction network to perform cell classification using the cell segmentation map generated by the first branch network. The feature extraction network is a modified VGG network where each convolutional layer uses multiple kernels of different sizes. The second branch network takes feature maps from two levels of the feature extraction network, and has multiple fully connected layers to independently process multiple cropped patches of the feature maps, the cropped patches being located at a centered and multiple shifted positions relative to the cell being classified; a voting method is used to determine the final cell classification. 1. An artificial neural network system implemented on a computer for cell segmentation and classification in biological images , comprising:a convolutional neural network, including a plurality of convolutional layers and a plurality of pooling layers connected in series, configured to receive an input image patch and generate feature maps at each of the plurality of convolutional layers and pooling layers;a first branch network, including at least one convolutional layer, configured to receive feature maps generated by a final pooling layer of the convolutional neural network and generate a cell segmentation map for the input image patch, the cell segmentation map being a binary map including a plurality of cell body regions corresponding to cells within the input image patch; anda second branch network, including N fully connected layers in parallel, N being equal to or greater than 1, the second branch network being configured to receive feature maps from the convolutional neural ...

Method And Apparatus

A computer implemented method is provided for determining an amount of tumor cells in a tissue sample. The method includes obtaining first image data describing an image of a tissue sample at a first resolution, obtaining second image data describing the image at a second resolution, wherein the first resolution is lower than the second resolution, selecting a candidate tumor region from the second image data based on texture data determined from the first image data, identifying a tumor structure in the candidate region of the second image data, and determining a number of cells in the tumor structure based on its area and an estimate of tumor cell area to estimate an amount of tumor cells in the tissue sample. 1. A computer implemented method of determining an amount of tumor cells in a tissue sample , the method comprising:obtaining first image data describing an image of a tissue sample at a first resolution;obtaining second image data describing the image at a second resolution, wherein the first resolution is lower than the second resolution;selecting a candidate tumor region from the second image data based on texture data determined from the first image data;identifying a tumor structure in the candidate region of the second image data;determining a number of cells in the tumor structure based on its area and an estimate of tumor cell area to estimate an amount of tumor cells in the tissue sample.2. The method of further comprising obtaining third image data claim 1 , describing the image at a third resolution claim 1 , wherein the third resolution is higher than the second resolution claim 1 , and identifying a tumor structure in the candidate region of the third image data.3. The method of comprising selecting a subset of image regions corresponding to the identified tumor objects claim 1 , wherein the subset of image regions is smaller in area than the total area of the tumor objects claim 1 , and counting cells in the subset of regions to estimate the ...

METHOD AND SYSTEM FOR AUTOMATICALLY ASSIGNING CLASS LABELS TO OBJECTS

A method of automatically assigning class labels to objects is provided. The method uses object data indicative of a plurality of parameters associated with each object. The method comprises (i) identifying, from the object data or from a lower-dimensional encoding of the object data a plurality of cluster centres in a d-dimensional space, each cluster centre corresponding to one of the class labels; (ii) for respective cluster centres, determining a surrounding region based on a nearest neighbour cluster centre, and assigning the respective class label to objects within the surrounding region; (iii) generating a predictive model using the object data, or the lower-dimensional encoding of the object data and the class labels of the assigned objects; and (iv) assigning class labels to unassigned objects using the predictive model. A corresponding system for performing the above method is also provided. 1. A method of automatically assigning class labels to objects , using object data indicative of a plurality of parameters associated with each object , the method comprising:(i) identifying, from the object data or from a lower-dimensional encoding of the object data, a plurality of cluster centres in a d-dimensional space, each cluster centre corresponding to one of the class labels;(ii) for respective cluster centres, determining a surrounding region based on a nearest neighbor cluster centre, and assigning the respective class label to objects within the surrounding region;(iii) generating a predictive model using the object data, or the lower-dimensional encoding of the object data, and the class labels of the assigned objects; and(iv) assigning class labels to unassigned objects using the predictive model.2. The method according to claim 1 , wherein the cluster centres are identified by: determining a kernel density estimate from the object data; and detecting peaks in the kernel density estimate claim 1 , said peaks corresponding to the cluster centres.3. The ...

ANALYSIS DEVICE, ANALYSIS METHOD, AND PROGRAM

An analysis device includes: a cell image acquisition unit that acquires a plurality of cell images in which a stimulated cell has been captured; a feature value calculation unit that calculates a feature value for each of first and second constituent elements constituting the cell, from the cell images acquired by the cell image acquisition unit; a correlation calculation unit that calculates correlations between first feature values and between second feature values in the first and second constituent elements calculated by the feature value calculation unit; a correlation extraction unit that extracts the correlation between the first feature values by selecting the first feature values with respect to the correlations between the feature values in the first and second constituent elements calculated by the correlation calculation unit; and a display unit that displays the correlation between the first feature values extracted by the correlation extraction unit. 125.-. (canceled)26. An analysis device configured to analyze a correlation between constituent elements from a feature value for constituent element constituting the cell in response to a stimulus , the device comprising:a feature value extraction unit configured to extract a feature value for each of constituent elements from the cell images in which the cell has been captured;a correlation calculation unit configured to calculate correlations between the constituent elements from the feature value extracted by the feature value extraction unit;a region specific unit configured to specify a region which the constituent elements present in the cell from the cell images; anda display unit configured to by selecting the region in the cell, display the correlation of the constituent elements pertaining to the selected region.27. The analysis device according to claim 26 , further comprising:a region display switching unit configured to switch the display of the region of the constituent elements,wherein the ...

Method for Classifying Cells

The disclosure provides example embodiments for automatically or semi-automatically classifying cells in microscopic images of biological samples. These embodiments include methods for selecting training sets for the development of classifier models. The disclosed selection embodiments can allow for the re-training of classifier models using training examples that have been subjected to the same or similar incubation conditions as target samples. These selection embodiments can reduce the amount of human effort required to specify the training examples. The disclosed embodiments also include the classification of individual cells based on metrics determined for the cells using phase contrast imagery and defocused brightfield imagery. These metrics can include size, shape, texture, and intensity-based metrics. These metrics are determined based on segmentation of the underlying imagery. The segmentation is based, in some embodiments, on phase contrast imagery and/or defocused brightfield imagery of biological samples. 1. A method for classification of cells , the method comprising:obtaining a set of images of a plurality of biological samples, wherein the set of images includes at least one image of each sample of the plurality of biological samples;obtaining an indication of a first set of cells within the plurality of biological samples and obtaining an indication of a second set of cells within the plurality of biological samples, wherein the first set of cells is associated with a first condition and the second set of cells is associated with a second condition;based on the set of images, the indication of the first set of cells, and the indication of the second set of cells, determining a first plurality of sets of metrics, wherein the first plurality of sets of metrics comprise a set of metrics for each cell of the first set of cells and a set of metrics for each cell of the second set of cells;based on the first plurality of sets of metrics, using a supervised ...

METHOD AND SYSTEM FOR IMAGE PROCESSING TO DETERMINE BLOOD FLOW

Embodiments include a system for determining cardiovascular information for a patient. The system may include at least one computer system configured to receive patient-specific data regarding a geometry of the patient's heart, and create a three-dimensional model representing at least a portion of the patient's heart based on the patient-specific data. The at least one computer system may be further configured to create a physics-based model relating to a blood flow characteristic of the patient's heart and determine a fractional flow reserve within the patient's heart based on the three-dimensional model and the physics-based model. 1184-. (canceled)185. A medical image diagnostic apparatus for processing medical images , comprising:processing circuitry configured to acquire data of a plurality of Fractional Flow Reserve (FFR) distribution maps comprising at least two time phases regarding a coronary artery; anda display configured to display a representation of the at least one FFR distribution map in phase to the at least one FFR distribution map, wherein the processing circuitry restricts display objects displayed by the display for the representation of the at least one FFR distribution map based on the at least one FFR distribution map.186. The medical image diagnostic apparatus according to claim 185 , wherein the processing circuitry acquires data of a plurality of morphological images comprising the at least two time phases claim 185 , and converts the at least one FFR distribution map into a at least one corresponding color map claim 185 , respectively claim 185 , and the display displays a plurality of superimposed images claim 185 , as the representation of the at least one FFR distribution map claim 185 , obtained by superposing the at least one color map and the plurality of morphological images respectively corresponding in phase.187. The medical image diagnostic apparatus according to claim 186 , wherein the processing circuitry restricts display ...

SYSTEMS AND METHODS FOR PROCESSING IMAGES OF SLIDES TO INFER BIOMARKERS

Systems and methods are disclosed for receiving a target electronic image corresponding to a target specimen, the target specimen comprising a tissue sample of a patient, applying a machine learning system to the target electronic image to identify a region of interest of the target specimen and determine an expression level of, category of, and/or presence of a biomarker in the region of interest, the biomarker comprising at least one from among an epithelial growth factor receptor (EGFR) biomarker and/or a DNA mismatch repair (MMR) deficiency biomarker, the machine learning system having been generated by processing a plurality of training images to predict whether a region of interest is present in the target electronic image, the training images comprising images of human tissue and/or images that are algorithmically generated, and outputting the determined expression level of, category of, and/or presence of the biomarker in the region of interest. 1. A computer-implemented method for analyzing an image corresponding to a specimen , the method comprising:receiving a target electronic image corresponding to a target specimen, the target specimen comprising a tissue sample of a patient;applying a machine learning system to the target electronic image to identify a region of interest of the target specimen and determine an expression level of, category of, and/or presence of a biomarker in the region of interest, the biomarker comprising at least one from among an epithelial growth factor receptor (EGFR) biomarker and/or a DNA mismatch repair (MMR) deficiency biomarker, the machine learning system having been generated by processing a plurality of training images to predict whether a region of interest is present in the target electronic image, the training images comprising images of human tissue and/or images that are algorithmically generated; andoutputting the determined expression level of, category of, and/or presence of the biomarker in the region of ...

Biological tissue image processing system, and machine learning method

A current observation area is determined exploratorily from among a plurality of candidate areas, on the basis of a plurality of observed areas in a biological tissue. A plurality of reference images obtained by means of low-magnification observation of the biological tissue are utilized at this time. A learning image is acquired by means of high-magnification observation of the determined current observation area. A plurality of convolution filters included in an estimator can be utilized to evaluate the plurality of candidate areas.

APPARATUS FOR AND METHOD OF PROCESSING IMAGE AND STORAGE MEDIUM

An image processing apparatus displays an object adopted and an object not adopted by an adoption/non-adoption process in a distinguishable manner. A user designates an object whose adoption/non-adoption result is desired to be reversed among the objects displayed by the image processing apparatus. The image processing apparatus changes an allowable range stored in a storage part so that the adoption/non-adoption result of the designated object is reversed. That is, the user views the adoption/non-adoption result of the objects to change the allowable range of a parameter so that the adoption/non-adoption result becomes proper. This makes the allowable range of the parameter for use in the adoption/non-adoption process proper with ease. 1. An image processing apparatus comprising:a storage part for storing therein allowable ranges of parameters for use in an adoption/non-adoption process for classifying an object included in an image as adopted or as not adopted;an object extraction part for extracting a plurality of objects from said image;an adoption/non-adoption processing part for determining that an object having said parameters within said allowable ranges is adopted and that an object having at least one of said parameters outside a corresponding one of said allowable ranges is not adopted among said plurality of objects;a display output part for displaying the object adopted and the object not adopted by said adoption/non-adoption processing part in a distinguishable manner;an input designation part for accepting the designation of an object whose adoption/non-adoption result is desired to be reversed among the objects displayed by said display output part; anda parameter changing part for changing one of said allowable ranges stored in said storage part so that the adoption/non-adoption result of the object designated by said input designation part is reversed.2. The image processing apparatus according to claim 1 , whereinsaid parameter changing part ...

Apparatus, Method, and System for Image-Based Human Embryo Cell Classification

Apparatuses, methods, and systems for automated cell classification, embryo ranking, and/or embryo categorization are provided. An apparatus includes a classification module configured to apply classifiers to images of one or more cells to determine, for each image, a classification probability associated with each classifier. Each classifier is associated with a distinct first number of cells, and is configured to determine the classification probability for each image based on cell features including one or more machine learned cell features. The classification probability indicates an estimated likelihood that the distinct first number of cells is shown in each image. The classification module is further configured to classify each image as showing a second number of cells based on the distinct first number of cells and the classification probabilities associated therewith. The classification module is implemented in at least one of a memory or a processing device. 1 each first classifier is associated with a distinct first number of cells, and determines the first classification probability for the each image based on a plurality of cell features including one or more machine learned cell features; and', 'classifying each image as showing a second number of cells based on the distinct first number of cells associated with the each first classifier and the plurality of first classification probabilities associated therewith.', 'the first classification probability indicates a first estimated likelihood that the distinct first number of cells associated with the each first classifier is shown in the each image, the each of the plurality of images thereby having a plurality of the first classification probabilities associated therewith; and'}], 'applying a plurality of first classifiers to each of a plurality of images of one or more cells to determine, for each image, a first classification probability associated with each first classifier, wherein. A method for ...

GRAPHICAL USER INTERFACE FOR ANALYSIS OF RED BLOOD CELLS

Methods and systems for generating a graphical user interface for analysis of red blood cells. The method generates a first view of the graphical user interface by highlighting red blood cells in an image that are classified into at least one group indicated by a user input. The method also generates a second view by extracting individual red blood cells being classified into the at least one group indicated by the user input from the image of red blood cells and displaying them group-wise. 1. A method for displaying red blood cells in a graphical user interface for analysis of red blood cells , comprising:receiving an image depicting a sample of red blood cells; a segmentation and a position of the red blood cell in the image, and', 'a classification of the red blood cell into at least one group with respect to at least one property of red blood cells;, 'receiving red blood cell data comprising, for each red blood cell in the image,'}receiving a first user input indicating at least one group into which the red blood cells in the image have been classified; finding all red blood cells in the red blood cell data which are classified into the at least one group indicated by the first user input,', 'highlighting the found red blood cells in the image, and', 'displaying the image with highlighted red blood cells in the first view;, 'generating a first view of the graphical user interface by finding all red blood cells in the red blood cell data that are classified into the group,', 'extracting each of the red blood cells that are classified into the group from the image by extracting image data from the image falling within the segmentation of each of the red blood cells that are classified into the group, and', 'displaying the extracted red blood cells in a sub-view of the second view that corresponds to the group., 'generating a second view of the graphical user interface by, for each of the at least one group indicated by the first user input2. The method of claim 1 ...

METHOD AND APPARATUS FOR TISSUE RECOGNITION

A computer implemented image processing method is disclosed. The method comprises: obtaining microscope image data defining a microscope slide image of a haematoxylin and eosin stained tissue sample, wherein the microscope slide image data comprises a plurality of image pixels; obtaining descriptor data indicating a type of tissue from which the tissue sample originates; selecting, based on the descriptor data, an image operation configured to transform the image data; applying the selected image operation to the image data to identify a number of discrete spatial regions of the image; selecting, from a data store, a set of quantitative image metrics wherein the quantitative image metrics are selected based on the descriptor data, determining, for each discrete spatial region, a sample region data value for each of the set of quantitative image metrics based on the subset of image data associated with the or each discrete spatial region, using the descriptor data to select, from the data store, at least one comparator set of tissue model data values, wherein each comparator set is associated with a different corresponding comparator tissue structure and each comparator set comprises data values of the set of quantitative image metrics for the corresponding comparator tissue structure; comparing the sample region data value for each discrete region with the at least one comparator set; and in the event that the sample region data value for the or each discrete spatial region matches the comparator set providing a map of the image data indicating that the discrete spatial region comprises the matching comparator tissue structure. 1. A computer implemented image processing method comprising:obtaining microscope image data defining a microscope slide image of a haematoxylin and eosin stained tissue sample, wherein the microscope slide image data comprises a plurality of image pixels;obtaining descriptor data indicating a type of tissue from which the tissue sample ...

IMAGE PROCESSING DEVICE, OBSERVATION DEVICE, AND PROGRAM

An image processing device includes: an associating unit configured to associate information on an identification element used to identify a cell in imaged cell information with information on a non-identification element serving as an element of the cell which is not the identification element on the basis of the information on the identification element and information of the non-identification element; and a status determination unit configured to determine a cell status on the basis of the information on the identification element and the information on the non-identification element associated together by the linking unit. 1. An image processing device comprising:a linking unit configured to associate information on an identification element used to identify a cell in imaged cell information with information on a non-identification element which is an element of the cell and not the identification element;a status determination unit configured to determine a status of the cell on the basis of the information on the identification element and the information on the non-identification element associated together by the linking unit; anda mask generation unit configured to generate an image mask corresponding to the information on the non-identification element,wherein the mask generation unit generates the image mask by performing dilation processing which dilates an image of the non-identification element by a predetermined number of pixels,the linking unit links the identification element and the non-identification element corresponding to the image mask, andthe status determination unit determine a status of the cell on the basis of a type of the image mask linked with an image of the identification element.2. The image processing device according to claim 1 , wherein the linking unit associates the identification element and the non-identification element depending on whether or not the image of the identification element is included in the image mask.34-. ( ...

IMAGE PROCESSING DEVICE, OBSERVATION DEVICE, AND PROGRAM

An image processing device includes an image processing unit that performs image processing on an observed image in which a cell is imaged and an image processing method selector that is configured to determine an observed image processing method for analyzing the imaged cell on the basis of information of a processed image obtained through image processing of the image processing unit. 1. An image processing device comprising:an image processing unit configured to perform image processing of reducing resolution on an observed image in which a cell is imaged; andan image processing method selector configured to determine an observed image processing method for analyzing the imaged cell on the basis of information of a processed image obtained through image processing of the image processing unit.2. The image processing device according to claim 1 , wherein the image processing of reducing resolution is performed through expansion processing and contraction processing.3. The image processing device according to claim 2 , wherein the image processing unit is configured to determine a size of a processing unit image of the image processing on the basis of a state of the observed image.4. The image processing device according to claim 3 , wherein the state of the observed image includes a form of a structure of the cell.5. The image processing device according to claim 4 , wherein the form of the structure of the cell includes a form of a projection of the cell.6. The image processing device according to claim 1 , wherein the information of the processed image includes a pixel value of the processed image.7. The image processing device according to claim 1 , wherein the information of the processed image includes a statistical quantity of the pixel value.8. The image processing device according to claim 1 , further comprising a cell image analyzer configured to analyze an image of a cell that is imaged in the observed image on the basis of the image processing method ...

HISTOLOGY RECOGNITION TO AUTOMATICALLY SCORE AND QUANTIFY CANCER GRADES AND INDIVIDUAL USER DIGITAL WHOLE HISTOLOGICAL IMAGING DEVICE

Digital pathology is the concept of capturing digital images from glass microscope slides in order to record, visualize, analyze, manage, report, share and diagnose pathology specimens. The present disclosure is directed to a desktop slide scanner, which enables pathologists to scan slides at a touch of a button. Included is a workflow for reliable imaging, diagnosis, quantification, management, and sharing of a digital pathology library. Also disclosed herein is an analysis framework that provides for pattern recognition of biological samples represented as digital images to automatically quantitatively score normal cell parameters against disease state parameters. The framework provides a pathologist with an opportunity to see what the algorithm is scoring, and simply agree, or edit the result. This framework offers a new tool to enhance the precision of the current standard of care. 1. A desktop slide scanner , comprising:a frame that defines a seat into which a portable device is disposed and a detector eye that aligns an embedded imaging device of the portable device with the frame;an imaging portal mounted to the frame that includes a lens having a predetermined magnification; anda light-tight enclosure that is received within the frame, the light-tight enclosure further including a moveable a slide tray having a slide seat adapted to receive a slide,wherein the slide tray is moved within the light-tight enclosure and imaged by the imaging device, and wherein the images are magnified at the predetermined magnification by the lens within the imaging portal.2. The desktop slide scanner of claim 1 , further comprising a seal that forms around the imaging device of the portable device to exclude light from entering an imaging field during use.3. The desktop slide scanner of claim 1 , further comprising a network connection provided to the portable device to transfer images to a remote repository.4. The desktop slide scanner of claim 1 , wherein the slide tray is ...

DENDRITIC STRUCTURES AND TAGS

The disclosure features dendritic tags, and methods and systems for fabricating and using such tags. The methods can include obtaining at least one image of a dendritic tag attached to an article, analyzing the at least one image to identify a set of features associated with the dendritic tag, and comparing the set of features to stored information to identify the article. 1. A method , comprising:obtaining an image of a dendritic structure in a tag attached to an article or to a container comprising the article;segmenting the image into a plurality of regions and assigning a binary value to each region based on the portion of the dendritic structure in each region;constructing a binary spatial representation of the dendritic structure based on the plurality of regions; andcomparing the binary spatial representation to reference information for a plurality of dendritic structures to determine information about the article.2. The method of claim 1 , wherein the comparing comprises:(a) segmenting the image into a first plurality of regions, assigning a binary value to each one of the first plurality of regions, constructing a first binary spatial representation of the dendritic structure based on the first plurality of regions, and identifying reference information corresponding to a first subset of the plurality of dendritic structures that correspond to the first binary spatial representation; and(b) segmenting the image into a second plurality of regions, assigning a binary value to each one of the second plurality of regions, constructing a second binary spatial representation of the dendritic structure based on the second plurality of regions, and identifying reference information corresponding to a second subset of the plurality of dendritic structures that correspond to the second binary spatial representation, wherein members of the second plurality of regions are smaller than members of the first plurality of regions, and wherein the second subset is selected ...

PROCESSING OF IMAGES FROM FLUORESCENT IN-SITU HYBRIDIZATION USING MULTIPLEXED FLUORESCENT SWITCHING

A computer program product includes instructions to cause one or more computers to obtain first and second images in the same color channel in the same hybridization step from a fluorescent in-situ hybridization imaging system, and to register the first image and the second image. One or more pixels having a first intensity exceeding a first threshold in the first image and having a second intensity exceeding a second threshold in the second image are identified and classified as activated in a first readout call, and one or more pixels having the first intensity exceeding the first threshold in the first image and the second intensity below the second threshold in the second image are identified and classified as activated in a second readout call. 1. A system for fluorescent in-situ hybridization imaging , comprising:a flow cell to contain a sample to be exposed to probes having fluorophores in a reagent;a valve to control flow from one of a plurality of reagent sources the flow cell;a pump to cause fluid flow through the flow cell;a microscope including a variable frequency excitation light source and a camera positioned to receive emitted light from the sample;a motor to cause relative lateral motion between the flow cell and the microscope; and cause the valve to sequentially couple the flow cell to a plurality of different reagent sources to expose the sample to a plurality of different reagents, the plurality of different reagents comprising at least one type of modifiable readout probe,', 'for each type of modifiable readout probe in the plurality of readout probes, modify the plurality of readout probes of the respective type such that the plurality of readout probes of the respective type proceed through a sequence of states, and', 'for each state in the sequence of states, obtain an image., 'a control system configured to, as nested loops,'}2. The system of claim 1 , wherein the control system is configured to claim 1 , as nested loops claim 1 ,for each ...

IDENTIFYING THE QUALITY OF THE CELL IMAGES ACQUIRED WITH DIGITAL HOLOGRAPHIC MICROSCOPY USING CONVOLUTIONAL NEURAL NETWORKS

A system for performing adaptive focusing of a microscopy device comprises a microscopy device configured to acquire microscopy images depicting cells and one or more processors executing instructions for performing a method that includes extracting pixels from the microscopy images. Each set of pixels corresponds to an independent cell. The method further includes using a trained classifier to assign one of a plurality of image quality labels to each set of pixels indicating the degree to which the independent cell is in focus. If the image quality labels corresponding to the sets of pixels indicate that the cells are out of focus, a focal length adjustment for adjusting focus of the microscopy device is determined using a trained machine learning model. Then, executable instructions are sent to the microscopy device to perform the focal length adjustment. 1. A computer-implemented method for detecting out of focus microscopy images , the method comprising:acquiring a plurality of microscopy images depicting cells;extracting one or more sets of pixels from the plurality of microscopy images, wherein each set of pixels corresponds to an independent cell;assigning one of a plurality of image quality labels to each set of pixels indicating the degree to which the independent cell is in focus;training a classifier to classify the set of pixels into the plurality of image quality labels, wherein the classifier is configured according to a multi-layer architecture and the training results in determination of a plurality of weights for connecting layers in the multi-layer architecture;creating a deployment of the classifier based on the multi-layer architecture, the plurality of weights, and the plurality of image quality labels.2. The method of claim 1 , wherein the classifier is a convolutional neural network.3. The method of claim 1 , wherein the microscopy images are synthetic images generated by:using a deep convolutional general adversarial network (DCGAN) to ...

IDENTIFICATION OF INFLAMMATION IN TISSUE IMAGES

Systems and methods are provided for identifying markers for inflammation in a tissue image. The tissue image is captured as an image of a histology slide. Subcellular structures in the tissue image are segmented via a first automated process to identify at least one variety of immune cells within the image. Glands and vilii are identified within the tissue image via a second automated process. Neutrophils are identified within the tissue image via a third automated process. An output representing the identified glands, villi, neutrophils, and other immune cells is provided to a human operator. 1. A method of identifying markers for inflammation in a tissue image comprising:capturing the tissue image as an image of a histology slide;segmenting subcellular structures in the tissue image, via a first automated process, to identify at least one variety of immune cells within the tissue image;identifying glands and villi within the tissue image via a second automated process;identifying neutrophils within the tissue image via a third automated process; andproviding an output representing the identified at least one variety of immune cells, the identified glands, the identified villi, and the identified neutrophils to a human operator.2. The method of claim 1 , wherein segmenting subcellular structures in the tissue image via the first automated process comprises:constructing a layered graph model, comprising a plurality of vertices and a plurality of edges according to at least one constraint;determining respective weights for each of the plurality of vertices and the plurality of edges according to at least one photometric prior; anddetermining a set of boundaries, represented by a path having a lowest total energy in the layered graph model, for a nucleus and a cytoplasm of a cell represented by the layered graph model.3. The method of claim 2 , further comprising identifying the cell as one of a plasma cell claim 2 , a lymphocyte claim 2 , and an eosinophil from the ...