МОНИТОРИНГ КАЧЕСТВА ЗЕРНА

Изобретение относится к сельскому хозяйству, в частности к улучшению сбора зерна. Способ управления работой уборочной машины предусматривает захват изображения насыпи зерна, переносимого уборочной машиной; применение выделителя признаков к изображению, чтобы определять признак насыпи зерна в изображении; для каждого из множества различных местоположений выборки в изображении определяют балл классификации на основе признака насыпи зерна в местоположении выборки для представления классификации материала в местоположении выборки; вывод сигнала, указывающего на качество насыпи зерна на изображения на основе совокупности баллов классификации различных местоположений выборки; автоматическое регулирование рабочих настроек уборочной машины на основе сигнала при прохождении уборочной машиной поля и при сборе насыпи зерна уборочной машиной. Способ управления работой уборочной машины осуществляется компьютерно-читаемым носителем с программой для работы уборочной машины. Предлагаемый способ управления ...

ОБСЛЕДУЮЩЕЕ УСТРОЙСТВО ДЛЯ ОБРАБОТКИ И АНАЛИЗА ИЗОБРАЖЕНИЯ

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

Computer implemented cell component e.g. cell core, analyzing method for cancer patient, involves marking cell components under utilization of marking procedure, and counting marked cell components under utilization of data network

The method involves specifying a class network and a processing hierarchy, and detecting pixel values of an image containing cell components e.g. cell core, of a tissue e.g. biopsy tissue (10). The determined cell components are marked under utilization of a marking procedure e.g. fluorescence in-situ hybridization, and a data network is generated based on the class network and processing hierarchy. The marked cell components are counted under utilization of the data network, and a volume of the cell components is determined by using the data network. An independent claim is also included for a computer program product comprising instructions to perform a method for analyzing and counting a fluorescence signal.

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 ...

Verfahren und Vorrichtung zur Segmentierung einer digitalen Abbildung von Zellen

Die Erfindung betrifft ein Verfahren und eine Vorrichtung zur Segmentierung einer digitalen Abbildung (22) von biologischen Zellen (2, 3), ein Verfahren und eine Vorrichtung zur Analyse des dynamischen Verhaltens biologischer Zellen (2, 3) sowie ein Verfahren und eine Vorrichtung zur Visualisierung des dynamischen Verhaltens biologischer Zellen (2, 3). Um ein Verfahren zur Segmentierung vorzustellen, welches nur geringe technische Anforderungen hat, keine spezielle Präparation oder Manipulation an den zu beobachtenden Zellen benötigt und möglichst wenige Annahmen über Zelleigenschaften voraussetzt, wird vorgeschlagen, dass es die folgenden Schritte aufweist: Bestimmen (41) eines maximalen Gradienten je Bildpunkt der Abbildung als das Maximum der Differenzen zwischen einem Bildwert des Bildpunktes und den entsprechenden Bildwerten aller oder ausgewählter benachbarter Bildpunkte, Bestimmen (42) eines Segmentierungsschwellwertes anhand der Häufigkeit der maximalen Gradienten, Klassifizieren ...

Measurement data selection method for biometric device, light exit position determination method for biometric device, and biometric device

Identifying a Tissue Boundary of a Tumour Region of a Tissue Sample

Improvements in and relating to imaging of the eye

Improvements in and relating to imaging of the eye

A method of determining a measurement of at least one cell type in an eye, comprising: obtaining a map of the cell type in the eye which may be in 2D or 3D and may be a cell type density or distribution map, obtaining a representation of the retina of the eye and matching the eye cell type map to the representation of the retina of the eye. A region of interest is then defined on the representation of the retina of the eye and its size calculated. The matched eye cell type map and the size of the region of interest are then used to determine a measure of the cell type in the region of interest of the retina of the eye.

Apparatus and method for image processing of specimen images for use in computer analysis thereof

Apparatus and method

A computer implemented image processing method of identifying a tissue boundary of a tumour region of a tissue sample 2, the tissue sample containing non-tumour regions and at least one tumour region, to enable excision of at least a portion of a tumour region from the tissue sample by cutting along the tissue boundary, the method comprises: obtaining an image of a tissue section of the tissue sample 4 such as an histology slide 10; identifying at least one image property of the image; comparing the image property with classification data; based on the comparison, classifying a region of the image as a tumour region representing a tumour region in the tissue sample or a non-tumour region representing a non-tumour region in the tissue sample; and if the region of the image is classified as a tumour region, identifying a boundary of the region of the image; and using the boundary to identify a tissue boundary of the tumour region of the tissue sample represented by the region of the image ...

Optimizing the initialization and convergence of active contours for segmentation of cell nuclei in histological sections

VERFAHREN UND ANORDNUNG ZUR UNTERSUCHUNG VON ZELLEN

PROCEDURE FOR THE QUANTIFICATION OF AN AT THE BASIS LYING CHARACTERISTICS OF A QUANTITY OF SAMPLES

VERFAHREN UND EINRICHTUNG ZUR SEGMENTIERUNG VON BEREICHEN

PROCEDURE AND DEVICE FOR DETERMINING AN OUTLINE AND A CENTER OF AN OBJECT

IDENTIFICATION OF CELLS DURING KINETIC TEST SERIES

PROCEDURE FOR OBJECTIVES THE DETERMINATION OF THE DEGREE OF DIFFICULTY OF SCARS

Observation device

... [Problem] To provide an observation device with which it is possible to obtain an excellent optical image by keeping contrast constant, while rendering unnecessary an operation for adjusting the distance from a phase-contrast microscope to an object to be observed. [Solution] This observation device 10 comprises a microscope 12. The microscope 12 comprises: an illumination light source 50; an illumination optical system 51; and an observation optical system 52 that forms an optical image of a to-be-observed object 28 from reflection light 62 obtained by illumination light 61 being reflected by the to-be-observed object 28. The observation optical system 52 comprises a phase plate 57 which, among the reflection light 62, changes the phase of direct reflection light 62A reflected by a reflection surface 49a. Both the illumination optical system 51 and the observation optical system 52 are positioned below the to-be-observed object 28. The observation device 10 comprises: an imaging element ...

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 ...

Identifying and measuring reticulocytes

Methods and systems for identifying reticulocytes in a blood sample deposited on a substrate include: illuminating the sample with incident light at two different wavelengths, obtaining a two-dimensional image of the sample corresponding to a first one of the wavelengths, and obtaining a two-dimensional image of the sample corresponding to a second one of the wavelengths; analyzing the images to identify a set of representative red blood cells; determining an area of each of the red blood cells in the set; determining a color value of each of the red blood cells in the set; and, for each one of the red blood cells in the set, identifying the red blood cell as a reticulocyte if the area of the red blood cell exceeds an area cutoff value and the color value of the red blood cell is less than a color cutoff value.

METHOD AND APPARATUS FOR MULTIPLE LABELING DETECTION AND EVALUAT ION OF A PLURALITY OF PARTICLES

System and method for measurement of a response of localized cellular compartments

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.

Overlapped layers in 3D capture

... -43 OVERLAPPED LAYERS IN 3D CAPTURE A method of registering a plurality of images of a three dimensional specimen captured by a microscope comprises capturing a first set of images on a first capture plane (zi, Layer i) of the 5 specimen including two images (721,722) having a first area (727; 810,820) of overlap. A second set of images on a second capture plane (z 2, Layer i+1) of the specimen are captured the second capture plane is parallel to the first capture plane and including two images (723,724) having a second area (728) of overlap that is offset from the first area of overlap in a direction along the capture planes so as to include in the second area of overlap at least one first alignable 10 image feature not present in the first area of overlap. The two images in the second set are aligned using the at least one first alignable image feature in the second area of overlap, and at least the two images in the first set are aligned using the alignment of the two images of the second ...

Marker quantitation in single cells in tissue sections

Improved assays incorporating single-cell based image analyses that enable quantitation of expression of individual cellular proteins and heterogeneity in terms of individual cellular protein molecule numbers per cell at the single cell level and mapped across sections of clinical tissue samples are disclosed.

Biological fluid analysis system and method

A biological fluid analysis system and method for measuring optical characteristics of a sample of biological fluid using a commercially available portable computing device having a camera, such as a smart phone. The system includes a scope, a case that attaches to the portable computing device, and a software application that runs on the portable computing device. Some embodiments of the invention include a sample slide having a viewing chamber that can be filled with a biological fluid to be analyzed by the biological fluid analysis system. The system may be adapted to analyze cow's milk to estimate the number of somatic cells per unit volume contained in the milk using a reagent that stains the somatic cells so that they will fluoresce when excited by light with a particular wavelength, with the light source in the scope being adapted to generate light of that particular wavelength.

Visual device

SYSTEMS AND METHODS FOR AUTOMATED DIAGNOSIS AND GRADING OF TISSUE IMAGES

CHROMATIN SEGMENTATION

A method of segmenting chromatin particles in a nucleus of a cell by locating regional minima in an image, computing a zone of influence (ZOI) around each regional minimum, and segmenting a single chromatin blob within each ZOI using a region growing procedure. The method can be used as the basis of a method of qualitatively characterizing the distribution of nuclear chromatin by computing features for individual chromatin particles. Chromatin features can be synthesized from the features of individual particles and particle features can be synthesized into nucleus features and slide features. The method is useful for detecting malignancy associated changes and changes during neoplasia. The method may also be used more generally to assess chromatin patterns in living cells during the cell life cycle. This makes it possible to measure alternations in the evolving patterns that result from pathological or environmental influences.

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.

METHOD AND SYSTEM FOR QUANTIFYING BIOMARKER OF A TISSUE

The present disclosure relates to a method and a system for quantifying a biomarker of a biological tissue. Two spectral sub-intervals are determined from a plurality of images of the tissue acquired at discrete wavelengths within a main wavelength interval using an imaging sensor in a manner such that the combination of the image data in the at least two spectral sub-intervals are correlated with a clinical variable of interest. The tissue is illuminated using one or more light sources with wavelengths within the at least two spectral sub-intervals. A measurement is of the light reflected by the tissue is acquired using an imaging sensor; and a measure of the biomarker of the tissue is calculated using the acquired measurement. The main wavelength interval is broader than each of the at least two spectral sub-intervals and at least one spectral confounder causing a spectral variability of the acquired image is present in the tissue.

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.

SEGMENTING 3D INTRACELLULAR STRUCTURES IN MICROSCOPY IMAGES USING AN ITERATIVE DEEP LEARNING WORKFLOW THAT INCORPORATES HUMAN CONTRIBUTIONS

A facility for identifying the boundaries of 3-dimensional structures in 3-dimensional images is described. For each of multiple 3-dimensional images, the facility receives results of a first attempt to identify boundaries of structures in the 3-dimensional image, and causes the results of the first attempt to be presented to a person. For each of a number of 3-dimensional images, the facility receives input generated by the person providing feedback on the results of the first attempt. The facility then uses the following to train a deep-learning network to identify boundaries of 3-dimensional structures in 3-dimensional images: at least a portion of the plurality of 3-dimensional images, at least a portion of the received results, and at least a portion of provided feedback.

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.

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.

METHODS OF DETECTING AND MONITORING CANCER USING 3D ANALYSIS OF CENTROMERES

The present application relates to a method of detecting and monitoring cancer or precancer in a cell using three-dimensional analysis to assess centromere organization. In addition, the application relates to a method and system for characterizing the 3D organization of centromeres.

SYSTEMS AND METHODS FOR ADAPTIVE HISTOPATHOLOGY IMAGE UNMIXING

The present invention relates to systems and methods for adaptively optimizing broadband reference spectra for a multi-spectral image or adaptively optimizing reference colors for a bright-field image. The methods and systems of the present invention involve optimization techniques that are based on structures detected in an unmixed channel of the image, and involves detecting and segmenting structures from a channel, updating a reference matrix with signals estimated from the structures, subsequently unmixing the image using the updated reference matrix, and iteratively repeating the process until an optimized reference matrix is achieved.

METHODS AND SYSTEMS FOR IMAGE DATA PROCESSING

Methods, storage mediums, and systems for image data processing are provided. Embodiments for the methods, storage mediums, and systems include configurations to perform one or more of the following steps: background signal measurement, particle identification using classification dye emission and cluster rejection, inter-image alignment, inter-image particle correlation, fluorescence integration of reporter emission, and image plane normalization.

METHODS AND SYSTEMS FOR IMAGE DATA PROCESSING

Methods, storage mediums, and systems for image data processing are provided. Embodiments for the methods, storage mediums, and systems include configurations to perform one or more of the following steps: background signal measurement, particle identification using classification dye emission and cluster rejection, inter- image alignment, inter-image particle correlation, fluorescence integration of reporter emission, and image plane normalization.

DIAGNOSIS ASSISTING DEVICE, AND IMAGE PROCESSING METHOD IN DIAGNOSIS ASSISTING DEVICE

Provided are a diagnosis assisting device that facilitates a user to grasp a difference of an affected area to perform a highly precise diagnosis assistance, an image processing method in the diagnosis assisting device, and a program. An image processing method in a diagnosis assisting device that diagnoses lesions from a picked-up image includes (A) performing an image processing on the picked-up image. In (A), a peripheral area other than a diagnosis area that has a high probability as diseases in the picked-up image is set to be a measuring area when an image correction is performed.

IMAGE ANALYSIS TECHNIQUES FOR DIAGNOSING DISEASES

Techniques for assessing a tissue condition and diagnosing, assessing the prognosis of, or the risk for pathological conditions are disclosed. The technique may comprise an image acquiring module adapted to receive an image comprising at least a portion of animal or human tissue, a delineation module adapted to indicate an analysis zone in said acquired image, a feature extraction module adapted to extract quantitative information from said analysis zone and a machine learning module adapted to receive said extracted information and apply at least one detection algorithm to assess a condition of said tissue. The feature extractor module may comprise at least a rotation compensation module to compensate for the rotation of the analysis zone.

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.

METHODS AND SYSTEMS FOR AUTOMATED SEGMENTATION OF DENSE CELL POPULATIONS

Systems and methods for segmenting images comprising cells, wherein the images comprise a plurality of pixels; one or more three dimensional (3D) clusters of cells are identified in the images, and the 3D clusters of cells are automatically segmented into individual cells using one or more models.

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.

METHOD FOR VISUALIZING BLOOD AND BLOOD-LIKELIHOOD IN VASCULAR IMAGES

Computer-implemented methods for use in improving the diagnostic quality of images, including intravascular ultrasound (IVUS) images, are disclosed. The methods include using a non-linear, probabilistic classifier algorithm to analyze a plurality of spatiotemporal features of RF backscatter and to produce a blood likelihood map or blood probability map that corresponds to the original IVUS image. The methods disclosed herein allow for visualizing both static and dynamic characteristic of a vessel either by producing a transparency modulated color overlay of the blood likelihood map without altering the underlying IVUS image or by processing the IVUS image based upon the blood likelihood map to better distinguish between static and dynamic components of the vessel.

MARKER QUANTITATION IN SINGLE CELLS IN TISSUE SECTIONS

Improved assays incorporating single-cell based image analyses that enable quantitation of expression of individual cellular proteins and heterogeneity in terms of individual cellular protein molecule numbers per cell at the single cell level and mapped across sections of clinical tissue samples are disclosed.

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 OF COUNTING PARTICLES

A method of seeking the number of particles of each kind in a group of particles in which plural kinds of particles are intermixed, by measuring, for example, two kinds of properties of each particle by a suitable method or device, plotting each particle on a rectangular co-ordinate system using the measured properties as its X and Y co-ordinates to obtain a distribution diagram, drafting boundaries to partition the distribution into clusters of the respective kinds and counting the number of particles within the boundary of each cluster, which, especially, even when the clusters of the respective particles are mutually overlapping, can reduce any deviation of the count of particles from its true value as much as possible by defining an optimum boundary. In this method, a center of gravity of each cluster is determined, a degree of attribution of each particle to each cluster is calculated based upon a distance of each particle from the center of gravity and the number of particles belonging ...

METHOD FOR AUTOMATED OF UNCONTROLLED ONTOLOGICAL ANALYSIS OF STRUCTURAL TYPES IN ELECTRON MICROGRAPHS

Methods of chromogen separation-based image analysis

Image Generation Device, Image Generation Method, and program

METHOD AND ELECTRONIC DEVICE FOR ASSISTING IN DETERMINING, IN IMAGING OF A SAMPLE, OF AT LEAST ONE ELEMENT OF INTEREST AMONG BIOLOGICAL ELEMENTS, ASSOCIATED COMPUTER PROGRAM

Ce procédé d'aide à la détermination, dans une image d'un échantillon, d'au moins un élément d'intérêt parmi une pluralité d'éléments biologiques, l'échantillon comportant la pluralité d'éléments biologiques, est mis en œuvre par un dispositif électronique. Ce procédé d'aide à la détermination comprend l'acquisition (100) d'une image de l'échantillon ; la présélection (110) d'au moins une zone dans l'image acquise, à partir d'une base de données comportant un ensemble de vignettes d'éléments de référence ; l'affichage (120) de chaque présélection de zone dans l'image ; l'acquisition (130) de la sélection par un opérateur d'au moins une zone parmi les zones présélectionnées, chaque zone sélectionnée correspondant à au moins un élément d'intérêt ; et la mise à jour (140) de la base de données en fonction de la ou des zones sélectionnées acquises, au moins une zone sélectionnée acquise étant ajoutée en tant que vignette d'élément de référence dans ladite base.

[...] and [...] of virus particles in electron [...][...]

"method of analyzing virus particles"

CYTOLOGICAL IMAGE PROCESSING DEVICE AND METHOD FOR QUANTIFYING CHARACTERISTICS OF CYTOLOGICAL IMAGE

CELLULAR PREDICTIVE MODELS FOR TOXICITIES

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; 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.

METHODS FOR SEGMENTING OBJECTS IN IMAGES

A method for segmenting a digital image into a plurality of target objects, comprising, generating a plurality of probability maps of the image, wherein each probability map is derived from a different segmentation classifier; generating a combined probability map based on the plurality of probability maps; mapping a plurality of image points based on one or more local object maxima; applying one or more object constraints based at least in part on the mapped points to identify local object information; applying one or more regional thresholds to the combined probability map, given the local object information and a background mask, to segment the image into regions; creating a segmented image at least in part by merging the segmented regions with corresponding local object maxima; and at least temporarily storing or displaying the segmented image on a digital device.

DISCOVER BIOLOGICAL FEATURES USING COMPOSITE IMAGES

An image processing system extracts parts or characteristics of interest from prepared biological samples One suitable use of the image processing system is to find biomarkers. But many other suitable uses are possible. Some components of the system include image preprocessing (data interpolation, retention time alignment, image noise filtering, background estimation, and formation of a composite image); image feature extraction (peaks, isotope groups, and charge groups); and computation of feature characteristics and expression statistics, differential expression, and non-differential expression. Outputs of the system include a candidate list of parts or characteristic of interest for aiding further discovery.

DATA PROCESSING DEVICE, DATA PROCESSING METHOD, IMAGE PROCESSING DEVICE, IMAGE PROCESSING METHOD, AND PROGRAM

Disclosed are a data processing device and a data processing method that allow generation of evaluation index data for accurately and minutely evaluating cultured myocardial cells. A motion detecting unit divides frame image data obtained by imaging cultured myocardial cells for a certain period of time into blocks to obtain motion detection data in units of blocks for each frame period. A feature-amount calculating unit calculates a feature amount for each block at the same position in frame images using the motion detection data. A classifying unit classifies each block into one of a plurality of classification categories using the calculated feature amounts. On the basis of the classification results, evaluation index data composed of individual classification result data that indicates the correspondence between the blocks and the classification categories is generated.

COLOR IMAGE COMPRESSION VIA SPECTRAL DECORRELATION AND ELIMINATION OF SPATIAL REDUNDANCY

A method of compressing a color image is provided. The color image comprises color data for a plurality of pixels. The method includes the step of obtaining red, green and blue pixel values of an object of interest in the image. A calculation is made of the complement of the red, green and blue values of the object of interest. Transformation coefficients are calculated which transform the complements of red, green and blue values of the object of interest into representations in a transformation color space. The transformation coefficients are applied to all the pixels in the image to thereby obtain a transformed data set representing the image having components along three mutually orthogonal axes (A, B and C herein) in a three-dimensional transformed color space. The transformed data set is scaled in accordance with the color quantization used in the system; e.g., the A, B and C values are between 0 and 255 for an 8 bit quantization. A compression algorithm, e.g., a loss less algorithm ...

CELLULAR PHENOTYPE

Phenotypes and the cells that exhibit those phenotypes are described. The phenotype may be established as a "snapshot" of the cells at a particular time or it may be established as a variation in features over time, or as some combination of these "static" and "dynamic" characterizations. The phenotype may be characterized by at least the following features: (a) chromosomes that approach metaphase but fail to separate and maintain alignment compared to a control cell or cell population; (b) a bipolar spindle that is at least about 10% longer than a corresponding metaphase mitotic spindle from the control cell or cell population; and (c) during interphase the cell or population of cells exhibits a phenotype that is substantially similar to that of the interphase cells of the control cell or cell population.

EXTRACTING CELL SHAPE INFORMATION CONTAINED IN CELL IMAGES

Methods and apparatus are provided for the analysis of images of cells and extraction biologically-significant shape-related features from the cell images. The extracted features may be correlated with particular conditions induced by biologically-active agents with which cells have been treated, thereby enabling the automated analysis of cells based on cell shape parameters. In particular, the invention provides methods for segmentation of cells in an image using a combination of a reference component image data and cell shape-indicative marker image data in a watershed technique. Further the invention provides a skeletonization and skeleton analysis technique for extracting biologically-relevant features from cells shapes.

Illumination Apparatus Optimized for Synthetic Aperture Optics Imaging Using Minimum Selective Excitation Patterns

A synthetic aperture optics (SAO) imaging method minimizes the number of selective excitation patterns used to illuminate the imaging target, based on the objects' physical characteristics corresponding to spatial frequency content from the illuminated target and/or one or more parameters of the optical imaging system used for SAO. With the minimized number of selective excitation patterns, the time required to perform SAO is reduced dramatically, thereby allowing SAO to be used with DNA sequencing applications that require massive parallelization for cost reduction and high throughput. In addition, an SAO apparatus optimized to perform the SAO method is provided. The SAO apparatus includes a plurality of interference pattern generation modules that can be arranged in a half-ring shape.

System and method for image analysis of multi-dimensional data

A system and method for analyzing multi-dimensional images includes a high content imaging system that includes an image capture device. An image acquisition module receives a series of images of a biological sample captured by the image capture device, and the series of images includes a sequence of image planes. A human interface module receives from a user computer specifications of a first image analysis step and a second image analysis step. The first image analysis step specifies a first image processing operation that processes an image plane of a series of images in accordance with at least another image plane of the series of images and the second image analysis step specifies a second image processing operation that processes each image plane of a series of images independently of the other image planes of the series. An image analysis module having a plurality of processors operating in parallel processes the first series of images in accordance with the first image processing ...

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 ...

Image analysis of the golgi complex

Methods, code and apparatus analyze cell images to automatically identify and characterize the Golgi complex in individual cells. This is accomplished by first locating the cells in the image and defining boundaries of those cells that subsume some or all of the Golgi complex of those cells. The Golgi complex in the images typically have intensity values corresponding to the concentration of a Golgi component in the cell (e.g. a polysaccharide associated with the Golgi complex). The method/system then analyzes the Golgi components of the image (typically on a pixel-by-pixel basis) to mathematically characterize the Golgi complex of individual cells. This mathematical characterization represents phenotypic information about the cells' Golgi complex and can be used to classify cells. From this information, mechanism of action and other important biological information can be deduced.

System for cell-based screening

The present invention provides systems, methods, and screens to measure receptor internalization in a single step with appropriate automation and throughput. This approach involves luminescent labeling of the receptor of interest and the automated measurement of receptor internalization to a perinuclear location.

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.

Method and device for segmenting a digital representation of cells

A method and a device for segmenting a digital image of biological cells, a method and a device for analyzing the dynamic behavior of biological cells and a method and a device for visualizing the dynamic behavior of biological cells. To provide a segmentation method that has minimal technical requirements, does not necessitate any special preparation or manipulation of the cells to be observed and which requires as few assumptions as possible to be made about the properties of the cells, it is proposed that the method comprises the following steps: determining a maximum gradient for each pixel of the image as the maximum difference between a pixel value of the pixel and the respective pixel values of all or selected neighboring pixels, determining a segmentation threshold value using the frequencies of the maximum gradients, classifying the images into an object class and an environment class using the segmentation threshold value and forming a segmentation zone of the digital image using ...

Image Data Analytics for Computation Accessibility and Configuration

The image data analytics for computation configuration method and computer system with computation accessibility is provided. The computation accessibility and configuration on image data processing are implemented by a plurality of computers having a plurality of processors and a plurality of data storages, the image data analytics for computation accessibility and configuration includes the following steps: inputting an original image by an input device and initializing the original image; defining a plurality of tiles equally dividing the original image into a same size of a regular shape; transferring the plurality of image regions to form a graph having vertices and edges; cutting the graph into a plurality of sub-graphs; arranging the plurality of sub-graphs to the plurality of processors or cores to conduct parallel processing simultaneously for analyzing the image data; and storing a plurality of processing results respectively in the plurality of data storages.

Method and apparatus for tissue region identification

Certain aspects of an apparatus and method for method and apparatus for tissue region identification may include segmenting the image into a plurality of regions, filtering out regions in the plurality of regions which are curvilinear, and isolating a target area where the tissue sample is identified as the plurality of regions not filtered.

Cell image evaluation device, method, and program

A cell image evaluation device includes an image acquisition unit that acquires a captured image of a cell, a cell evaluation unit that evaluates the cell image, and a maturity information acquisition unit that acquires information related to maturity of the cell. The cell evaluation unit determines a method for evaluating the cell image on the basis of the information related to the maturity.

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.

SPATIAL MULTIPLEXING OF HISTOLOGICAL STAINS

The following concerns a method for co-localization of microscopy or histology stains by the assembly of a virtual image from one or more imaging operations. In particular, the method decreases the time required to obtain multiple labeled antigen or protein histology images of a biological sample. The method includes imaging the tissue as it is sliced by a microtome with a knife edge scanning microscope and spatially aligning the samples by the generated images. The spatial alignment of samples enabled by the method allows a panel of different antigen or protein secondary or functional stains to be compared across different sample slices, thereby allowing concurrent secondary stains of tissues and cells.

Methods and systems for automated segmentation of dense cell populations

Systems and methods for segmenting images comprising cells, wherein the images comprise a plurality of pixels; one or more three dimensional (3D) clusters of cells are identified in the images; and the 3D clusters of cells are automatically segmented into individual cells using one or more models.

Quality Control of Automated Whole-slide Analyses

The subject disclosure presents systems and methods for automatically selecting meaningful regions on a whole-slide image and performing quality control on the resulting collection of FOVs. Density maps may be generated quantifying the local density of detection results. The heat maps as well as combinations of maps (such as a local sum, ratio, etc.) may be provided as input into an automated FOV selection operation. The selection operation may select regions of each heat map that represent extreme and average representative regions, based on one or more rules. One or more rules may be defined in order to generate the list of candidate FOVs. The rules may generally be formulated such that FOVs chosen for quality control are the ones that require the most scrutiny and will benefit the most from an assessment by an expert observer.

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.

Image processing using measures of similarity

The invention provides methods of relating a plurality of images based on measures of similarity. The methods of the invention are useful in the segmentation of a sequence of colposcopic images of tissue, for example. The methods may be applied in the determination of tissue characteristics in acetowhitening testing of cervical tissue, for example.

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.

Fluorescence image processing apparatus and method

A fluorescence image processing apparatus for enhancing a fluorescence image includes an image segmentation unit, a background determination unit a background reduction unit, and an image enhancement unit. The background and the area of interest of a glass slide are first segmented and then this segmentation is used to enhance the contrast between both areas.

Methods of chromogen separation-based image analysis

Methods for chromogen separation-based image analysis are provided, with such methods being directed to quantitative video-microscopy techniques in cellular biology and pathology applications.

СПОСОБ АНАЛИЗА КЛЕТОЧНЫХ СТРУКТУР И ИХ КОМПОНЕНТОВ

Изобретение относится к области вирусологии. Предложен способ анализа изображения вирусных частиц, при котором получают с помощью технологии электронной микроскопии изображения вирусных частиц. Первую вирусную частицу характеризуют как находящуюся на первой стадии созревания, а вторую вирусную частицу - как находящуюся на второй стадии созревания. Первое изображение и второе изображение преобразуют соответственно в первый и второй профили шкалы яркости на основе пиксельных данных. Первый и второй профили шкалы яркости затем сохраняют соответственно в виде первой и второй матриц. Третью вирусную частицу идентифицируют в третьем изображении. Третье изображение преобразуют в третий профиль шкалы яркости. Третью шкалу яркости сравнивают с первой и второй матрицей для определения стадии созревания третьей вирусной частицы. 3 з.п. ф-лы, 7 ил.

ИДЕНТИФИКАЦИЯ И КЛАССИФИКАЦИЯ ВИРУСНЫХ ЧАСТИЦ НА ТЕКСТУРИРОВАННЫХ ЭЛЕКТРОННЫХ МИКРОФОТОГРАФИЯХ

Изобретение относится к способу идентификации и определения характеристик структур на электронных микрофотографиях. Техническим результатом является повышение качества идентификации и сокращение времени анализа вирусных частиц с помощью электронного микроскопа. Способ включает: отбор структур на первом изображении, при этом структуры имеют первый тип формы, деформированный в первом направлении; преобразование отобранных структур во второй тип формы, отличающийся от первого типа формы; использование преобразованных структур второго типа формы для формирования эталонных изображений; идентификация новой структуры на втором изображении; при этом новая структура имеет первый тип формы; деформирование структуры со вторым типом формы на каждом эталонном изображении в первом направлении; определение какое из эталонных изображений является предпочтительным эталонным изображением, которое наилучшим образом соответствует новой структуре; и деформирование ряда эталонных изображений, чтобы они приобрели ...

ВЫРАВНИВАНИЕ УПОРЯДОЧЕННОГО СТЕКА ИЗОБРАЖЕНИЙ ОБРАЗЦА

Изобретение относится к области техники выравнивания упорядоченного стека изображений разрезанного образца. Техническим результатом является повышение точности выравнивания каждого изображения образца, разделенного на части. В соответствии с настоящим способом и устройством, упорядоченный стек изображений выравнивается посредством последовательного определения, по меньшей мере, для двух уже выровненных изображений упорядоченного стека соответствующих рассогласований с невыровненным изображением, которое должно быть выровнено следующим, выбора, по меньшей мере, из двух выровненных изображений, в качестве опорного изображения, выровненного изображения, с которым невыровненное изображение имеет наименьшую величину рассогласования, и выравнивания невыровненного изображения с выбранным опорным изображением. 3 н. и 11 з.п. ф-лы, 3 ил.

АРХИТЕКТУРА СЕРВЕРОВ И КЛИЕНТОВ В ЦИФРОВОЙ ПАТОЛОГИИ

СПОСОБ ПОДДЕРЖКИ РАЗЛИЧЕНИЯ КОРНЕОЦИТОВ

... 1. Способ поддержки различения изображения корнеоцитов с использованием компьютера, содержащий этапы, на которых: ! регистрируют в запоминающем устройстве компьютера данные группы окрашенных образцов, включающие в себя множество увеличенных изображений окрашенных образцов корнеоцитов, и данные группы неокрашенных образцов, включающих множество увеличенных изображений неокрашенных образцов корнеоцитов; ! считывают данные группы окрашенных образцов и данные группы неокрашенных увеличенных образцов из запоминающего устройства, и сравнивают и те, и другие данные с помощью центрального процессора компьютера; ! создают формулу преобразования для значения RGB на основе результата сравнения с помощью центрального процессора; ! считывают в запоминающее устройство изображение, показывающее состояние корнеоцитов кожи, в виде увеличенной картины изображения посредством средства захвата изображения, подключенного к компьютеру; ! считывают увеличенную картину изображения и преобразуют значение RGB для ...

Display apparatus and display control method for displaying images

A display control unit 12 performs predetermined image processing 122 on at least one image data item among a plurality of pictures, and does not execute the pre-set image processing on at least another one image among the set of video items. A display unit 13 shows a number of image data items, if (A) it is determined, on the basis of metadata 125 associated with the image set, that the related images derive from the same apparatus, or if (B) that a presentation mode is set in which a number of images are to be depicted simultaneously, including a first image and a second picture which is a copy of the first one. The metadata may include information on the camera which has acquired the pictures (Fig, 7a). The image processing may comprise adding different colours according to the picture brightness level.

Methods And Apparatus For Characterising Cells And Treatments

Methods, data processing apparatus and computer program products for characterising cells and the affect of treatments administered to cells are disclosed. In particular methods of identifying bi-nuclear cells are described which include capturing an image of a plurality of marked cells and processing the image to obtain features of the plurality of cells. The features are analyzed to determine whether the features is indicative of bi-nuclear cells. Those cells for which the first feature is indicative of bi-nuclear cells are identified as being bi-nuclear. Three algorithms in particular are described. A first algorithm can be used to determine the number of nuclei in an image of a nuclear component by determining the number of concave regions within the outline of the image. A second algorithm uses a measure of the amount of cytoplasmic material between a pair of nuclei to identify by-nuclear cells. A third algorithm uses the statistics of the spatial distribution of objects to identify ...

Method and apparatus

Method and apparatus for use in the image analysis of biological specimens

Surface Analysis

A method and system of determining a condition of a surface are described. The method comprises obtaining image data 402 of a surface, followed by extracting a plurality of test parameters 406 associated with a condition of the surface from the image data and, finally, determining the condition of the surface based on these test values 408. The method may comprise the use of similarity values for the test parameters and the comparison of these affinity values with a reference one. The similarity values may be evaluated via Principal Component Analysis (PCA). The image data may be normalised 404 to reduce illumination geometry and colour effects. The trial parameters may relate to a hue and/or texture of the surface. The surface condition can be its roughness. The method may be applied to a part made with an additively manufactured polymer. The system may comprise a microscope (200, Fig. 2a) with a camera (210, Fig. 2a).

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.

Instrument guidance system for sinus surgery

IMAGE PROCESSING METHOD IN MICROSCOPY

The invention pertains to the field of image processing in digital pathology. It notably proposes a method for processing a first digital image, representing a sample in a region, and which image has been acquired from the sample by means of a microscopic imaging system () and is stored in a multi-resolution image data structure (), comprising the steps of:—retrieving () a sub-region of the first digital image at a first resolution, —executing () a transform function on the retrieved sub-region, the transform function modifying a content of the sub-region according to at least one metric derived from a second resolution representation of the first digital image. 1. A method for processing a first digital image , representing a sample in a region , and which image has been acquired from the sample by means of a microscopic imaging system and is stored in a multi-resolution image data structure , comprising the steps of:retrieving a sub-region of the first digital image at a first resolution,executing a transform function on the retrieved sub-region, the transform function modifying a content of the sub-region according to at least one metric derived from a second resolution representation of the first digital image.2. A method according to claim 1 , further comprising the steps of computing the transform function according to at least one metric derived from a second digital image claim 1 , in addition to said metric derived from said first image.3. A method according to claim 2 , wherein the transform function is a spatial transformation.4. A method according to claim 2 , wherein the first and second digital images represent the same sample claim 2 , and wherein computing the transform function comprises the step of spatially aligning the first digital image with respect to the second digital image.5. A method according to claim 2 , further comprising the steps of selecting a sub-region in the second digital image claim 2 , this sub-region having a contour claim 2 , ...

AUTOMATED DETECTION OF UVEITIS USING OPTICAL COHERENCE TOMOGRAPHY

Systems and methods for automatically detecting, classifying and quantifying clumps indicative of inflammation in the eye using optical coherence tomography images are described. Clump detection relies on both intensity and geometric thresholding. Applications of the invention include improved diagnosis, classification and monitoring of inflammatory disease. 1. A method for automatically identifying inflammatory clumps within the eye from image data obtained with an optical coherence tomography (OCT) system , the method comprising:identifying the region of the tissue from within the image data that will be searched for clumps;identifying locations in the region that have a brightness above a predetermined threshold and have geometric properties that fall within a predetermined range associated with clumps; anddisplaying or quantifying the identified clumps.2. A method as recited in claim 1 , wherein the step of identifying the region of the tissue from within the image data that will be searched for clumps claim 1 , includes segmenting the image data.3. A method as recited in claim 1 , wherein prior to identifying the region of the tissue from within the image data that will be searched for clumps claim 1 , the image data is smoothed.4. A method as recited in claim 1 , wherein an image is generated and displayed that permits visualization of the size and location of the identified clumps.5. A method as recited in claim 4 , wherein the image is displayed in inverted grayscale to enhance visualization.6. A method as recited in claim 4 , wherein the image is displayed to a clinician on a user interface and further comprising validating the automatically detected clumps based on input from the clinician.7. A method as recited in claim 6 , wherein the clinician provides input on false detections.8. A method as recited in claim 1 , wherein the number of identified clumps is calculated and then displayed or stored.9. A method as recited in claim 8 , wherein the number of ...

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 ...

IMAGE REGISTRATION METHOD

The present application relates to an image registration method, comprising: selecting a registration source image {p,} and a registration target image {q}; applying a random perturbation to {p} in accordance with a preset random perturbation control parameter σ so that it is deformed to obtain {p′}, and obtaining a set of closest points, i.e., {q′}, on {q} corresponding to points on {p′}; performing an iterative operation on {p} and {q′} in accordance with a preset initial coordinate transformation Hto obtain a coordinate transformation {H}, Подробнее

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.

INSTRUMENT-BASED IMAGE REGISTRATION FOR FUSING IMAGES WITH TUBULAR STRUCTURES

A system and method for registering three-dimensional images with two-dimensional intra-operative images includes segmenting () a tubular structured organ in a three-dimensional image of the organ, and projecting () the three-dimensional image of the organ into two-dimensional space to provide a projected image. A medical instrument depicted in a two-dimensional image of the medical instrument is segmented (). A similarity score is computed between the projected image and a shape of the medical instrument depicted in the two-dimensional image to determine a best match. The projected image is registered () to the two-dimensional image based on the best match. 1. A method for registering three-dimensional images with two-dimensional intra-operative images , comprising:segmenting a tubular structured organ in a three-dimensional image of the organ;projecting the three-dimensional image of the organ into two-dimensional space to provide a projected image;segmenting a medical instrument depicted in a two-dimensional image of the medical instrument; andregistering the projected image to the two-dimensional image based on a best match between the projected image of the organ and a shape of the medical instrument depicted in the two-dimensional image.2. The method as recited in claim 1 , wherein segmenting a tubular structured organ includes determining a centerline of the tubular structured organ.3. The method as recited in claim 2 , further comprising computing a similarity score to determine the best match wherein computing the similarity score includes comparing a centerline of a segmented medical instrument with the centerline of the tubular structured organ to determine the similarity score.4. The method as recited in claim 1 , wherein the tubular structured organ includes airways of a lung and the medical instrument includes a bronchoscope.5. The method as recited in claim 1 , wherein the three-dimensional image includes a pre-operative computer tomography image.6. ...

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.

SPECTRAL UNMIXING FOR VISUALIZATION OF SAMPLES

Disclosed methods include: acquiring a first sequence of multiple images of a sample, with each image in the first sequence corresponding to a different spectral weighting function; unmixing the first sequence of images into data corresponding to a first set of unmixed images, where each unmixed image in the first set corresponds to a spatial distribution in the sample of a different one of multiple components at a first time; acquiring one or more additional images of the sample and combining the additional images with one or more of the images in the first sequence to form a second sequence of images; unmixing the second sequence of images into data corresponding to a second set of unmixed images; and displaying information about the sample as a function of time based on the data corresponding to the first and second sets of unmixed images. 115-. (canceled)16. A computer-implemented method comprising:acquiring a first sequence of spectral images of an animal and unmixing the first sequence of spectral images into set of component images, wherein each component image shows a spatial distribution of a different component of the animal, and wherein each component has a unique spectral signature;displaying at least some of the component images; and (a) acquiring and combining one or more additional spectral images of the animal with at least some of the previously acquired spectral images;', '(b) unmixing the combined spectral images into a new set of component images;', '(c) displaying at least some members of the new set of component images; and', '(d) repeating steps (a)-(c)., 'providing a real-time display of updated component images, wherein providing the real-time display comprises17. The method of claim 16 , wherein step (c) is repeated at a frequency of at least 1 Hz.18. The method of claim 16 , wherein acquiring spectral images of the animal comprises measuring fluorescence emission from the animal.19. The method of claim 18 , wherein acquiring spectral ...

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 ...

APPARATUS, SYSTEM, AND METHOD FOR IMAGE NORMALIZATION USING A GAUSSIAN RESIDUAL OF FIT SELECTION CRITERIA

An apparatus and method for image normalization using a Gaussian residual of fit selection criteria. The method may include acquiring a two-dimensional image of a plurality of particles, where the plurality of particles comprises a plurality of calibration particles, and identifying a calibration particle by correlating a portion of the image corresponding to the calibration particle to a mathematical model (e.g. Gaussian fit). The measured intensity of the calibration particle may then be used to normalize the intensity of the image. 1. A method for normalizing an image , the method comprising:acquiring a two-dimensional image of a plurality of particles, where the plurality of particles comprises a plurality of calibration particles;identifying a calibration particle by correlating a portion of the image corresponding to the calibration particle to a mathematical model;measuring an intensity of the calibration particle; andutilizing the intensity of the calibration particle to normalize the intensity of the image.2. The method of claim 1 , where the two-dimensional image is an image in a reporter channel.3. The method of claim 1 , where the calibration particle is internally dyed.4. The method of claim 1 , further comprising:identifying a plurality of calibration particles, where the plurality of calibration particles are distributed into a plurality of regions of the two-dimensional image; andutilizing an intensity of the plurality of calibration particles to normalize an intensity of the plurality of regions.5. The method of claim 1 , further comprising utilizing the intensity of the calibration particle to normalize the intensity of a second two-dimensional image of the plurality of particles.6. The method of claim 5 , where the second two-dimensional image is a classification image.7. The method of claim 1 , where the mathematical model is a Gaussian mathematical model.8. The method of claim 1 , where the mathematical model is a quadratic mathematical model.9. ...

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.

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 ...

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 ...

IMAGE PROCESSING APPARATUS AND METHOD OF OPERATION OF IMAGE PROCESISNG APPARTUS

An image processing apparatus includes a feature value calculating section that calculates a feature value from an image picked up of a living mucous membrane, an extraction section that extracts a structure corresponding to the feature value, and a region division section that divides the structure into partial regions according to a predetermined condition. 1. An image processing apparatus comprising:an image input device that receives an input of an image obtained by image pickup of a living mucous membrane having a three-dimensional structure;an extraction device that extracts pixels corresponding to the structure appearing as a continuous pattern in the image from pixels included in the image inputted to the image input device;a feature value calculating device that calculates a feature value representing a feature of the structure extracted by the extraction device; anda region division device that extracts, based on the feature value calculated by the feature value calculating device, pixels corresponding to a structure that is continuous in a three-dimensional space, from the pixels corresponding to the structure.2. The image processing apparatus according to claim 1 , further comprising:a divarication portion identifying device that identifies a pixel corresponding to a divarication portion from the pixels corresponding to the structure extracted by the extraction device; anda segment division device that divides the pixels corresponding to the structure into a plurality of segments each including the divarication portion at an end,wherein the feature value calculating device, based on feature values of the pixels corresponding to the structure extracted by the extraction device, calculates the feature value representing the feature of the structure, for each of the plurality of segments; andwherein the region division device, based on the feature value calculated by the feature value calculating device for each of the plurality of segments, extracts the ...

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 ...

Illumination Apparatus Optimized for Synthetic Aperture Optics Imaging Using Minimum Selective Excitation Patterns

A synthetic aperture optics (SAO) imaging method minimizes the number of selective excitation patterns used to illuminate the imaging target, based on the objects' physical characteristics corresponding to spatial frequency content from the illuminated target and/or one or more parameters of the optical imaging system used for SAO. With the minimized number of selective excitation patterns, the time required to perform SAO is reduced dramatically, thereby allowing SAO to be used with DNA sequencing applications that require massive parallelization for cost reduction and high throughput. In addition, an SAO apparatus optimized to perform the SAO method is provided. The SAO apparatus includes a plurality of interference pattern generation modules that can be arranged in a half-ring shape. 1. An apparatus for performing synthetic aperture optics (SAO) on a target including one or more objects , the apparatus comprising: a beam splitter for splitting a first laser beam into the pair of laser beams including a second laser beam and a third laser beam; and', 'a rotating window configured to modulate an optical path length of the third laser beam, wherein the second laser beam and the modulated third laser beam interfere to generate the selective excitation pattern on the target, and wherein the plurality of phases of the selective excitation pattern of the IPGM is generated by adjusting the optical path length of the third laser beam; and, 'a plurality of interference pattern generation modules (IPGMs), each IPGM configured to generate a pair of light beams that interfere to generate a selective excitation pattern illuminating the target at a predetermined orientation and a predetermined pitch, each IPGM further configured to generate a plurality of phases of the selective excitation pattern, each IPGM further includingan optical imaging module configured to optically image the illuminated target at a first resolution, the optical imaging module further configured to ...

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 ...

METHOD OF TRACKING A PLURALITY OF OBJECTS

Method of tracking a plurality of objects comprising: focusing an image of the objects on an imaging element using an optical system; capturing the image of the objects using an imaging element comprising a plurality of pixels; measuring at least one characteristic of the objects from the captured image using an image processor; wherein the field of view is set be the widest field of view for which the image processor is able to measure the at least one characteristic. 1. Method of tracking a plurality of objects comprising:focusing an image of the objects on an imaging element using an optical system;capturing the image of the objects using an imaging element comprising a plurality of pixels;measuring at least one characteristic of the objects from the captured image using an image processor;wherein the field of view is set be the widest field of view for which the image processor is able to measure the at least one characteristic.2. The method of claim 1 , wherein the at least one characteristic includes on or more of: bends per minute claim 1 , bending amplitude claim 1 , length claim 1 , translational speed and paralysis rate.3. The method of any preceding claim claim 1 , wherein the field of view is determined based on the average size of the objects and a predefined resolution required for the image processor to measure the at least one characteristic.4. The method of claim 3 , wherein the characteristic is length and/or translational speed and the predefined resolution is the resolution at which the objects are still detectable by the image processor.5. The method of claim 3 , wherein the characteristic is bending amplitude and/or bends per minute and the bending is calculated based on an eccentricity of the objects claim 3 , and the predefined resolution is the resolution at which the total error in the characteristic is at a minimum.6. The method of claim 5 , wherein the error minimum is determined based on the error on eccentricity caused by pixelisation ...

Particle characterization using optical microscopy

A method of optically characterizing individual molecules/molecular complexes, or other particles, in solution. The method comprises flowing a solution comprising the molecules/molecular complexes into an imaging region of a microfluidic channel, wherein the imaging region of the microfluidic channel has a first lateral dimension of greater than 1 μm in an x-direction wherein the x-direction is perpendicular to a direction of the flow; capturing a succession of images of the individual molecules/molecular complexes in the imaging region; tracking movement of the individual molecules/molecular complexes in at least the x-direction in the imaging region using the succession of images; and characterizing the individual molecules/molecular complexes from the tracked movement. In some implementations the characterizing comprises determining a diffusion coefficient of the molecules/molecular complexes from the tracked movement.

IDENTIFYING GEOMETRICAL PROPERTIES OF ROCK STRUCTURE THROUGH DIGITAL IMAGING

A workflow for rock sample image processing is established to determine geometrical parameters of rock texture. Individual pores and grains are identified using Hoshen-Kopelman multi-cluster labeling algorithm and watershedding technique. Separated elements are fitted with ellipses and pore/grain size distributions, aspect ratios and orientations of fitting ellipses are obtained. Such information is especially valuable in interpretation and forward modeling of dielectric responses using textural models. Rock sample images by high-resolution confocal microscopy are selected to test the workflow and results are analyzed. 1. A computer-implemented method executed by one or more processors , the method comprising:receiving an image depicting a rock sample from a reserve;binarizing the image into two a first population and a second population, the first population representing empty space in the rock sample, and the second population representing a solid matrix in the rock sample;calculating a porosity of the rock sample based on the first and second populations, the porosity comprising a measure of the empty space in the rock sample;generating a binary image of the rock sample based on the first and second populations when the calculated porosity meets a threshold as compared to a measured value;determining geometrical properties of the empty space and the solid matrix of the rock sample based on a particle analysis of the binary image;generating a digital model of the rock sample based on the geometrical properties of the empty space and the solid matrix of the rock sample; anddetermining a productivity of the reserve according to the digital model of the rock sample.2. The method of claim 1 , further comprising:before binarizing the image, filtering the image to remove noise or enhance a contrast of the image.3. The method of claim 1 , wherein the particle analysis of the binary image includes watershedding the binary image to separate grains in the solid matrix in ...

Non-transitory computer-readable recording medium storing image analysis program and image analysis method

Provided are a non-transitory computer-readable recording medium storing an image analysis program and an image analysis method which can properly extract a cell nucleus area corresponding to a diagnostician from an image for pathological diagnosis with reducing a burden on diagnosticians. A device for analyzing images for pathological diagnosis, acquires a pathological diagnosis image prepared by shooting a tissue specimen, and extracts possibles for a cell nucleus area from the pathological diagnosis image, by using a first parameter for determining a criterion for cell nucleus determination for each diagnostician. The device further extracts one or more cell nucleus areas from the possibles for a cell nucleus area, by using the second parameter for determining a criterion for cell nucleus determination for each diagnosis purpose, and causes a display unit to display the pathological diagnosis image with the one or more cell nucleus areas made recognizable.

SYSTEM AND METHOD ASSOCIATED WITH PROGRESSIVE SPATIAL ANALYSIS OF PRODIGIOUS 3D DATA INCLUDING COMPLEX STRUCTURES

A system associated with progressive spatial analysis of prodigious 3D data including complex structures is disclosed. The system receives minimum boundary information related to a first data object and a second data object, which are proximate neighbors. The system determines whether boundary data associated with a first data object is within an area delineated by minimum boundary information of first data objects. A first geometric structure associated with the first data object is generated based on respective decompressed data. A structural skeleton is determined using the first geometric structure to identify respective skeleton vertices. A geometric representation is generated based on the skeleton vertices associated with the first geometric structure. The system determines whether boundary data associated with the second data object is within the area delineated by the minimum boundary information of the first data object. A centroid point of the second data object that intersects the geometric representation associated with the first object is identified. A location of the centroid point of the second data object with respect to the first data object is determined in order to identify a minimum distance between the first data object and the second data object. 1. A system associated with progressive spatial analysis of prodigious 3D data including complex structures , the system comprising: receiving minimum boundary information related to a first data object;', 'receiving minimum boundary information related to a second data object, the first data object and the second data object being proximate neighbors;', 'determining whether boundary data associated with the first data object is within an area delineated by minimum boundary information of first data objects;', 'generating a first geometric structure associated with the first data object based on respective decompressed data associated with the first data object;', 'determining a structural skeleton ...

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 ...

IMAGE PROCESSING METHOD AND IMAGE PROCESSOR

An image processing method is for detecting the edge of a well where cells are analyzed, from image data obtained by capturing the well and surroundings of the well, and an edge coordinate group for the well is extracted from the image data. If a plurality of edge coordinate groups has been extracted, edge candidates for the well are generated for each of the plurality of edge coordinate groups. Then, an edge candidate that satisfies a predetermined reference value is selected from among the generated edge candidates. If a plurality of edge candidates has been selected, relative evaluations are conducted to determine the edge of the analysis region from among the plurality of edge candidates. 1. An image processing method of detecting an edge of an analysis region where an object is analyzed , from image data obtained by capturing an image of the analysis region and surroundings of the analysis region , the method comprising the steps of:a) extracting an edge coordinate group for the analysis region from the image data;b) in a case where a plurality of edge coordinate groups has been extracted in the step a), generating edge candidates for the analysis region from each of the plurality of edge coordinate groups;c) selecting an edge candidate that satisfies a predetermined reference value from among the edge candidates generated in the step b); andd) in a case where a plurality of edge candidates has been selected in the step c), conducting relative evaluations to determine the edge of the analysis region from among the plurality of edge candidates.2. The image processing method according to claim 1 , wherein in the step b) claim 1 , edge candidates are generated using polynomial approximation for each of the plurality of edge coordinate groups.3. The image processing method according to claim 2 , whereinin the step c), an edge candidate is selected that is generated from an edge coordinate group that includes a predetermined number or more of edge coordinates whose ...

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 ...

SAMPLE APPLICATOR SENSING AND POSITIONING

Systems and methods for positioning a sample applicator relative to a substrate include: (a) obtaining an image of the sample applicator in proximity to the substrate, where the image includes a direct image region corresponding to the sample applicator and a first reflected image region corresponding to an image of the sample applicator reflected from a surface of the substrate; (b) determining a position of an edge of the sample applicator in the direct image region; (c) determining a position of a reflected edge of the sample applicator in the first reflected image region; (d) determining a distance between the edge of the sample applicator and the reflected edge of the sample applicator; and (e) determining the position of the sample applicator relative to the substrate based on the distance between the edges. 1obtaining an image of the sample applicator in proximity to the substrate, the image comprising a direct image region corresponding to the sample applicator and a first reflected image region corresponding to an image of the sample applicator reflected from a surface of the substrate;determining a position of an edge of the sample applicator in the direct image region;determining a position of a reflected edge of the sample applicator in the first reflected image region;determining a distance between the edge of the sample applicator and the reflected edge of the sample applicator; anddetermining the position of the sample applicator relative to the substrate based on the distance between the edges.. A method for positioning a sample applicator relative to a substrate, the method comprising: This application is a continuation application of U.S. patent application Ser. No. 13/549,251, filed on Jul. 13, 2012, which claims priority under 35 U.S.C. §119(e) to U.S. Provisional Patent Application No. 61/510,728, filed on Jul. 22, 2011. The entire contents of each of these applications are incorporated by reference in their entirety.This disclosure relates to ...

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.

BLOOD DETECTION SYSTEM WITH REAL-TIME CAPABILITY AND METHOD OF OPERATION THEREOF

A blood detection system, and a method of operation thereof, including: a camera for obtaining an input image frame; and a processing unit connected to the camera, the processing unit including: an image block module for extracting image blocks from the input image frame, and an automatic blood detection module, coupled to the image block module, for calculating an overall blood probability of the image blocks including: determining a red color dominance probability, determining a red color deviation probability, and determining a red color colorfulness probability. 1. A method of operation of a blood detection system comprising:obtaining an input image frame with a camera;extracting image blocks from the input image frame; and determining a red color dominance probability,', 'determining a red color deviation probability, and', 'determining a red color colorfulness probability., 'calculating an overall blood probability of the image blocks including2. The method as claimed in further comprising providing a blood detection threshold probability.3. The method as claimed in further comprising determining a high blood probability region by comparing the overall blood probability to a blood detection threshold probability.4. The method as claimed in further comprising generating a blood mask based on the overall blood probability being higher than a blood detection threshold probability.5. The method as claimed in wherein extracting image blocks from the input image frame includes grouping blocks of pixels to form each of the image blocks.6. A method of operation of a blood detection system comprising:obtaining an input image frame with a camera;extracting image blocks from the input image frame; determining a red color dominance probability,', 'determining a red color deviation probability, and', 'determining a red color colorfulness probability;, 'calculating an overall blood probability of the image blocks includingproviding a blood detection threshold probability; ...

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.

Method for Determining the Overall Brightness of at Least One Object in a Digital Image

The present invention relates to a method and a system for the overall brightness of at least one object in a digital image. In particular, the method for determining the overall brightness of at least one object in a digital image, the at least one object being configured to receive molecules comprising genetic information, the method comprising the steps of: generating an area A comprising k sample points, the k points being distributed in the area A according to a predetermined distribution; aligning the center position of the area A to the center position of the at least one object in the digital image; extracting the intensity value at each of the k sample points; and calculating the central tendency of the extracted intensity values at the respective k sample points. 1. A computer implemented method for determining an overall brightness of at least one object in a digital image , the at least one object being configured to receive molecules comprising genetic information , the method comprising:generating an area A comprising k sample points, the k sample points being distributed in the area A according to a predetermined distribution;aligning a center position of the area A to a center position of the at least one object in the digital image;extracting an intensity value at each of the k sample points; andcalculating a central tendency of the extracted intensity values at the respective k sample points.2. The method according to claim 1 , wherein the area A has a shape of the at least one object in the digital image.3. The method according to claim 1 , wherein the area A has a size of the at least one object in the digital image multiplied with a factor.4. The method according to claim 1 , wherein the at least one object is a bead configured to receive DNA or RNA.5. The method according to claim 1 , wherein the at least one object has a shape of a first disk in the digital image having a diameter of approximately 2.8 pixels.6. The method according to claim 1 ...

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.

Method for Image Registration

A method () for image registration of sections, in particular for image registration of histological sections, is described. The method comprises reading in () of a data set for at least two sections, wherein each of the data sets comprises m images of each section in m resolution levels (), wherein each image of the resolution levels 1 to m−1 is divided into at least two cells, wherein each image has a different image resolution, wherein the image with the highest image resolution is associated with the resolution level 1 and the image with the lowest image resolution is associated with the resolution level m; registering () of the two mth images of the two sections on the resolution level m and determining of a global transformation for the resolution level m; aligning () of the two images of the resolution level m−1 using the global transformation of the resolution level m; and registering () of a subgroup of cells from all cells of the two images of the resolution level m−1. 1. A method for image registration of sections comprising:a) reading in data sets of at least two sections, wherein each of the data sets comprises m images of each section at m resolution levels, wherein each image of the resolution levels 1 to m−1 is divided into at least two cells, wherein each image has a different image resolution, wherein the image with the highest image resolution is associated with resolution level 1 and the image with the lowest image resolution is associated with resolution level m;{'sup': 'th', 'b) registering the two mimages of the two sections on the resolution level m and determining a global transformation for the resolution level m;'}c) aligning the two images of the resolution level m−1 using the global transformation of the resolution level m; andd) registering a subgroup of cells from all of the cells of the two images of the resolution level m−1.2. The method according to claim 1 , further comprisinge) aligning the two images of the resolution level m−2 ...

EFFICIENT CONTOURS AND GATING

Methods and systems for efficient contour and gating in flow cytometry are provided. Event data is compressed to reduce the number of points needed to represent polygon contours for the event data. Selection of a level within the contour then causes the generation of a gate. This allows limited resource devices, such as touchscreen wireless devices, to render and gate flow cytometry data in a resource efficient manner. 1. A computer-implemented method of polygon mesh reduction for flow cytometry events , the method comprising: receiving a requested density level for presenting data for flow cytometry events;', 'generating an initial contour diagram, wherein the initial contour diagram is defined by a plurality of polygons, said plurality of polygons representing regions corresponding to respective density levels for the data in two dimensions, and wherein each polygon is defined as at least a portion of one of a plurality of tiles that divide the initial contour diagram in two dimensions;', 'for a given tile included in the plurality of tiles of the initial contour diagram corresponding to the requested density level, identifying an adjacent tile to the given tile at the requested density level that defines a first polygon matching a second polygon defined by the given tile based at least in part on a tile code for the given tile, wherein the tile code comprises a set of elements, each element encoding a density level for a point defining the second polygon;', 'when the adjacent tile is identified, combining the first polygon and the second polygon into a larger polygon, thereby reducing the number of polygons to form a reduced contour diagram defined by less data than the data for the initial contour diagram; and', 'causing display of the reduced contour diagram for the requested density level., 'under control of one or more processors,'}2. The computer-implemented method of claim 1 , wherein generating the initial contour diagram comprises:receiving a set of data; ...

Methods and Systems for Image Data Processing

Methods, storage mediums, and systems for image data processing are provided. Embodiments for the methods, storage mediums, and systems include configurations to perform one or more of the following steps: background signal measurement, particle identification using classification dye emission and cluster rejection, inter-image alignment, inter-image particle correlation, fluorescence integration of reporter emission, and image plane normalization. 17-. (canceled)8. A system , comprising: selecting a first set of one or more optical filters corresponding to a first wavelength band;', 'illuminating the particles through the first set of optical filters;', 'selecting a second set of one or more optical filters corresponding to a second wavelength band; and', 'illuminating the particles through the second set of optical filters; and, 'an imaging subsystem configured to image, at different wavelength bands, particles disposed within the imaging subsystem, wherein the imaging comprises store data acquired for multiple images of the particles, and wherein particular images of the multiple images include spots corresponding to the particles, wherein a first image of the particular images corresponds to the first set of optical filters, wherein a second image of the particular images corresponds to the second set of optical filters;', 'create a first composite image of the multiple images, wherein the first composite image includes first composite spots corresponding to the particles, the first composite spots having a first amount of misalignment from the spots in the particular images; and', 'modify coordinates of at least one of the multiple images such that a second composite image based on the modified coordinates includes second composite spots having a second, smaller amount of misalignment from the spots in the multiple images., 'a data processing subsystem configured to9. The system of claim 8 , wherein the first set of optical filters corresponds to a plurality of ...

APPARATUS, SYSTEMS, AND METHODS FOR RAPID CANCER DETECTION

Presented herein are systems, methods, and apparatus that analyze molecular imprints for detecting cancerous cells. Embodiments of the present disclosure include systems, methods, and apparatus that analyze metabolic imprints of cells for cancer detection. In certain embodiments, the methods/systems comprise extracting thermal and thermodynamic quantities and properties from the molecular imprints. The thermal/thermodynamic quantities and/or further-processed quantities can be mapped on a universal cancer diagnostic scale for disease stratification, thereby providing/determining a normality status of the subject cells. 1(i) accessing an image in a database;(e.g., selecting an area to be analyzed in the image) (ii) identifying, by a processor of a computing device, one or more cells in the image;(iii) segmenting, by the processor, each of the one more cells into a nucleus area and a cellular area;(iv) for each of the one or more cells, extracting, by the processor, an information surface value associated with a nuclear contrast feature (e.g., temperature difference between the nucleus area and the cellular area, e.g., contrast difference between the nucleus area and the cellular area), and a nuclear area feature (e.g., a ratio of a nucleus area to a nuclear volume projection)(e.g., wherein the nuclear contrast feature and/or the nuclear area feature are obtained at a wavelength corresponding to a maximal radiant power of the naturally emitted IR from one or more cells),(e.g., wherein the nuclear contrast feature is an averaged value of one or more nuclear contrast features obtained at one or more wavelengths of naturally emitted IR from one or more cells),(e.g., wherein the nuclear area feature is an averaged value of one or more nuclear area features obtained at one or more wavelengths of the naturally emitted IR from one or more cells);(v) calculating, by the processor, a specificity index, a log thermal capacity and a diagnostic score from the information surface ...

SEM IMAGE ENHANCEMENT METHODS AND SYSTEMS

Systems and methods for image enhancement are disclosed. A method for enhancing an image may include acquiring a first scanning electron microscopy (SEM) image at a first resolution. The method may also include acquiring a second SEM image at a second resolution. The method may further include providing an enhanced image by using the first SEM image as a reference to enhance the second SEM image. The enhanced image may be provided by using one or more features extracted from the first image to enhance the second SEM image, or using the first SEM image as a reference to numerically enhance the second SEM image. 1. A method for enhancing an image , the method comprising:acquiring a first scanning electron microscopy (SEM) image by use of an on-axis beam of a multi-beam system;acquiring a second SEM image by use of an off-axis beam of the multi-beam system; andproviding an enhanced image, the enhanced image being provided by using the first SEM image as a reference to enhance the second SEM image.2. The method of claim 1 , wherein the enhanced image is provided by using one or more features extracted from the first image to enhance the second SEM image claim 1 , or using the first SEM image as a reference to numerically enhance the second SIEM image.3. The method of claim 1 , further comprising:extracting a trained feature from the first SEM image.4. An inspection system claim 1 , comprising:a memory storing a set of instructions; and acquire a first scanning electron microscopy (SEM) image by use of an on-axis beam of a multi-beam system;', 'acquire a second SEM image by use of an off-axis beam of the multi-beam system; and', 'provide an enhanced image, the enhanced image being provided by using the first SEM image as a reference to enhance the second SEM image., 'a processor configured to execute the set of instructions to cause the inspection system to5. The system of claim 4 , wherein the enhanced image is provided by using one or more features extracted from the ...

IMAGE PROCESSING METHOD AND RECORDING MEDIUM

An image processing method includes acquiring an original image including cells as an imaging target, performing band-pass filtering on the original image with respect to a spatial frequency component within a band determined in accordance with a width of an outline of the imaging target and attenuating relatively other spatial frequency component outside the band, segmenting an image after the filtering into a first region having density higher than a threshold value and a second region, connecting the first regions sandwiching the second region by changing a portion of the sandwiched second region to the first region in an image after the segmenting, converting a closed region which is the second region in an image after the connection surrounded by the first region into the first region, and dividing the first region having a neck into regions at a position of the neck in an image after the converting. 1. An image processing method , comprising:acquiring an original image which includes a cell or a three-dimensional cell-based structure formed of a plurality of cells that aggregate as an imaging target;performing band-pass filtering on the original image with respect to a spatial frequency component within a band which is determined in accordance with a width of an outline of the imaging target and thereby attenuating relatively other spatial frequency component outside the band;segmenting an image after the filtering into a first region which is a region having density higher than a predetermined threshold value and a second region which is a region other than the first region;connecting a plurality of the first regions that sandwich the second region by changing a portion of the second region which is sandwiched by the first regions to the first region in an image after the segmenting;converting a closed region which is the second region in an image after the connection surrounded by the first region into the first region; anddividing the first region having a ...

Foreground segmentation and nucleus ranking for scoring dual ish images

The present disclosure describes a method of foreground segmentation and nucleus ranking for scoring dual ISH images. The method has been developed to better identify those nuclei, within a selected field of view, that meet the criteria for dual ISH scoring.

METHOD AND SYSTEM FOR IDENTIFICATION OF CEREBROVASCULAR ABNORMALITIES

System and methods are disclosed for identifying as well as distinguishing between cerebrovascular abnormalities. The system comprises a medical imaging device configured for capturing plurality of images of a cerebrovascular region of a subject and communicating the same to a computing device. The computing device comprises one or more modules configured for extracting features from the captured images, grouping the cerebrovascular region into one of blood vessel type and non-blood vessel type and further extracting features associated with the blood vessels to classify each point on the blood vessel into one of a non-branching, converging and diverging type. The system further comprises a user interface for interactively viewing the results of the classification thereby providing a means to identify cerebrovascular abnormalities. 1. A method for identifying cerebrovascular abnormalities in a cerebral region of a subject , the method comprising:extracting one or more CNN features of the circulatory system in the cerebral region using a virtual representation of the cerebral region;grouping, based on the CNN features, the circulatory system in the cerebral region into a blood vessel type and a non-blood vessel type;deriving one or more additional features associated with the blood vessels;classifying each point of the blood vessel into one of a non-branching, diverging and converging type using a graph convolution network;selecting a region of interest in the virtual representation of the cerebral region, wherein the region of interest comprises the one or more points of the blood vessel; andidentifying the cerebrovascular abnormalities using the extracted one or more features associated with the blood vessel or the classification of the blood vessel or both at the region of interest.2. The method as claimed in claim 1 , wherein the cerebrovascular abnormalities identified include one of developmental venous anomaly or cerebral venous malformations.3. The method as ...

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 ...

GRAIN QUALITY MONITORING

A method and non-transitory computer-readable medium capture an image of bulk grain and apply a feature extractor to the image to determine a feature of the bulk grain in the image. For each of a plurality of different sampling locations in the image, based upon the feature of the bulk grain at the sampling location, a determination is made regarding a classification score for the presence of a classification of material at the sampling location. A quality of the bulk grain of the image is determined based upon an aggregation of the classification scores for the presence of the classification of material at the sampling locations. 1. A method comprising:capturing an image of bulk grain;applying a feature extractor to the image to determine a feature of the bulk grain in the image;for each of a plurality of different sampling locations within the image, determining, based upon the feature of the bulk grain at the sampling location, a classification score for presence of a classification of material at the sampling location;outputting a signal indicating quality of the bulk grain of the image based upon an aggregate of the classification scores of the different sampling locations; andautomatically adjusting operational settings of a harvester based upon the signal as the harvester is traversing a field and as the bulk grain is being harvested by the harvester from plants in a growing medium.2. The method of comprising applying a plurality of feature extractors to the image to determine a plurality of features of the bulk grain in the image claim 1 , wherein the classification score for the presence of the classification of material at the sampling location is based upon the plurality of features of the bulk grain at the sampling location.3. The method of further comprising:segmenting the image into segments defined by presumed boundaries of individual bulk grain elements;determining a feature of each segment, wherein the classification score for the presence of the ...

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.

SYSTEMS FOR CELL SHAPE ESTIMATION

The present disclosure is directed, among other things, to automated systems and methods for analyzing, storing, and/or retrieving information associated with biological objects including lymphocytes. In some embodiments, a shape metric is derived for each detected and segmented lymphocyte and the shape metric is stored along with other relevant data. 1. A system for processing image analysis data derived from an image of a biological sample stained for a presence of at least one lymphocyte biomarker , the system comprising: (i) one or more processors , and (ii) at least one memory coupled to the one or more processors , the at least one memory to store computer-executable instructions that , when executed by the one or more processors , cause the system to perform operations comprising:detecting lymphocytes in the image of the stained biological sample;identifying outlines of the detected lymphocytes by segmenting the detected lymphocytes from other cells within the image;deriving a shape metric based on the identified outlines of each of the detected lymphocytes;associating the derived shape metrics with location information for each of the detected lymphocytes;comparing a value of each of the derived shape metrics to a predetermined threshold value for the derived shape metric; andassigning a predictive cell motility label to each of the detected lymphocytes based on the comparison.2. The system of claim 1 , wherein the shape metric is selected from the group consisting of a minor axis/major axis aspect ratio claim 1 , an eccentricity parameter claim 1 , a circularity parameter claim 1 , a roundness parameter claim 1 , and a solidity parameter.3. The system of claim 2 , wherein the minor axis/major axis aspect ratio is derived by: (i) fitting an ellipse to the outline of each of the segmented lymphocytes; (ii) calculating a length of the fitted ellipse's minor axis and major axis; and (iii) calculating an aspect ratio between the calculated lengths of the minor ...

CELL ANALYSIS DEVICE AND CELL ANALYSIS METHOD

For the purpose of counting the number of cells within a culture container readily and accurately, a cell analysis device includes a cell-image acquiring unit that acquires an image of cells within a culture container in which the cells are cultured, a usability determining unit that determines whether or not the image acquired by the cell-image acquiring unit is usable, a number-of-cells counting unit that counts the number of cells within the image determined as being usable by the usability determining unit, and a number-of-cells calculating unit that calculates the number of cells within the culture container based on the number of cells counted by the number-of-cells counting unit. 1. A cell analysis device comprising:a cell-image acquiring unit that acquires an image of cells within a culture container in which the cells are cultured;a usability determining unit that determines whether or not the image acquired by the cell-image acquiring unit is usable;a number-of-cells counting unit that counts the number of cells within the image determined as being usable by the usability determining unit; anda number-of-cells calculating unit that calculates the number of cells within the culture container based on the number of cells counted by the number-of-cells counting unit.2. The cell analysis device according to claim 1 ,wherein the usability determining unit detects blurriness in the image and determines that the image is usable if the image has no blurriness.3. The cell analysis device according to claim 1 ,wherein the usability determining unit detects whether or not an edge of the culture container appears in the image and determines that the image is usable if the edge of the culture container does not appear in the image.4. The cell analysis device according to claim 1 ,wherein the usability determining unit detects whether or not an exterior of the culture container appears in the image and determines that the image is usable if the exterior of the culture ...

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 ( ...

Identifying and Excluding Blurred Areas of Images of Stained Tissue To Improve Cancer Scoring

A method for identifying blurred areas in digital images of stained tissue involves artificially blurring a learning tile and then training a pixel classifier to correctly classify each pixel as belonging either to the learning tile or to a blurred copy. A learning tile is first selected from a digital image of stained tissue. The learning tile is copied and blurred by applying a filter to each pixel. The pixel classifier is trained to correctly classify each pixel as belonging either to the learning tile or to the blurred, copied learning tile. The pixel classifier then classifies each pixel of the entire digital image as most likely resembling either the learning tile or the blurred learning tile. The digital image is segmented into blurred and unblurred areas based on the pixel classification. The blurred areas and the unblurred areas of the digital image are identified on a graphical user interface. 117-. (canceled)18. A method comprising:selecting a learning region of a digital image of a slice of tissue from a cancer patient that has been stained using a biomarker, wherein the digital image comprises pixels, wherein each of the pixels has a color defined by pixel values, wherein a portion of the pixels exhibits the color stained using the biomarker, and wherein the learning region includes a first subregion and a second subregion;distorting the second subregion of the learning region by applying a filter to the pixel values of each pixel of the second subregion so as artificially to blur the second subregion;generating a pixelwise descriptor by analyzing and comparing the pixel values of each pixel of the learning region with the pixel values of neighboring pixels at predetermined offsets from each analyzed pixel, wherein the pixelwise descriptor is trained to indicate based on the comparing with neighboring pixels that each pixel of the learning region most likely belongs either to an unblurred class of pixels such as those in the first subregion or to a ...

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,', ' ...

AUTOMATED STEREOLOGY FOR DETERMINING TISSUE CHARACTERISTICS

Systems and methods for automated stereology are provided. In some embodiments, an active deep learning approach may be utilized to allow for a faster and more efficient training of a deep learning model for stereology analysis. In other embodiments, existing deep learning models for stereology analysis may be re-tuned to develop greater accuracy for a given data set of interest, either with or without an active deep learning approach. A method can include: capturing a data set including a stack of images of a three-dimensional (3D) object; determining whether an existing deep learning model is appropriate for use on the stack of images (or for re-tuning); performing pre-processing on the data set; performing a training of a deep learning model; applying the deep learning model to obtain a confidence score for each label of the data set; reviewing, by a user, at least some labels in the active set to verify whether the label displays sufficient agreement with an expected result, and moving only those that display sufficient agreement to a training set; and performing a stereology analysis using the trained deep learning model. 1. A method of performing computerized stereology , the method comprising:obtaining a data set comprising a stack of images representing sections of a three-dimensional (3D) object;pre-processing the stack of images using segmentation and focus algorithms;determining whether an existing deep learning model matches the stack of images; (i) training a deep learning model using a training set of the pre-processed stack of images, wherein the training of the deep learning model results in an initial trained deep learning model;', '(ii) applying the initial trained deep learning model to at least a portion of an active set of the stack of images, and computing at least one confidence score for at least one predicted label of each image of the portion of the active set;', '(iii) displaying to a user those images of the active set having a confidence ...

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 ...

AUTOMATIC NUCLEI SEGMENTATION IN HISTOPATHOLOGY IMAGES

Provided herein are systems and computer-implemented methods for quantitative analyses of tissue sections (including, histopathology samples, such as immunohistochemically labeled or H&E stained tissue sections), involving automatic unsupervised segmentation of image(s) of the tissue section(s), measurement of multiple features for individual nuclei within the image(s), clustering of nuclei based on extracted features, and/or analysis of the spatial arrangement and organization of features in the image based on spatial statistics. Also provided are computer-readable media containing instructions to perform operations to carry out such methods. A quantitative image analysis pipeline for tumor purity estimation is also described 1. A system , comprising:an image-capture device configured to capture an image of a cell population, the image comprising input-pixel values of respective pixels of the image; and determine a feature image based at least in part on the input-pixel values, the feature image comprising per-pixel feature values associated with respective pixels of the pixels of the image;', 'determine a plurality of clusters based at least in part on the feature image, each cluster of the plurality of clusters associated with at least some of the pixels of the image;', 'select a first cluster of the plurality of clusters, the first cluster associated with nuclei of cells in the cell population;', 'determine a nuclei mask image representing pixels of the image associated with the first cluster; and', 'determine a plurality of per-nucleus mask images by applying morphological operations to the nuclei mask image., 'a control unit operatively connected with the image-capture device and configured to2. The system of claim 1 , wherein the image-capture device and/or the control unit is configured to carry out one or more operations automatically.3. The system of claim 1 , wherein the image of the cell population is a histopathology image.4. The system of claim 3 , ...

SYSTEM AND METHOD FOR AUTOMATED STEREOLOGY OF CANCER

Systems and methods for applying an ensemble of segmentations to microscopy images of a tissue sample to determine if the tissue sample is representative of cancerous tissue. The ensemble of segmentations is applied to a plurality of greyscale or color microscopy images to generate a final image level segmentation and a final blob level segmentation. The final image level segmentation and final blob level segmentation are used to calculate a mean nuclear volume to determine if the tissue sample is representative of cancerous tissue. 1. An automated stereology system configured to determine whether a tissue sample is representative of cancerous tissue , the system comprising an electronic processor configured to:apply an ensemble of segmentations to a plurality of greyscale images to generate a set of segmented images for each of the plurality of greyscale images;determine if a segmented image of the set of segmented images comprises a blob that is larger than a predetermined maximum blob size, and reject the set of segmented images if a segmented image of the set of segmented images comprises a blob that is larger than a predetermined maximum blob size;determine if at least half of the segmented images of the set of segmented images are similar, reject the set of segmented images if at least half of the segmented images of the set of segmented images are not similar, and accept the set of segmented images if at least half of the segmented images of the set of segmented images are similar to generate a set of accepted image level segmented images;determine if a blob is present in at least half of the segmented images of the set of segmented images, reject the set of segmented images if the blob is not present in at least half of the segmented images of the set of segmented images, and accept the set of segmented images if the blob is present in at least half of the segmented images of the set of segmented images to generate a set of accepted blob level segmented ...

METHOD AND SYSTEM FOR GENERATING A SYNTHETIC IMAGE OF A REGION OF AN OBJECT

A method for generating a synthetic image of a region of an object, includes: generating, by a charged particle microscope, a charged particle microscope image of the region of the object; calculating a sparse representation of the charged particle microscope image; wherein the sparse representation of the charged particle microscope image comprises multiple first atoms; generating the synthetic image of the region, wherein the synthetic image of the region is formed from multiple second atoms; wherein the generating of the synthetic image of the region is based on a mapping between the multiple first atoms and the multiple second atoms; wherein the charged particle microscope image and the multiple first atoms are of a first resolution; and wherein the synthetic image of the region and the multiple second atoms are of a second resolution that is finer than the first resolution. 1. A method for generating a synthetic image of a region of an object , the method comprising:generating, by a charged particle microscope, a charged particle microscope image of the region of the object;calculating a sparse representation of the charged particle microscope image; wherein the sparse representation of the charged particle microscope image comprises multiple first atoms;generating the synthetic image of the region, wherein the synthetic image of the region is formed from multiple second atoms;wherein the generating of the synthetic image of the region is based on a mapping between the multiple first atoms and the multiple second atoms;wherein the charged particle microscope image and the multiple first atoms are of a first resolution; andwherein the synthetic image of the region and the multiple second atoms are of a second resolution that is finer than the first resolution.2. The method according to wherein the generating of the charged particle microscope image is preceded by performing a dictionary learning process for generating at least one dictionary that comprises the ...

Optimized sub-sampling in an electron microscope

Disclosed are methods for optimized sub-sampling in an electron microscope. With regard at least to utilization of electron dose budgets, of time for acquisition of measurements, and of computing/processing capabilities, very high efficiencies can be achieved by informing and/or adapting subsequent sub-sampling measurements according to one or more earlier-acquired sparse datasets and/or according to analyses thereof.

SYSTEM, METHOD, AND COMPUTER-ACCESSIBLE MEDIUM FOR PROCESSING BRAIN IMAGES AND EXTRACTING NEURONAL STRUCTURES

An exemplary system, method, and computer-accessible medium for generating an image(s) of an anatomical structure(s) in a biological sample(s) can include receiving first wide field microscopy imaging information for the biological sample, generating second imaging information by applying a gradient-based distance transform to the first imaging information, and generating the image(s) based on the second imaging information. The second imaging information can be generated by applying an anisotropic diffusion procedure to the first imaging information. The second imaging information can be generated by applying a curvilinear filter and a Hessian-based enhancement filter after the application of the gradient-based distance transform. The second information can be generated by applying (i) a tube enhancement procedure or (ii) a plate enhancement procedure after the application of the gradient-based distance transform. 1. A non-transitory computer-accessible medium having stored thereon computer-executable instructions for generating at least one image of at least one anatomical structure in at least one biological sample , wherein , when a computing arrangement executes the instructions , the computing arrangement is configured to perform procedures comprising:receiving first wide field microscopy imaging information for the biological sample;generating second imaging information by applying a gradient-based distance transform to the first imaging information; andgenerating the at least one image based on the second imaging information.2. The computer-accessible medium of claim 1 , wherein the computer arrangement is further configured to generate the second imaging information by applying an anisotropic diffusion procedure to the first imaging information.3. The computer-accessible medium of claim 1 , wherein the computer arrangement is configured to generate the second imaging information by applying a curvilinear filter and a Hessian-based enhancement filter after ...

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.

Method and System for Imaging and Analysis of a Biological Specimen

The present disclosure provides methods of preparing a biological specimen for imaging analysis, comprising fixing and clearing the biological specimen and subsequently analyzing the cleared biological specimen using microscopy. Also included are methods of quantifying cells, for example, active populations of cells in response to a stimulant. The present disclosure also provides devices for practicing the described methods. A flow-assisted clearing device provides rapid clearing of hydrogel-embedded biological specimens without the need of specialized equipment such as electrophoresis or perfusion devices.