Automatic assessment of histological indices

An apparatus for calculating a histological index from an image comprises a first neural network configured to receive the image as an input, to detect one or more inflammatory infiltrate regions in the image, and to output the detected inflammatory infiltrate regions as a first output. The apparatus further comprises a second neural network configured to receive the image as an input, detect one or more cell regions in the image and classify the detected cell regions wherein classifying the detected cell regions comprises determining whether each detected cell region corresponds to one or more predetermined cell types. The image is then filtered to remove detected cell regions which are determined not to correspond to one or more of the predetermined cell types and the filtered image is output as a second output. The apparatus also comprises a third neural network configured to receive the image as an input, to detect one or more granular tissue regions in the image, and to output the ...

METHOD FOR DETECTING OBJECT IMAGE USING HYPERSPECTRAL IMAGING BY BAND SELECTION

The present application related to a method for detecting image using hyperspectral imaging by band selection. Firstly, obtaining a hyperspectral imaging information according to a reference image, hereby, obtaining corresponded hyperspectral image from an input image, and obtaining corresponded feature values by band selection for operating Principal components analysis to simplify feature values. Then, obtaining feature images by Convolution kernel, and then positioning an image of an object under detected by a default box and a boundary box from the feature image. By Comparing with the esophageal cancer sample image, the image of the object under detected is classifying to an esophageal cancer image or a non-esophageal cancer image. Thus, detecting an input image from the image capturing device by the convolutional neural network to judge if the input image is the esophageal cancer image for helping the doctor to interpret the image of the object under detected.

BATCH EFFECT MITIGATION IN DIGITIZED IMAGES

The present disclosure relates to a non-transitory computer-readable medium storing computer-executable instructions that, when executed, cause a processor to perform operations. The operations include extracting one or more image characterization metrics from respective ones of a plurality of digitized images within an imaging data set. The plurality of digitized images have batch effects. The operations further include identifying a plurality of batch effect groups of the digitized images using the one or more image characterization metrics, and dividing the plurality of batch effect groups between a training set and/or a validation set. The training set and/or the validation set include some of the plurality of digitized images associated with respective ones of the plurality of batch effect groups.

SYSTEMS AND METHODS FOR PROCESSING ELECTRONIC IMAGES FOR COMPUTATIONAL DETECTION METHODS

Segmentation of histological tissue images into glandular structures for prostate cancer tissue classification

The method according to the invention utilizes a color decomposition of histological tissue image data to derive a density map. The density map corresponds to the portion of the image data that contains the stain/tissue combination corresponding to the stroma, and at least one gland is extracted from said density map. The glands are obtained by a combination of a mask and a seed for each gland derived by adaptive morphological operations, and the seed is grown to the boundaries of the mask. The method may also derive an epithelial density map used to remove small objects not corresponding to epithelial tissue. The epithelial density map may further be utilized to improve the identification of glandular regions in the stromal density map. The segmented gland is extracted from the tissue data utilizing the grown seed as a mask. The gland is then classified according to its associated features.

METHOD FOR THE DETECTION, SEGMENTATION AND MORPHOLOGICAL MAPPING ON NEURAL CELL IMAGES OF THE WHOLE BRAIN

A method and apparatus for morphological analysis of neural cells may include receiving an immunohistochemistry (IHC) image of one or more neural cells; detecting the one or more neural cells in the IHC image using a first neural network, wherein the detecting includes identifying boundaries of the one or more neural cells; generating a segmentation cell mask using a second neural network based on the identified boundaries of the one or more neural cells; and classifying the one or more neural cells based on the generated segmentation cell mask using a trained classification model. 1. A method for morphological analysis of neural cells , the method executed by one or more processors , the method comprising:receiving an immunohistochemistry (IHC) image of one or more neural cells;detecting the one or more neural cells in the IHC image using a first neural network, wherein the detecting includes identifying boundaries of the one or more neural cells;generating a segmentation cell mask using a second neural network based on the identified boundaries of the one or more neural cells; andclassifying the one or more neural cells based on the generated segmentation cell mask using a trained classification model.2. The method of claim 1 , further comprising:reconstructing a cell mapping based on cell location of the one or more neural cells, activation classes of the one or more neural cells, morphology of the one or more neural cells, and morphometric parameters of the one or more neural cells.3. The method of claim 1 , wherein the first neural network and the second neural network are trained on a same training data and rule-based ground truths.4. The method of claim 1 , wherein prior to the detecting the one or more neural cells in the IHC image claim 1 , the IHC image is preprocessed claim 1 , the preprocessing comprising:removing a background from the IHC image;converting the IHC image into a grayscale image, wherein the converting includes intensity inversion to reduce ...

Method for Detecting Particles Using Structured Illumination

A particle detection method detects presence and location of particles on a target using measured signals from a plurality of structured illumination patterns. The particle detection method uses measured signals obtained by illuminating the target with structured illumination patterns to detect particles. Specifically, the degree of variation in these measured signals in raw images is calculated to determine whether a particle is present on the target at a particular area of interest.

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.

AUTOMATIC SELECTION OF STRUCTURES-OF-INTEREST FOR LAMELLA SAMPLE PREPARATION

Disclosed herein are scientific instrument support systems, as well as related methods, computing devices, and computer-readable media. For example, in some embodiments, a support apparatus is provided for a charged particle microscope. The support apparatus is configured to apply automated image processing to an image representing a lamella sample to segment the image into a plurality of segmented classes. The support apparatus is also configured to identify, based on the plurality of segmented classes, a subset of candidate structures-of-interest in the lamella sample and to select, from the subset of candidate structures-of-interest in the lamella sample, a selected structure-of-interest for milling. The support apparatus is also configured to set, based on the selected structure-of-interest for milling, at least one milling parameter for the scientific instrument. An automated method performed via a computing device for providing such scientific instrument support is also provided.

Systems and methods for processing images to determine image-based computational biomarkers from liquid specimens

A method of using a machine learning model to output a task-specific prediction may include receiving a digitized cytology image of a cytology sample and applying a machine learning model to isolate cells of the digitized cytology image. The machine learning model may include identifying a plurality of sub-portions of the digitized cytology image, identifying, for each sub-portion of the plurality of sub-portions, either background or cell, and determining cell sub-images of the digitized cytology image. Each cell sub-image may comprise a cell of the digitized cytology image, based on the identifying either background or cell. The method may further comprise determining a plurality of features based on the cell sub-images, each of the cell sub-images being associated with at least one of the plurality of features, determining an aggregated feature based on the plurality of features, and training a machine learning model to predict a target task based on the aggregated feature.

Image analysis apparatus, image analysis method, and image analysis program

An image analysis apparatus including a processor configured to: acquire a first fluorescence image indicating an observation target including a plurality of types of cells, each of which has a first region stained by first staining, and a second fluorescence image indicating the observation target in which a second region of a specific cell among the plurality of types of cells is stained by second staining different from the first staining; determine whether or not the first region is included in the second region in a superimposed image obtained by superimposing the first fluorescence image and the second fluorescence image and acquire a first determination result for each of the first regions; and determine the type of cell included in the observation target on the basis of the first determination result.

Condensation Countermeasures for Airborne Particle Detectors

Condensation associated with the collection and identification of airborne particles is detected. Upon the detection, one or more condensation countermeasures are triggered to address the condensation.

DETERMINING INTERACTIONS BETWEEN CELLS BASED ON FORCE SPECTROSCOPY

Methods and systems for determining interaction between cells are described wherein the method includes determining or receiving a sequence of images representing manipulating first cells, in a holding space, the holding space including a functionalized wall comprising second cells, the manipulating including settling of the first cells onto the functionalized wall and applying a force on the settled first cells; detecting groups of pixels representing first cells in first images representing the settling of the first cells onto the functionalized wall; tracking locations of detected first cells in the first images; and, determining settling events, a settling event being determined if a cell in a first image is not distinguishable from background of the first image, the location in the image at which a cell settling event is detected defining a cell settling location; detecting groups of pixels representing cells in second images captured during the application of the force and tracking ...

VIRTUAL MICROSCOPY

RAPID DETERMINATION OF MICROBIAL GROWTH AND ANTIMICROBIAL SUSCEPTIBILITY

This disclosure is related to methods for rapid determination of microorganism growth and antimicrobial agent susceptibility and/or resistance.

MACHINE LEARNING USING DISTANCE-BASED SIMILARITY LABELS

Selecting locations for base-calling

Methods (300) and systems (110) for image analysis are provided, and in particular for identifying a set of base-calling locations in a flow cell for DNA sequencing. These include capturing (310) flow cell images (200) after each sequencing step performed on the flow cell (112), and identifying (320) candidate cluster centers (220A, 220B, 220C) in at least one of the flow cell images (220). Intensities are determined for each candidate cluster center (220A, 220B, 220C) in a set of flow cell images (200). Purities are determined (330) for each candidate cluster center based on the intensities. Each candidate cluster center (220A, 220B, 220C) with a purity greater than the purity of the surrounding candidate cluster centers (225A, 225B, 225C) within a distance threshold is added (330) to a template set of base-calling locations.

Method and analysis devices for classifying tissue samples

A method and analysis devices for classifying tissue samples are provided. In the method, annotated training data is generated from a known positive tissue sample and a known negative tissue sample from a patient, and the annotated training data is then used to train an automatic classifier patient-specifically. To delimit an affected tissue region of the patient, then, unknown tissue samples from the same patient are classified by the automatic classifier trained for the patient.

SYSTEM AND METHOD FOR DETECTING MICROBIAL AGENTS

A system for identifying microbial agents such as virus particles in a sample. The system includes at least one processing unit for identifying in an electron micrograph obtained from the sample a darker region and identifying virus particles within the darker region. The system can optionally include an electron microscope, a sample collector and sample treatment chamber.

ARTIFICIAL INTELLIGENCE BASED MEDICAL IMAGE AUTOMATIC DIAGNOSIS SYSTEM AND METHOD

An artificial intelligence (AI)-based medical image automatic diagnosis system and method. The method comprises: acquiring a medical microscope image and corresponding diagnostic data; annotating the medical microscope images to obtain annotation data corresponding to the medical microscope images; building a training set and a test set on the basis of the diagnostic data and annotation data corresponding to the medical microscope images; and performing training on the basis of a deep learning model to obtain the optimal AI classification model and the optimal AI semantic segmentation model to implement the automatic diagnosis of the medical microscope image of a test sample. The system and method can effectively save human resources, shorten the diagnosis time, and improve diagnosis accuracy.

MULTIPLE INSTANCE LEARNER FOR TISSUE IMAGE CLASSIFICATION

METHOD AND DEVICE FOR DETECTING A PRESENCE OF A FLUORESCENCE PATTERN TYPE ON AN ORGAN SEGMENT BY MEANS OF IMMUNOFLUORESCENCE MICROSCOPY

Vorgeschlagen wird ein Verfahren zum Detektieren wenigstens einer potentiellen Präsenz wenigstens eines Fluoreszenzmustertyps auf einem Organschnitt mittels Immunfluoreszenzmikroskopie mittels digitaler Bildverarbeitung. Es wird ein Organschnitt auf einem Objektträger bereitgestellt. Es erfolgt dann ein Inkubieren des Organschnittes mit einer flüssigen Patientenprobe, welche potenziell primäre Antikörper aufweist und mit sekundären Antikörpern, welche mit einem Fluoreszenzfarbstoff markiert sind. Es erfolgt ferner ein Erfassen eines Fluoreszenzbildes des Organschnittes. Ferner erfolgt ein Bestimmen einer Teilfläche des Fluoreszenzbildes, welche für eine Ausbildung des Fluoreszenzmustertyps relevant ist, durch Segmentieren des Fluoreszenzbildes mittels eines ersten neuronalen Netzes. Ferner erfolgt ein Bestimmen des Konfidenzmaßes, welches eine tatsächliche Präsenz des Fluoreszenzmustertyps indiziert, auf Basis des Fluoreszenzbildes mittels eines zweiten neuronalen Netzes. Ferner erfolgt ...

Device, microscope device, method, and program

Image recognition device and method

In order to select an optimal learning model for an image when inference is carried out in the extraction of a profile line using machine learning, without requiring a correct value or degree of certainty, a feature extraction learning model group containing a plurality of learning models is used for feature extraction. A recall learning model group containing recall learning models is paired with the feature extraction learning models. A feature amount extraction unit for referencing a feature extraction learning model and extracting a feature amount from input data; a data-to-data recall unit for referencing a recall learning model and outputting a recall result with the feature amount subjected to dimensional compression; and a learning model selection unit for selecting a feature extraction learning model from the feature extraction learning model group under the condition that the difference between the feature amount and the recall result is minimized are provided.

Image display method and apparatus for detecting abnormal object based on artificial intelligence, device, and medium

An image display method includes: processing a first image to obtain a first feature image, the first image being an image of a local area of a smear captured by a microscope, and the local area including multiple objects to be tested; obtaining a second feature image corresponding to the first feature image, the second feature image and the first feature image having a same size; obtaining a third feature image according to an image obtained by overlaying the first feature image and the second feature image, a feature point in the third feature image indicating a possibility that one of the multiple objects is an abnormal object; obtaining a second image according to the third feature image; and displaying the second image superimposed on the first image.

DETERMINING TISSUE CHARACTERISTICS USING MULTIPLEXED IMMUNOFLUORESCENCE IMAGING

METHOD AND DEVICE FOR EVALUATING QUALITY OF PATHOLOGICAL SLIDE IMAGE

A computing device includes at least one memory, and at least one processor configured to analyze at least one object expressed in a pathological slide image, evaluate quality of the pathological slide image based on a result of the analyzing, and perform at least one additional operation according to a result of the evaluating.

SYSTEM AND METHOD FOR REAL TIME ASSAY MONITORING

An assay monitoring system (10) and mehtod for monitoring a staining process of an assay are disclosed. The assay monitoring system (10) comprises a sample processing system (21) comprising at least one sample (50) having at least one specimen (54) undergoing a staining process in the course of an assay, an imaging system (15) positioned adjacent the sample processing system (21), the imaging system (15) configured to capture an image of the at least one sample (50) during the staining process of the specimen (54), and a controller (12) configured to control the imaging system (15) and the sample processing system (21), wherein the controller (12) is communicatively coupled to the imaging system (15) to receive the captured image of the at least one sample (50) from the imaging system (15). The controller (12) is configured to select a region of the captured image, the region including at least a portion of the specimen undergoing the assay, calculate an index value for the selected region ...

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.

Cell classification algorithm

A method of detecting a protein (e.g. receptor) on cells by obtaining spatial coordinates of the detected protein. The method comprises detecting boundaries of the cells. A data vector is constructed based on the obtained spatial coordinates and the detected boundaries. The method can use dSTORM or fPALM. A spatial distribution can be evaluated based on the obtained spatial coordinates. The spatial coordinates can be partitioned into one or more clusters at predetermined length scales by performing a spatial distribution analysis algorithm. The boundaries can be obtained by use of an optical image of the cells. A segmentation algorithm can be performed on the optical image of the cells. A border obtained by the segmentation algorithm can be extended by a predetermined distance. Colocalization analysis can be performed on an overlapping area between two cells. Principal component analysis (PCA) can be performed on the data. The cells can be classified based on a reference cell. A partitioning ...

DEEP LEARNING AUTOMATED DERMATOPATHOLOGY

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

Systems and methods for detecting complex networks in MRI image data

Systems and methods for detecting complex networks in MRI image data in accordance with embodiments of the invention are illustrated. One embodiment includes an image processing system, including a processor, a display device connected to the processor, an image capture device connected to the processor, and a memory connected to the processor, the memory containing an image processing application, wherein the image processing application directs the processor to obtain a time-series sequence of image data from the image capture device, identify complex networks within the time-series sequence of image data, and provide the identified complex networks using the display device.

IMAGING-BASED SYSTEM FOR MONITORING QUALITY OF CELLS IN CULTURE

Described herein are techniques to regulate the treatment of a culture of cells having cells corresponding to different cell categories. Some techniques may be used together with a cell imaging and incubation system including an imaging sensor configured to obtain an image of the culture and an incubator configured to incubate the culture. Regulation of treatment of the culture may be based on the processing of an image of the culture obtained by the imaging sensor of the system. The processing may include segmenting the image of the culture into multiple image segments by assigning individual pixels of the image to corresponding cell categories. According to some embodiments, the techniques include determining, based on the image segments, an amount of the culture corresponding to a particular cell category. The amount of the culture corresponding to the particular cell category may inform regulation of the treatment of the culture.

MEDICAL IMAGE ANALYSIS USING MACHINE LEARNING AND AN ANATOMICAL VECTOR

Device, system, and program

Cell counting method and system

The application relates to illuminating and imaging a biological sample using a brightfield microscope for the purpose of counting biological cells. The method comprises the steps of, positioning a sample to be viewed by way of an objective lens of the microscope on an x-y plane at an x-y position of a plurality of x-y positions, the sample comprising a plurality of biological cells, capturing and storing, one or more focal image stacks, each said focal image stack comprising a plurality of focal images of the sample positioned at the x-y position, each of the focal images in the focal image stack being captured at a different focal position of a plurality of discrete focal positions within a focal range, the focal range being located on a z plane perpendicular to the x-y plane, processing the one or more focal image stacks using a cell localisation neural network, the cell localisation neural network outputting a list of one or more cell locations, each said cell location corresponding ...

ARTIFICIAL INTELLIGENCE (AI) ASSISTED ANALYSIS OF ELECTRON MICROSCOPE DATA

A computer-implemented method, includes (i) sectioning at least a portion of a real data set of interest into a grid of chips, each chip comprising a real data subset of the portion of the real data set of interest, and receiving a few user-selected chips corresponding to ground truth examples selected from the portion of the real data set, wherein the selected chips define a support set for a few-shot class prototype, (ii) encoding a latent space representation of the support set using an embedding neural network, and defining the few-shot class prototype as a mean vector of the latent space representation of the support set, and (iii) using the embedding neural network, encoding a latent space representation of other chips of the real set data of interest, and, using a few-shot neural network, comparing the latent space representation of the other chips to the few-shot class prototype and assigning few-shot class prototype labels to the other chips based on the comparison to identify ...

Weakly supervised learning with whole slide images

Techniques are provided for determining classifications based on WSIs. A varied-size feature map is generated for each training WSI by generating a grid of patches for the training WSI, segmenting the training WSI into tissue and non-tissue areas, and converting patches comprising the tissue areas into tensors. Bounding boxes are generated based on the patches comprising tissue areas and segmented into feature map patches. A fixed-size feature map is generated based on a subset of the feature map patches. A classifier model is trained to process fixed-size feature maps corresponding to the training WSIs such that, for each fixed-size feature map, the classifier model is operable to assign a WSI-level tissue or cell morphology classification or regression based on the tensors. A classification engine is configured to use the trained classifier model to determine a WSI-level tissue or cell morphology classification or regression for a test WSI.

PATHOLOGY PREDICTIONS ON UNSTAINED TISSUE

A method for training a pattern recognizer to identify regions of interest in unstained images of tissue samples is provided. Pairs of images of tissue samples are obtained, each pair including an unstained image of a given tissue sample and a stained image of the given tissue sample. An annotation (e.g., drawing operation) is then performed on the stained image to indicate a region of interest. The annotation information, in the form of a mask surrounding the region of interest, is then applied to the corresponding unstained image. The unstained image and mask are then supplied to train a pattern recognizer. The trained pattern recognizer can then be used to identify regions of interest within novel unstained images.

METHOD AND DEVICE FOR DETECTING A PRESENCE OF A FLUORESCENCE PATTERN TYPE ON AN ORGAN SEGMENT BY MEANS OF IMMUNOFLUORESCENCE MICROSCOPY

Vorgeschlagen wird ein Verfahren zum Detektieren einer Präsenz eines Fluoreszenzmustertyps auf einem Organschnitt mittels Immunfluoreszenzmikroskopie und digitaler Bildverarbeitung. Schritte umfassen: ein Bereitstellen des Organschnittes, ein Inkubieren des Organschnittes mit einer flüssigen Patientenprobe, ein Inkubieren des Organschnittes mit sekundären Antikörpern, welche mit einem Fluoreszenzfarbstoff markiert sind, ein Erfassen eines Fluoreszenzbildes des Organschnittes in einem Farbkanal, welcher zu dem Fluoreszenzfarbstoff korrespondiert sowie ein Bereitstellen des Fluoreszenzbildes an ein neuronales Netz. Mittels des neuronalen Netzes wird gleichzeitig eine Segmentierungsinformation durch Segmentieren des Fluoreszenzbildes bestimmt und ferner wird gleichzeitig ein Konfidenzmaß bestimmt, welches eine tatsächliche Präsenz des Fluoreszenzmustertyps indiziert. Ferner erfolgt ein Bestimmen wenigstens einer Teilfläche des Fluoreszenzbildes, welche für eine Ausbildung des Fluoreszenzmustertyps ...

METHODS AND SYSTEMS FOR ASSESSING HISTOLOGICAL STAINS

SIGNAL ACQUISITION APPARATUS, SIGNAL ACQUISITION SYSTEM, AND SIGNAL ACQUISITION METHOD

A signal acquisition apparatus includes a light source that irradiates a living tissue with light and a detector that acquires signals from light returned from the living tissue to generate output data on the basis of the signals. The detector includes circuitry that acquires the signals and characteristic data regarding the signals and generates the output data on the basis of the characteristic data. The circuitry is implemented in a single semiconductor chip. Further, the present technology also provides a signal acquisition system including the signal acquisition apparatus and an analysis unit configured to analyze output data output from the image acquisition apparatus.

SYSTEMS AND METHODS FOR PROCESSING ELECTRONIC IMAGES TO INFER BIOMARKERS

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

METHODS AND APPARATUS FOR AUTOMATED SPECIMEN CHARACTERIZATION USING DIAGNOSTIC ANALYSIS SYSTEM WITH CONTINUOUS PERFORMANCE BASED TRAINING

FERTILIZED EGG DEVELOPMENT STAGE DETERMINATION METHOD, PROGRAM, RECORDING MEDIUM, IMAGING METHOD AND IMAGING APPARATUS

A fertilized egg development stage determination method according to the invention includes: obtaining image data corresponding to an image of a fertilized egg captured by optical microscope; extracting a region corresponding to the fertilized egg in the image based on the image data and obtaining an index value indicating a degree of coincidence of the region with a circle; extracting a region corresponding to a zona pellucida of the fertilized egg in the image based on the image data and obtaining an index value indicating a thickness of the region; and determining a development stage of the fertilized egg based on the index value indicating the degree of coincidence, the index value indicating the thickness and determination criteria determined in advance for a combination of these index values.

System and method for identifying and counting biological species

A system and method for generating sample data for analysis provides an image capture unit configured to capture a stack of images in image layers through a thickness of a sample with each image layer comprising pixel data in two orthogonal planes across the sample at a given sample depth. A processing unit is configured to process the captured pixel data to determine therefrom a pixel value of the energy of each pixel of the image, to select from each group of pixels through the stack of images the pixel having a highest energy and to generate an output image file comprising a set of pixel data obtained from the selected pixels. The output image file comprises for each pixel, the pixel position in the two orthogonal planes, the pixel vale and the depth position of the pixel in the image stack wherein the depth position of the selected pixel is provided in a fourth channel of the output image file, which represents a topography of a sample. An analysis unit then receives the output image ...

SYSTEM AND METHOD FOR INTERACTIVELY AND ITERATIVELY DEVELOPING ALGORITHMS FOR DETECTION OF BIOLOGICAL STRUCTURES IN BIOLOGICAL SAMPLES

A method for categorizing biological structure of interest (BSOI) in digitized images of biological tissues comprises a stage of identifying BSOIs in digitized images and further comprises presenting an image from the plurality of images that comprises at least one BSOI with high level of entropy to a user, receiving from the user input indicative of a category to be associated with the BSOI that had the high level of entropy and updating the cell categories classifier according to the category of the BSOI provided by the user.

Identifying the quality of the cell images acquired with digital holographic microscopy using convolutional neural networks

A system for performing adaptive focusing of a microscopy device comprises a microscopy device configured to acquire microscopy images depicting cells and one or more processors executing instructions for performing a method that includes extracting pixels from the microscopy images. Each set of pixels corresponds to an independent cell. The method further includes using a trained classifier to assign one of a plurality of image quality labels to each set of pixels indicating the degree to which the independent cell is in focus. If the image quality labels corresponding to the sets of pixels indicate that the cells are out of focus, a focal length adjustment for adjusting focus of the microscopy device is determined using a trained machine learning model. Then, executable instructions are sent to the microscopy device to perform the focal length adjustment.

METHOD FOR AUTOMATED UNSUPERVISED ONTOLOGICAL INVESTIGATION OF STRUCTURAL APPEARANCES IN ELECTRON MICROGRAPHS

The method is for dividing dark objects, substructures and background of an image from an electron microscope into segments by analyzing pixel values. The segments are transformed and aligned so that the transformed objects, sub-structures and background are meaningfully comparable. The transformed segments are clustered into classes which are used for ontological investigation of samples that are visualized by using electron microscopy. A triangle inequality comparison can be used to further cluster groups of objects to transfer understanding from different interactions between objects and to associate interactions with each other.

TISSUE STAINING AND SEQUENTIAL IMAGING OF BIOLOGICAL SAMPLES FOR DEEP LEARNING IMAGE ANALYSIS AND VIRTUAL STAINING

There is provided a method for training a ground truth generator machine learning model, comprising: creating a ground truth multi-record training dataset wherein a record comprises: a first image of a sample of tissue of a subject depicting a first group of biological objects, a second image of the sample depicting a second group of biological objects presenting at least one biomarker, and ground truth labels indicating a respective biological object category of a plurality of biological object categories for biological object members of the first group and the second group; and training the ground truth generator machine learning model on the ground truth multi-record training dataset for automatically generating ground truth labels selected from the plurality of biological object categories for biological objects depicted in an input set of images of a first type corresponding to the first image and a second type corresponding to the second image.

SEQUENTIAL CONVOLUTIONAL NEURAL NETWORKS FOR NUCLEI SEGMENTATION

Methods and apparatus for segmenting cell nuclei in medical images apply first and second trained machine learning algorithms. The first trained machine learning algorithm processes a medical image to provide center locations of cell nuclei depicted in the image. The second machine learning algorithm processes each of a plurality of patches of the image. Each of the patches correspond to one of the plurality of center locations. Processing each patch yields a nuclear boundary corresponding to the corresponding one of the center locations. The methods and apparatus allow associating individual pixels of the image with one or more than one nuclei and have been shown to be effective for instance segmentation of nuclei in clusters of overlapping cell nuclei.

SYSTEMS AND METHODS FOR PREDICTING PATIENT RESPONSES

Data processing systems for predicting one or more responses to a chemical substance based on biological images. At least some of the data processing systems include at least one processor configured to execute at least one artificial neural network trained to predict one or more responses to a chemical substance based on biological images. When the at least one processor is executing computer-executable instructions, the at least one processor is configured to carry out operations including processing spatially arranged image tile data through one or more data structures storing one or more portions of executable logic included in the artificial neural network to determine one or more responses of a patient to the chemical substance.

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.

Method for object detection using hierarchical deep learning

A hierarchical deep-learning object detection framework provides a method for identifying objects of interest in high-resolution, high pixel count images, wherein the objects of interest comprise a relatively a small pixel count when compared to the overall image. The method uses first deep-learning model to analyze the high pixel count images, in whole or as a patchwork, at a lower resolution to identify objects, and a second deep-learning model to analyze the objects at a higher resolution to classify the objects.

IDENTIFYING REGIONS OF INTEREST FROM WHOLE SLIDE IMAGES

METHOD FOR CLASSIFYING CELLS

METHOD FOR OBJECT DETECTION USING HIERARCHICAL DEEP LEARNING

A hierarchical deep-learning object detection framework provides a method for identifying objects of interest in high-resolution, high pixel count images, wherein the objects of interest comprise a relatively a small pixel count when compared to the overall image. The method uses first deep-learning model to analyze the high pixel count images, in whole or as a patchwork, at a lower resolution to identify objects, and a second deep-learning model to analyze the objects at a higher resolution to classify the objects.

Image capture system

An image capturing system includes a camera unit that captures an image of a cell; a display unit; an input unit that receives input from an operator regarding a selection operation on the cell; an analyzing unit that analyzes the image corresponding to the cell to which the selection operation is given and extracts a feature amount of the cell; and a specifying unit that specifies a recommended cell. The specifying unit sets a parameter that defines a range of the cell to be selected based on the feature amount of the cell to which the selection operation is given up to a first time point. In an image including the cell obtained by image capturing at a second time point later than the first time point, the specifying unit specifies the recommended cell on which the operator is prompted to make a selection based on the parameter.

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.

SYSTEMS AND METHODS FOR CELL CLASSIFICATION

The present disclosure relates to automated systems and methods adapted to quickly and accurately train a neural network to detect and/or classify cells and/or nuclei. The present disclosure also relates to automated systems and methods for using a trained cell detection and classification engine, such as one including a neural network, to classify cells within an unlabeled image.

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

Prostate cancer tissue image classification with deep learning

The method of the present invention classifies the nuclei in prostate tissue images with a trained deep learning network and uses said nuclear classification to classify regions, such as glandular regions, according to their malignancy grade. The method according to the present disclosure also trains a deep learning network to identify the category of each nucleus in prostate tissue image data, said category representing the malignancy grade of the tissue surrounding the nuclei. The method of the present disclosure automatically segments the glands and identifies the nuclei in a prostate tissue data set. Said segmented glands are assigned a category by at least one domain expert, and said category is then used to automatically assign a category to each nucleus corresponding to the category of said nucleus' surrounding tissue. A multitude of windows, each said window surrounding a nucleus, comprises the training data for the deep learning network.

Method and computer program for clustering large multiplexed spatially resolved data of a biological sample

The invention relates to a method for processing large multiplexed image data of a biological sample, the method comprising the steps of, recording a plurality of images of a biological sample, wherein the plurality of images comprises images having a different entity of the biological sample targeted with a predefined stain, determining spatially corresponding image pixels in the plurality of registered images, associating the spatially corresponding image pixels to a pixel profile, wherein each pixel profile comprises the pixel values of the spatially corresponding pixels and wherein the pixel profile is associated with the respective image coordinate of the spatially corresponding pixels, pooling the pixel profiles by means of a clustering method configured to determine pixel profiles with similar values, and thereby generating a plurality of clusters, each comprising pixel profiles with similar pixel values, for each cluster assigning a cluster value to the image coordinate of the pixel ...

Systems And Methods For Performing Segmentation Based On Tensor Inputs

An example system for performing segmentation of data based on tensor inputs includes memory storing computer-executable instructions defining a learning network, where the learning network includes a plurality of sequential encoder down-sampling blocks. A processor is configured to execute the computer-executable instructions to receive a multi-dimensional input tensor including at least a first dimension, a second dimension and a plurality of channels. The processor is also configured to process the received multi-dimensional input tensor by passing the received multi-dimensional input tensor through the plurality of sequential encoder down-sampling blocks of the learning network, and to generate an output tensor in response to processing the received multi-dimensional input tensor. The output tensor includes at least one segmentation classification.

DATA ACQUISITION DEVICE, DATA ACQUISITION METHOD, AND BIOLOGICAL SAMPLE OBSERVATION SYSTEM

To provide a data acquisition device capable of reducing the amount of data to be output when a biological sample is imaged. A data acquisition device including an imaging element including an acquisition unit that acquires image signals of a biological sample at two or more different time points, an information processing unit that extracts a feature amount from the image signals and generates data related to the biological sample on the basis of the feature amount, and an output control unit that causes the data related to the biological sample to be output to an outside of the imaging element, in which the signal acquisition unit, the information processing unit, and the output unit are arranged in a single chip.

BIOLOGICAL SUBJECT TRANSFER DEVICE

Systems and methods for quantitative phenotyping of fibrosis

Systems and methods are provided for computer aided phenotyping of fibrosis-related conditions. A digital image indicates presence of collagens in a biological tissue sample. The image is processed to quantify parameters, each parameter describing a feature of the collagens that is expected to be different for different phenotypes of fibrosis. At least some features are tissue level features that describe macroscopic characteristics of the collagens, morphometric level features that describe morphometric characteristics of the collagens, and texture level features that describe an organization of the collagens. At least some of the plurality of parameters are statistics associated with histograms corresponding to distributions of the associated parameters across at least some of the digital image. At least some of the plurality of parameters are combined to obtain one or more composite scores that quantify a phenotype of fibrosis for the biological tissue sample.

Adaptive sorting for particle analyzers

A cell sorting system that automatically generates a sorting strategy based on examples of target events provided by an operator. The target events can be selected using measurements ranging from traditional flow cytometry measurements to derived measurements that are computationally expensive to complex measurements such as images.

IDENTIFYING REGIONS OF INTEREST FROM WHOLE SLIDE IMAGES

The present application relates generally to identifying regions of interest in images, including but not limited to whole slide image region of interest identification, prioritization, de-duplication, and normalization via interpretable rules, nuclear region counting, point set registration, and histogram specification color normalization. This disclosure describes systems and methods for analyzing and extracting regions of interest from images, for example biomedical images depicting a tissue sample from biopsy or ectomy. Techniques directed to quality control estimation, granular classification, and coarse classification of regions of biomedical images are described herein. Using the described techniques, patches of images corresponding to regions of interest can be extracted and analyzed individually or in parallel to determine pixels correspond to features of interest and pixels that do not. Patches that do not include features of interest, or include disqualifying features, can be ...

Image analysis system and method of using the image analysis system

A system and method for analyzing images using programmable device is disclosed. A sequencer operating on the non-transitory programmable device applies the first image analysis step to the first image to develop annotated training data. Specifications of the first image and the first image analysis step are developed using a graphical user interface operating on a computer. In addition, a machine learning system trainer operating on the programmable device trains an untrained machine learning system to develop a trained machine learning system. When the trained machine learning system is presented with the first image as an input, the trained machine learning system develops a prediction of the annotated training data. In addition, the sequencer analyzes a second image by undertaking a workflow, wherein the workflow is received from the computer and is specified using the graphical user interface and comprises a second image analysis step that that specifies operating the trained machine ...

Portable biological microscopic image analysis system

A portable biological microscopic image analysis system is suitable for analyzing a sample on a slide in real time, and the sample comes from an animal. The portable biological microscopic image analysis system includes a handheld electronic device and a microscope kit; the handheld electronic device includes an image capture unit, an image analysis module electrically connected to the image capture unit, and a real-time state analysis module electrically connected to the image analysis module; the microscope kit is detachably mounted on the handheld electronic device; wherein the image capture unit is used to obtain an image related to the sample through the microscope kit, the image analysis module is used to obtain cell information corresponding to the animal according to the image, and the real-time state analysis module is used to obtain state information corresponding to the animal according to the cell information.

TESTING APPARATUS AND TESTING METHOD

Provided is a technique for preventing erroneous recognition of a fine particle region from a captured image of fine particles. A fine particle testing apparatus of the present disclosure includes: an imaging part capturing a first fine particle image of a well that holds a liquid containing fine particles; an image processor executing a process of generating a second fine particle image by extracting a contour of the first fine particle image, a process of performing a logical operation between the first fine particle image and the second fine particle image, a process of calculating a feature amount of the fine particles based on a result of the logical operation, and a process of determining growth of the fine particles in the well based on the calculated feature amount; and an output part outputting a result of the determination. 1. An testing apparatus comprising:an imaging part capturing a first fine particle image of a well that holds a liquid containing fine particles;an image processor executing a process of generating a second fine particle image by extracting a contour of the first fine particle image, a process of performing a logical operation between the first fine particle image and the second fine particle image, a process of calculating a feature amount of the fine particles based on a result of the logical operation, and a process of determining growth of the fine particles in the well based on the calculated feature amount; andan output part outputting a result of the determination.2. The testing apparatus according to claim 1 , whereinthe image processor calculates an area of a region where the fine particles are present in the first fine particle image as the feature amount of the fine particles.3. The testing apparatus according to claim 1 , whereinthe image processor performs a logical AND operation between the first fine particle image and the second fine particle image.4. The testing apparatus according to claim 2 , whereinthe image ...

SCALABLE AND HIGH PRECISION CONTEXT-GUIDED SEGMENTATION OF HISTOLOGICAL STRUCTURES INCLUDING DUCTS/GLANDS AND LUMEN, CLUSTER OF DUCTS/GLANDS, AND INDIVIDUAL NUCLEI IN WHOLE SLIDE IMAGES OF TISSUE SAMPLES FROM SPATIAL MULTI-PARAMETER CELLULAR AND SUB-CELLULAR IMAGING PLATFORMS

Shift invariant loss for deep learning based image segmentation

Systems and methods of improving alignment in dense prediction neural networks are disclosed. A method includes identifying, at a computing system, an input data set and a label data set with one or more first parts of the input data set corresponding to a label. The computing system processes the input data set using a neural network to generate a predicted label data set that identifies one or more second parts of the input data set predicted to correspond to the label. The computing system determines an alignment result using the predicted label data set and the label data set and a transformation of the one or more first parts, including a shift, rotation, scaling, and/or deformation, based on the alignment result. The computing system computes a loss score using the transformation, label data and the predicted label data set and updates the neural network based on the loss score.

METHOD FOR ANALYZING A STRUCTURE WITHIN A FLUIDIC SYSTEM

MATURITY CLASSIFICATION OF STAINED RETICULOCYTES USING OPTICAL MICROSCOPY

APPARATUSES, SYSTEMS AND METHODS FOR GENERATING SYNETHETTC IMAGE SETS

Deep learning based instance segmentation via multiple regression layers

Novel tools and techniques are provided for implementing digital microscopy imaging using deep learning-based segmentation and/or implementing instance segmentation based on partial annotations. In various embodiments, a computing system might receive first and second images, the first image comprising a field of view of a biological sample, while the second image comprises labeling of objects of interest in the biological sample. The computing system might encode, using an encoder, the second image to generate third and fourth encoded images (different from each other) that comprise proximity scores or maps. The computing system might train an AI system to predict objects of interest based at least in part on the third and fourth encoded images. The computing system might generate (using regression) and decode (using a decoder) two or more images based on a new image of a biological sample to predict labeling of objects in the new image.

A METHOD FOR AUTOMATED DETERMINATION OF PLATELET COUNT BASED ON MICROSCOPIC IMAGES OF PERIPHERAL BLOOD SMEARS

A method for analysing microscopic images of a blood smear allowing to determine the number of thrombocytes in a tested sample. The present disclosure uses an algorithm which allows for differentiating between platelets from other blood cells (including erythrocytes), and then counts the quantity of thrombocytes (number/μl) and determines their size (μm). The method includes providing a grayscale microscopic image of platelets, segmenting and analysing the image, wherein the step of segmentation and analysis of the image comprises analysing light regions of the image and analysing dark regions of the image, including detecting distinctive regions in the image using a maximally stable external regions algorithm; calculating for each light and dark region identified its convex hull and filtering the results obtained by shape; removing nesting regions; identifying aggregates; classifying cells into platelets and other blood constituents; and determining the number of platelets and masks thereof ...

Method and system for artificial intelligence based medical image segmentation

Methods and systems for artificial intelligence based medical image segmentation are disclosed. In a method for autonomous artificial intelligence based medical image segmentation, a medical image of a patient is received. A current segmentation context is automatically determined based on the medical image and at least one segmentation algorithm is automatically selected from a plurality of segmentation algorithms based on the current segmentation context. A target anatomical structure is segmented in the medical image using the selected at least one segmentation algorithm.

Systems and methods for computing the contributions of autofluorescence in multichannel image

Disclosed herein are systems and methods of estimating the autofluorescence (AF) signal and other non-target signals in each channel of a multi-channel image of a biological sample that is stained with one or more fluorescent labels. In some embodiments, the estimated autofluorescence signal can then be subtracted or masked from the multi-channel image. In some embodiments, the autofluorescence-removed multichannel image can then be use for further processing (e.g. image analysis, etc.).

METHOD AND DEVICE FOR DETECTING A PRESENCE OF A FLUORESCENCE PATTERN TYPE ON AN ORGAN SEGMENT BY MEANS OF IMMUNOFLUORESCENCE MICROSCOPY

METHOD OF EVALUATING AT LEAST ONE IMAGE OF AT LEAST ONE MICRO-ARRAY

A computer-implemented method of evaluating at least one image (110) of at least one micro-array (112) on a substrate (114), specifically of at least one microscopic fluorescence image (116) of the micro-array (112), the method comprising: i. at least one retrieving step, comprising retrieving the at least one image (110) and retrieving at least one set of deposition data comprising information on a deposition of spots (120) of the micro-array (112) on the substrate (114); ii. at least one pattern recognition step for automatically detecting a printing pattern (130) of the micro-array (112) in the image (110), comprising ii.1. generating a set of test patterns (132) comprising a plurality of test patterns (132), by using the set of deposition data, ii.2. comparing the set of test patterns (132) with information retrieved from the image (110), and ii.3. generating an estimate printing pattern (148) in accordance with a degree of congruence of the test patterns (132) with the image (110), ...

REPRODUCTIVE SPECIMEN PROCESSING SYSTEMS AND METHODS

IMAGE ANALYSIS IN PATHOLOGY

AUTOMATICALLY IDENTIFYING REGIONS OF INTEREST ON IMAGES OF BIOLOGICAL CELLS

Systems and methods for processing electronic images to infer biomarkers

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

SYSTEMS AND METHODS FOR MACHINE LEARNING FEATURES IN BIOLOGICAL SAMPLES

Systems and methods for machine learning tissue classification are provided herein. Datasets for a plurality of biological samples are first generated. The dataset of each biological sample includes image data of the biological sample and molecular measurement data of the biological sample captured at a plurality of capture areas of the biological sample. The capture areas of the biological sample are registered to corresponding locations in the image data of the biological sample. Then, a machine learning module is trained with the datasets. Another dataset for another biological sample is generated (e.g., in the same or similar manner as the other datasets). And, the other dataset of the other biological sample is processed through the trained machine learning module to predict features in the other biological sample.

PHENOTYPE MEASUREMENT SYSTEMS AND METHODS

An image acquisition and analysis system are disclosed. The system enables high throughput, objective analysis of microbial samples over days or weeks. The system may accommodate upwards of twelve 96- or 384-well plates simultaneously (liquid or solid media). The system may acquire and analyze a large number of samples in a short period of time. For example, over 384 samples per minute or 18,432 samples per hour. The system hardware may include a multi-spectral imager (fluorescence and bright field detection), electro-mechanical assemblies, and an optional high-resolution stage. The system may automate image acquisition, image data processing, simplify data storage, and enable automated analysis tools to significantly reduce the manual labor and time associated with such tasks. The system may allow for quick processing and analysis of data into clear phenotypic classes. The analysis capabilities may include colony growth, colorimetry, and structural morphology assays, and automated phenotype ...

SPATIAL ANALYSIS OF ANALYTES

DIAGNOSIS AND MONITORING OF NEURODEGENERATIVE DISEASES

Digital microscope system, method for operating the same and computer program

A digital microscope system comprises an imaging device configured to generate digital image data representing a target region of an object, the target region being determined by a changeable setting of the imaging device; and a controller configured to generate monitor image data corresponding to the digital image data generated in accordance with the setting, the monitor image data being configured to be displayed as a monitor image; wherein the controller is further configured to change the setting in response to a user input; and wherein the controller is further configured to compensate for a delay in updating the monitor image data in accordance with the changed setting by storing the digital image data generated in accordance with the unchanged setting in response to the user input and generating simulation monitor image data by performing digital image processing on the stored digital image data taking into account the changed setting, the simulation monitor image data being configured ...

METHODS, APPARATUSES, AND COMPUTER PROGRAM PRODUCTS FOR ANALYZING IMAGE DATA RELATED TO FLUID SAMPLES

Example methods, apparatuses, and computer program products related to analyzing fluid samples are provided. For example, an example computer-implemented method for analyzing fluid samples includes receiving digital holography image data associated with a fluid sample in a flow chamber device; extracting, from the digital holography image data, an upper reference mark image region associated with an upper reference mark and a lower reference mark image region associated with a lower reference mark; determining a maximum focal depth and a minimum focal depth associated with the digital holography image data, respectively; focusing each of a plurality of focal depth layers associated with the digital holography image data; and extracting, from the plurality of focal depth layers, one or more region of interest (ROI) portions that are associated with the fluid sample.

METHOD FOR PROCESSING CELL IMAGE, ELECTRONIC DEVICE, AND STORAGE MEDIUM

The present disclosure provides a method for processing a cell image, an electronic device, and a storage medium. The method includes: obtaining, based on a morphological method, a target cell nucleus map according to an initial image segmentation result of a target cell image, where the target cell image is an image corresponding to at least one cell; obtaining, based on the morphological method, a target cell region contour map according to the target cell nucleus map and the initial image segmentation result; obtaining, based on the morphological method, a target cell contour map according to the target cell region contour map and the target cell nucleus map; and obtaining a target image segmentation result according to the target cell contour map and the target cell nucleus map.

PARALLEL IMAGE SEGMENTATION AND SPECTRAL ACQUISITION

A scanning microscope system configured for material analysis and mineralogy comprising a first detector and a second detector, and a data-processing system comprising a data-storage component and a segmentation component. The data-storage component is configured for providing image(s) of a sample based on first emissions from a plurality of first scan locations. The segmentation component is configured for determining at least one or a plurality of second scan locations for at least one or a plurality of region(s) of the at least one image. The second detector is configured for detecting second emissions from at least one of the second scan locations of at least one of the regions. The system is further configured for determining the second scan location(s) for the region(s) and detecting the second emissions from the at least one of the second scan locations of the at least one of the regions in parallel.

Digital histopathology and microdissection

A computer implemented method of generating at least one shape of a region of interest in a digital image is provided. The method includes obtaining, by an image processing engine, access to a digital tissue image of a biological sample; tiling, by the image processing engine, the digital tissue image into a collection of image patches; identifying, by the image processing engine, a set of target tissue patches from the collection of image patches as a function of pixel content within the collection of image patches; assigning, by the image processing engine, each target tissue patch of the set of target tissue patches an initial class probability score indicating a probability that the target tissue patch falls within a class of interest, the initial class probability score generated by a trained classifier executed on each target tissue patch; generating, by the image processing engine, a first set of tissue region seed patches by identifying target tissue patches having initial class ...

SYSTEMS AND METHODS FOR PREDICTING EXPRESSION LEVELS

Method and system for automated microbial colony counting from streaked sample on plated media

An imaging system and method for microbial growth detection, counting or identification. One colony may be contrasted in an image that is not optimal for another type of colony. The system and method provides contrast from all available material through space (spatial differences), time (differences appearing over time for a given capture condition) and color space transformation using image input information over time to assess whether microbial growth has occurred for a given sample.

MICROSCOPE SYSTEM, PROJECTION UNIT, AND IMAGE PROJECTION METHOD

A microscope system 1 includes an eyepiece 104, an objective 102 that guides light from a sample to the eyepiece 104, a tube lens 103 that is disposed on a light path between the eyepiece 104 and the objective 102 and forms an optical image of the sample on the basis of light therefrom, a projection image generation section 13 that generates projection image data representing a projection image including a first assistance image, and a projection apparatus 131 that projects the projection image onto an image plane on which the optical image is formed. The first assistance image is an image of the sample in which a region wider than the actual field of view corresponding to the optical image is seen.

Charged particle microscope scan masking for three-dimensional reconstruction

Disclosed herein are CPM support systems, as well as related apparatuses, methods, computing devices, and computer-readable media. For example, in some embodiments, a charged particle microscope computational support apparatus may include: first logic to, for each angle of a plurality of angles, receive an associated image of a specimen at the angle, and generate an associated scan mask based on one or more regions-of-interest in the associated image; second logic to, for each angle of the plurality of angles, generate an associated data set of the specimen by processing data from a scan, in accordance with the associated scan mask, by a charged particle microscope of the specimen at the angle; and third logic to provide, for each angle of the plurality of angles, the associated data set of the specimen to reconstruction logic to generate a three-dimensional reconstruction of the specimen.

Digital image analysis using multi-step analysis

Systems and methods for implementing a multi-step image recognition framework for classifying digital images are provided. The provided multi-step image recognition framework utilizes a gradual approach to model training and image classification tasks requiring multi-dimensional ground truths. A first step of the multi-step image recognition framework differentiates a first image region from a remainder image region. Each subsequent step operates on a remainder image region from the previous step. The provided multi-step image recognition framework permits model training and image classification tasks to be performed more accurately and in a less resource intensive fashion than conventional single-step image recognition frameworks.

Image processing apparatus, incubation observing apparatus, and image processing method

An image processing apparatus includes an outline extracting processing unit inputting a phase contrast image of a cell colony acquired by an observing unit, and extracting an outline of the cell colony, an extracting unit extracting a feature quantity of an outline part of the cell colony based on brightness information at an outside and brightness information at an inside of the outline on the phase contrast image, and an automatic discriminating unit automatically discriminating whether or not the cell colony is an iPS cell colony based on a discriminant criterion determined in advance and the feature quantity extracted by the extracting unit.

Alignment of an ordered stack of images from a specimen

The present invention relates to the field of alignment of an ordered stack of images from a sliced specimen. According to the present method and apparatus, the ordered stack of images is aligned by successively determining, for at least two already aligned images of the ordered stack, the respective misalignments with an unaligned image which is to be aligned next, selecting from the at least two aligned images as a reference image that aligned image with which the unaligned image has the smallest amount of misalignment, and aligning the unaligned image with the selected reference image. This is intended to provide a robust and computationally cheap aligning method and apparatus.

Automated prostate tissue referencing for cancer detection and diagnosis

This application provides to a method for identifying one or more prostate tissue samples in a database that are closest to a test prostate sample, which can be used to aid pathologists when examining prostate tissues to attain reliable and consistent diagnoses of prostate cancer. Also provided are databases and computer algorithms that can be used with such methods.

Image processing apparatus, image processing method, image processing program, and virtual microscope system

Provided is an image processing apparatus capable of analyzing a target sample image with high accuracy in line with a phenomenon occurring in the target sample. The image processing device includes: a dye spectrum storage portion ( 233 ) for storing a dye spectrum of a dye used in staining the stained sample; and an arithmetic portion ( 250 ) including: a variation characteristic calculating portion ( 2501 ) for calculating, based on the stored dye spectrum, a variation characteristic representing either a sharp or gradual change of the dye spectrum in the wavelength direction; and a dye-amount/variation-amount estimating portion ( 2503 ) for estimating, based on the stored dye spectrum and the calculated variation characteristic, a variation amount from a pixel value of each pixel forming the stained sample image based on the dye-amount and the variation characteristic, the arithmetic portion analyzing the stained sample image at least based on the variation amount.

Image-based popularity prediction

A machine may be configured to access an image of an item described by a description of the item. The machine may determine an image quality score of the image based on an analysis of the image. A request for search results that pertain to the description may be received by the machine, and the machine may present a search result that references the item's image, based on its image quality score. Also, the machine may access images of items and descriptions of items and generate a set of most frequent text tokens included in the item descriptions. The machine may identify an image feature exhibited by an item's image and determine that a text token from the corresponding item description matches one of the most frequent text tokens. A data structure may be generated by the machine to correlate the identified image feature with the text token.

System and method for automatic color segmentation and minimum significant response for measurement of fractional localized intensity of cellular compartments

A system, a method, and a programmed device for measurement of translocational activity among cellular compartments process magnified images of cellular material exposed to an agent by segmenting and compartmentalizing the images and then measuring fractional localized intensity of two or more compartments in the segmented, compartmentalized image. The measured fractional localized intensities are compared to determine translocation of cellular material among the measured components caused by the agent.

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.

Data processing apparatus, data processing method, image processing apparatus and method, and program

The present disclosure relates to a data processing apparatus and a data processing method capable of generating evaluation index data for performing accurate and detailed evaluation of cultured cardiomyocytes. A motion detecting unit divides frame image data obtained by photographing the cultured cardiomyocytes for a predetermined time into blocks and obtains motion detection data in units of blocks per each frame period. A feature amount calculating unit calculates a feature amount for each block at the same position in a frame image using the motion detection data. A classification processing unit classifies each of the blocks into any one of a plurality of classification categories using the calculated feature amount. On the basis of the classification result, evaluation index data made of individual classification result data that represent correspondences between the blocks and the classification categories is generated.

Region segmented image data creating system and feature extracting system for histopathological images

A region segmented image data creating system for histopathological images is provided. The region segmented image data creating system is capable of creating region segmented image data required to generating a region segmented image. A first bi-level image data creating section 12 creates first bi-level image data, in which nucleus regions can be discriminated from other regions, from histopathological image data. A second bi-level image data creating section 14 creates second bi-level image data, in which a background regions can be discriminated from other regions, from the histopathological image data. A three-level image data creating section 15 clarifies cytoplasm regions by computing a negative logical addition of the first bi-level image data and the second bi-level image data, and to create three-level image data as the region segmented image data.

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.

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.

Network cycle features in relative neighborhood graphs

Methods for analyzing biomedical data include: (a) obtaining macroscopic imaging data; (b) obtaining histopathological imaging data; (c) executing a parallel algorithm stored on a non-transient computer-readable medium to compute one or a plurality of network cycle features of a relative neighborhood graph derived from the histopathological imaging data; (d) registering the macroscopic imaging data and the histopathological imaging data; and (e) correlating the macroscopic imaging data and the network cycle features. Systems for analyzing biomedical data and computer readable storage media are described.

Systems and methods for performing quality review scoring of biomarkers and image analysis methods for biological tissue

Exemplary embodiments include methods, systems, and devices for enabling users to provide quality scores for indicating the quality of image analysis methods performed on images of biological tissue. An exemplary user interface displays results of an image analysis method performed on an image of biological tissue in an overlaid manner on an image of biological tissue. The exemplary user interface enable a user to provide, directly on the user interface, one or more quality scores to indicate the user's assessment of the quality of the image analysis performed on the image. Exemplary embodiments store the quality scores provided by the user in association with the image analysis method and the image of biological tissue.

Image processing device, image processing method, image processing program, and virtual microscope system

An image processing device for processing a stained sample image including hematoxylin stain is provided with a dye spectrum storage unit that stores a dye spectrum of dye used in staining, a change characteristic calculation unit that calculates a change characteristic in a wavelength direction of the dye spectrum based on the dye spectrum, a dye amount/wavelength shift amount estimation unit that estimates at least a dye amount of the hematoxylin stain and a shift amount in the wavelength direction for each pixel in the stained sample image based on the dye spectrum and the change characteristic; and a cell nucleus extraction unit that extracts a cell nucleus region of the stained sample image based on the shift amount estimated in the wavelength direction.

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.

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.

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.

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.

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.

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

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

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

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

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.

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.

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).

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.

Method for quantifying the morphological regularity degree of the pellucid zone in embryos and oocytes

The present invention refers to a method for calculating a shape factor indicative of the evenness of the pellucid zone thickness of a biological structure and to a method for evaluating the evenness of the pellucid zone thickness of a biological structure through said shape factor.

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.

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.

Analysis device, analysis method, and program

An analysis device includes a living cell feature value extraction unit configured to extract a feature value of a living cell from a captured image of the living cell, a fixed cell feature value extraction unit configured to extract a feature value of a fixed cell from a captured image of a cell that is the fixed cell of the living cell, and a computing unit configured to associate a feature value of the living cell extracted by the living cell feature value extraction unit with a feature value of the fixed cell extracted by the fixed cell feature value extraction unit.

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.

Cell observation apparatus, cell observation method, and program product

A cell observation apparatus includes a cell recognition unit that recognizes a cellular region representing a cell from cell image data acquired by imaging the cell at a plurality of time points; and a cell parameter measurement unit that measures cell parameters characteristic of the cellular region recognized by the cell recognition unit. The apparatus also includes a cell viability determination unit that determines cell viability by comparing the cell parameters measured by the cell parameter measurement unit with thresholds.

Training apparatus, image recognition apparatus, training method, and program

Provided are a training apparatus, an image recognition apparatus, a training method, and a program capable of improving the accuracy of image recognition of a photographic image using classifiers that have been trained using CG images. An intermediate feature identifying section identifies an intermediate feature. An offset feature identifying section identifies an offset feature on the basis of a CG intermediate feature and a photographic intermediate feature. A post-offset intermediate feature identifying section identifies a post-offset intermediate feature associated with a CG image on the basis of the intermediate feature associated with the CG image and the offset feature. A second-classifier training section executes training of a second classifier using the post-offset intermediate feature associated with the CG image, the second classifier being configured to receive, in a case where image recognition is performed on a photographic image, the intermediate feature associated with the photographic image.

Deep learning automated dermatopathology

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

Microscope Assembly

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

Segmentation based image transform

The present invention relates to a method for changing the appearance of an original image comprising N>1 classes of image elements, the method comprising the step of: for each pixel and for at least one subset of the original image: calculating N probability values, each probability value defining a probability for the pixel of belonging to a corresponding one of the N classes of image elements, transforming a color of the pixel by using predetermined color transforms for each of the N classes of image elements and the N probability values for the pixel.

Image processing apparatus, evaluation system, image processing program, and image processing method

An image processing apparatus includes an acquisition unit that acquires a hologram obtained by imaging a plurality of granules contained within an imaging visual field, a generation unit that generates, from the hologram, phase difference images at positions different from each other in an optical axis direction in a case in which the hologram is captured, a specifying unit that specifies a plurality of image ranges in a direction of a plane intersecting the optical axis direction, which correspond to the plurality of granules, in an averaged image obtained by averaging at least some of the phase difference images, and an extraction unit that extracts the phase difference image at a center position of a corresponding granule in the optical axis direction for each of the plurality of image ranges.

METHOD FOR PROCESSING CROSS MATCHING IMAGE BASED ON DEEP LEARNING AND APPARATUS THEREOF

The present disclosure relates to method and apparatus for processing cross matching image based on deep learning. 1. A method for processing cross matching image based on deep learning , the method comprising:a preprocessing operation of combining three class areas distinguished in an original image of a cross-matching test to create a labelled image;an operation of modelling a certain area and an uncertain area of the labelled image and creating an uncertainty-aware weight map (UAWM); andan operation of creating a classification image in which live cells and dead cells are distinguished in the original image by performing P-Net learning relative to at least one among the original image, the labelled image, and the UAWM.2. The method of claim 1 , wherein the preprocessing operation includes an operation of distinguishing a first class area which is a live cell area claim 1 , a second class area which is a dead cell area claim 1 , and a third class area which is a background area in the original image.3. The method of claim 2 , wherein the certain area is a cell inner area which is divided into the live cell area and the dead cell area in the labelled image claim 2 , andwherein the uncertain area is the remaining areas excluding the certain area in the labelled image.4. The method of claim 3 , wherein the UAWM creating operation includes:an operation of creating a certain area weight map by modeling the certain area;an operation of creating an uncertain area weight map by modeling the uncertain area; andan operation of creating the UAWM using the certain area weight map and the uncertain area weight map.5. The method of claim 4 , wherein the UAWM creating operation weights the certain area weight map and the uncertain area weight map claim 4 , and adds up the weighted certain area weight map and the weighted uncertain area weight map in order to create the UAWM.6. The method of claim 2 , wherein the classification image creating operation inputs data of the first ...

Biological tissue image processing system, and machine learning method

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

Systems and methods for counting cells

According to at least one aspect, a system configured to count cells in a vessel is provided. The system comprises an imaging system configured to image cells in the vessel and a controller coupled to the imaging system. The controller is configured to control the imaging system to capture a focused image of the cells and estimate a number of cells in the focused image. The controller is configured to control the imaging system to capture a focused image of the cells at least in part by controlling the imaging system to capture a plurality of images of the cells in a plurality of focal planes, determining an area of at least one cell in each of the plurality of images, and selecting one image from the plurality of images as the focused image using the area of the at least one cell in the plurality of images.

Quantitative analysis and visualization of spatial points

A method for analyzing spatial point patterns and visualizing the results is presented. The method includes simulating at least one point set within a region using a point process, dividing the region into a plurality of elements, determining scores for both real data and simulated data for each element by weighting the point sets within a domain of a predetermined kernel. The method further includes comparing scores for each element, computing confidence intervals for at least one confidence level having a predetermined statistical significance; and providing a visualization to identify clusters and exclusion zones.

Method of image evaluation for sim microscopy and sim microscopy method

A method of image evaluation when performing SIM microscopy on a sample includes: A) providing n raw images of the sample, which were each generated by illuminating the sample with an individually positioned SIM illumination pattern and imaging the sample in accordance with a point spread function, B) providing (S1) n illumination pattern functions, which each describe one of the individually positioned SIM illumination patterns, C) providing (S1) the point spread function and D) Carrying out an iteration method, which includes following iteration steps a) to e), as follows: a) providing an estimated image of the sample, b) generating simulated raw images, in each case by image processing of the estimated image using the point spread function and one of the n illumination pattern functions such that n simulated raw images are obtained, c) assigning each of the n simulated raw images to that of the n provided raw images which was generated by the illumination pattern that corresponds to the illumination pattern function used to generate the simulated raw image, and calculating n correction raw images by the comparison of each provided raw image with the simulated raw image assigned thereto, d) generating a correction image by combining image processing of the n correction raw images using the point spread function and the n illumination pattern functions, wherein a filtering step is carried out in each implementation of iteration step d), said filtering step suppressing a spatial fundamental frequency of the illumination pattern, and e) reconstructing the estimated image of the sample by means of the correction image and using the corrected estimated image of the sample as the estimated image of the sample in iteration step a) in the next run through the iteration.

Magnetic particle inspection device

A magnetic particle inspection device includes an image acquisition unit configured to acquire an image obtained by attaching magnetic particles to a magnetized inspection object and capturing the image of the inspection object, and a binarized image obtained by binarizing a whole or a part of the image; a region specifying unit configured to specify a magnetic particle group region based on at least one of the image and the binarized image, the magnetic particle group region containing a magnetic particle group; and a detection unit configured to detect a flaw by processing luminance information and form information with artificial intelligence obtained by supervised learning, the luminance information being obtained by performing statistical processing on luminances of a plurality of minute regions in the specified magnetic particle group region, and the form information being information regarding a form of the magnetic particle group that is obtained from the binarized image.

Apparatus and method of user interaction

A device for interaction with a user is provided. The device includes an image input controller operable to receive a video image comprising an image of at least the user's face. The device also includes an analysis processor such as a CPU, which is adapted to analyze the video image, and to classify the user as belonging to a respective demographic group in dependence upon analysis of the image of at least the user's face. The device also includes an interface processor, which may also be the CPU, and which is adapted to generate a user interface for output to the user. The interface processor is operable to modify one or more elements of the user interface in response to the respective demographic group to which the user is classified as belonging.

Pose tracker with multi threaded architecture

Tracking pose of an articulated entity from image data is described, for example, to control a game system, natural user interface or for augmented reality. In various examples a plurality of threads execute on a parallel computing unit, each thread processing data from an individual frame of a plurality of frames of image data captured by an image capture device. In examples, each thread is computing an iterative optimization process whereby a pool of partially optimized candidate poses is being updated. In examples, one or more candidate poses from an individual thread are sent to one or more of the other threads and used to replace or add to candidate poses at the receiving thread(s).

Stain normalization for automated whole-slide image classification

Techniques for stain normalization image processing for digitized biological tissue images are presented. The techniques include obtaining a digitized biological tissue image; applying to at least a portion of the digitized biological tissue image an at least partially computer implemented convolutional neural network trained using a training corpus including a plurality of pairs of images, where each pair of images of the plurality of pairs of images includes a first image restricted to a lightness axis of a color space and a second image restricted to at least one of: a first color axis of the color space and a second color axis of the color space, such that the applying causes an output image to be produced; and providing the output image.

Image quality for diagnostic resolution digital slide images

An improved diagnostic resolution of digital slide images is obtained by scanning a first digital slide image at diagnostic resolution that is then deconvolved into separate images with one stain per image. The single stain images are then enhanced with image adjustments and/or processed with image analysis algorithms. The resulting single image data sets from the image analysis algorithms can then be stored. Additionally, the resulting enhanced single images can be recombined into a second digital slide image at diagnostic resolution that is also enhanced.

METHOD AND SYSTEM FOR PREDICTING FAILURE OF MINING MACHINE CROWD SYSTEM

Methods for monitoring a machine are described. In one aspect, a method includes receiving information on a plurality of events associated with the machine, determining a severity value for at least one event of the plurality of events, the severity value based on at least one of a safety value, a hierarchy value, a time-to-repair value, and a cost-of-repair value, and outputting an alert includes the severity value if the severity value exceeds a predetermined threshold associated with the at least one event. Systems and machine-readable media are also described.

Methods for Preparing Data from Tissue Sections for Machine Learning Using Both Brightfield and Fluorescent Imaging

In digital pathology, obtaining a labeled data set for training, testing and/or validation of a machine learning model is expensive, because it requires manual annotations from a pathologist. In some cases, it can be difficult for the pathologist to produce correct annotations. The present invention allows the creation of labeled data sets using fluorescent dyes, which do not affect the appearance of the slide in the brightfield imaging modality. It thus becomes possible to add correct annotations to a brightfield slide without human intervention.

LABEL-FREE ASSESSMENT OF BIOMARKER EXPRESSION WITH VIBRATIONAL SPECTROSCOPY

The present disclosure relates to automated systems and methods for predicting an expression of one or more biomarkers in a sample of a biological specimen. In some embodiments, the sample is one which has an unknown fixation status, or one where the duration of fixation to which the sample was subject is unknown. In some embodiments, the predicted expression is a quantitative estimation of the percent positivity of one or more biomarkers. In other embodiments, the predicted expression is a quantitative estimation of the staining intensity of one or more biomarkers. In some embodiments, the systems and methods utilize a trained biomarker expression estimation engine which has been trained with a plurality of training samples, where the trained biomarker expression estimation engine is adapted to derive biomarker expression features from the sample. 1. A system for predicting an expression of one or more biomarkers in an test biological specimen the system comprising: (i) one or more processors , and (ii) one or more memories coupled to the one or more processors , the one or more memories to store computer-executable instructions that , when executed by the one or more processors , cause the system to perform operations comprising:a. obtaining test spectral data from the test biological specimen, wherein the obtained test spectral data comprises vibrational spectral data derived from at least a portion of the biological specimen;b. deriving biomarker expression features from the obtained test spectral data using a trained biomarker expression estimation engine; andc. predicting the expression of the one or more biomarkers in the test biological specimen based on the derived biomarker expression features.2. The system of claim 1 , wherein the predicted expression of the one or more biomarkers comprises one of a predicted percent positivity or a predicted staining intensity.3. The system of claim 1 , wherein the predicted expression of the one or more biomarkers ...

SYSTEM AND METHOD FOR REAL TIME ASSAY MONITORING

A real time assay monitoring system and method can be used to monitor reagent volume in assays for fluid replenishment control, monitor assays in real-time to obtain quality control information, monitor assays in real-time during development to detect saturation levels that can be used to shorten assay time, and provide assay results before the assay is complete, enabling reflex testing to begin automatically. The monitoring system can include a real time imaging system with a camera and lights to capture images of the assay. The captured images can then be used to monitor and control the quality of the staining process in an assay, provide early assay results, and/or to measure the on-site reagent volume within the assay. 1. A system for predicting an unmasking status of a test biological specimen treated in an unmasking process , the system comprising: (i) one or more processors , and (ii) one or more memories coupled to the one or more processors , the one or more memories to store computer-executable instructions that , when executed by the one or more processors , cause the system to perform operations comprising:a. obtaining test spectral data from the test biological specimen, wherein the test spectral data comprises vibrational spectral data derived from at least a portion of the biological specimen;b. deriving unmasking features from the obtained test spectral data using a trained unmasking status estimation engine; andc. predicting the unmasking status of the test biological specimen based on the derived unmasking features.2. The system of claim 1 , wherein the unmasking status comprises one of a predicted duration of unmasking or a predicted temperature of unmasking.3. The system of claim 2 , wherein the unmasking status comprises both a predicted duration of unmasking and a predicted temperature of unmasking.4. The system of claim 2 , wherein the unmasking status further comprises an estimate of tissue quality.5. The system of claim 1 , wherein the ...

Cell Image Analysis Method and Cell Image Analyzer

A cell image analysis method includes converting a first image into a label image by performing a segmentation process to identify a region of a cell that has already started differentiation and a region of an undifferentiated cell in the first image, acquiring a shape feature amount from the label image, and determining whether or not a cell colony includes a colony region that is a candidate for a search target based on the shape feature amount and a determination criterion. 1. A cell image analysis method comprising:acquiring a first image of a cell colony including a cell having differentiation potential;converting the first image into a label image by performing a segmentation process to identify a colony region of a cell that has already started differentiation and a colony region of an undifferentiated cell in the cell colony in the first image;acquiring a shape feature amount of the cell colony from the label image;receiving an input regarding a colony region of a search target from a user using a computer;setting a determination criterion for the shape feature amount based on the user's input; anddetermining whether or not the cell colony includes a colony region that is a candidate for the search target based on the shape feature amount and the determination criterion.2. The cell image analysis method according to claim 1 , whereinthe receiving of the input regarding the colony region of the search target includes receiving an input of selection information as to whether or not the cell colony in a second image acquired in advance includes a desired colony region from the user; andthe setting of the determination criterion includes setting the determination criterion based on the received selection information.3. The cell image analysis method according to claim 2 , whereinthe setting of the determination criterion includes creating a first trained model that has acquired the determination criterion for the shape feature amount by machine learning using ...

INFORMATION PROCESSING DEVICE, INFORMATION PROCESSING METHOD, AND INFORMATION PROCESSING SYSTEM

There is provided an information processing device that includes a display control unit that controls display of image data in which a biological region is captured, an information acquisition unit that acquires first region information input with respect to the image data, and a processing unit that generates second region information on the basis of the image data, the first region information, and a fitting mode. 1. An information processing device comprising:a display control unit that controls display of image data in which a biological region is captured;an information acquisition unit that acquires first region information input with respect to the image data; anda processing unit that generates second region information on a basis of the image data, the first region information, and a fitting mode.2. The information processing device according to claim 1 , whereinthe processing unit generates a third region information on a basis of the image data, the second region information, the fitting mode, and a movable range of fitting.3. The information processing device according to claim 2 , whereinthe display control unit controls display of the third region information.4. The information processing device according to claim 3 , whereinthe display control unit switches a display target between the second region information and the third region information.5. The information processing device according to claim 3 , whereinthe display control unit causes simultaneous display of the second region information and the third region information in different display modes.6. The information processing device according to claim 2 , whereinthe processing unit selects the second region information or the third region information as data used for a predetermined process.7. The information processing device according to claim 6 , whereinthe processing unit selects the second region information or the third region information as data used for the predetermined process on a ...

System for the identification and quantification of helminth eggs in environmental samples

Process and system for identifying and quantifying helminth eggs in water, sludge, biosolid and/or excreta samples among others, from images comprising filtering the images with an anisotropic filter maintaining the borders of the images, obtaining filtered images; filtering the filtered images applying Laplacian of Gaussian detecting changes in the filtered images, and obtaining binarized images; separating the binarized images by means of a filtered distance field Watershed filter, obtaining the images; filtering the images eliminating objects by perimeter compactness, considering the size of the objects in the images filtered again and separating the differences to avoid false positives, obtaining images with identified objects; characterizing the objects identified in the images segmenting the objects by means of gray profiles; and classifying the characterized objects according to a statistic classifier for identifying and quantifying different species of helminth eggs.

Methods and systems for classifying biological samples, including optimization of analyses and use of correlation

Methods, systems, and devices for classifying a biological sample that include receiving an image of a biological sample and applying one or more algorithms from a data repository to the image, generating a classification of the biological sample based on the outcome of the one or more algorithms applied to the image, and transmitting the classification for presentation on a display or via another medium. The methods, systems, and devices may also include features for developing a data master reference and/or other correlation/translation features to enable comparison of data sets from one platform to another or from one machine to another or from the same machine at different points in time.

Cytological analysis of nuclear neat-1 expression for detection of cholangiocarcinoma

A method for detecting malignancy in a biliary cytology sample. In situ hybridization is performed on the biliary cytology sample to express nuclear paraspeckle assembly transcript 1 (NEAT-1). The hybridized biliary cytology sample is imaged, and the NEAT-1 expressions are identified. A plurality of features of the identified NEAT-1 expressions are detected. The detected features of the NEAT-1 expressions are processed by a processor configured with algorithm criteria defined by a trained neural network and based on the plurality of features to provide predictions of malignancy in the sample.

Detecting Cells of Interest in Large Image Datasets Using Artificial Intelligence

A method for selecting a final model for detecting cells of interest in image datasets includes dividing a curated image dataset into a training set, a validation set, and a testing set where each image in the curated image dataset has been labeled as positive or negative for a cell of interest. The method trains each model of an ensemble of neural networks using the training and validation sets. Next, each model of the ensemble is tested using the testing set and the predictions of the ensemble are combined. The combined prediction is compared to the label and the method determines whether the combined prediction satisfies a pre-determined level of detection (LOD). If so, the method outputs the ensemble as a final ensemble. If not, the method modifies a hyperparameter of at least one of the models of the ensemble until the combined prediction satisfies the pre-determined LOD. 1. A method for selecting a final model for detecting one or more cells of interest in image datasets , the method comprising:dividing a curated image dataset into a training set, a validation set, and a testing set, wherein each image in the curated image dataset has been labeled as positive or negative for a cell of interest;training each model of an ensemble of deep convolutional neural networks using the training and validation sets by evaluating and minimizing an error function that represents the accuracy of the prediction generated by each model versus the label;testing each model of the ensemble using the testing set;combining the predictions of the ensemble to make a combined prediction;comparing the combined prediction to the label;determining whether the combined prediction satisfies a pre-determined level of detection;if the combined prediction satisfies the pre-determined level of detection, outputting the ensemble as a final ensemble; andif the combined prediction does not satisfy the pre-determined level of detection, modifying at least one hyperparameter of at least one of the ...

Computer-implemented methods, computer-readable media, and systems for tracking a plurality of spermatozoa

One aspect of the invention provides a computer-implemented method for tracking a plurality of spermatozoa. The method includes: identifying a coordinate for each of the plurality of spermatozoa in a plurality of video frames; and applying a nearest-neighbor joint probabilistic data association filter (NN-JPDAF) algorithm to associate the coordinates with one or a plurality of sperm tracks. Another aspect of the invention provides a non-transitory computer-readable medium containing program instructions executable by a processor. The computer-readable medium can include program instructions for performing a method as described herein. Another aspect of the invention provides a system including: a processor and a computer-readable medium including program instructions for performing a method as described herein.

CORRELATED IMAGE ANALYSIS FOR 3D BIOPSY

The present invention relates to image analysis of pathology images. In order to improve reliability in image analysis of pathology images, a method is provided for providing support in identifying at least one feature of a tissue sample in a microscopic image. The method comprises the steps of providing a first image of a first microscopy modality representing an area of the tissue sample, providing a second image of a second microscopy modality representing the said area of the tissue sample, generating a first high intensity image by applying a first high intensity filter to the first image or a first low intensity image by applying a first low intensity filter to the first image to obtain first information of the at least one feature, generating a second high intensity image by applying a second high intensity filter to the second image or a second low intensity image by applying a second low intensity filter to the second image to obtain second information of the at least one feature, calculating a correlation of an image pair comprising one of the first high intensity image and the first low intensity image and one of the second high intensity image and the second low intensity image for correlating the first information and the second information of the at least one feature, and outputting the calculated correlation for providing support in identifying the at least one feature of the tissue sample. 2. Method according to claim 1 , wherein the correlation is calculated based on a Boolean operation.4. Data processing apparatus according to claim 3 , wherein the correlation unit is configured to calculate the correlation based on a Boolean operation.5. A system for providing support in identifying at least one feature of a tissue sample in a microscopic image claim 3 , comprising:{'claim-ref': {'@idref': 'CLM-00003', 'claim 3'}, 'a data processing apparatus according to ; and'}a display;wherein the display is configured to display at least one of the first and ...

Weakly supervised learning with whole slide images

Techniques are provided for determining classifications based on WSIs. A varied-size feature map is generated for each training WSI by generating a grid of patches for the training WSI, segmenting the training WSI into tissue and non-tissue areas, and converting patches comprising the tissue areas into tensors. Bounding boxes are generated based on the patches comprising tissue areas and segmented into feature map patches. A fixed-size feature map is generated based on a subset of the feature map patches. A classifier model is trained to process fixed-size feature maps corresponding to the training WSIs such that, for each fixed-size feature map, the classifier model is operable to assign a WSI-level tissue or cell morphology classification or regression based on the tensors. A classification engine is configured to use the trained classifier model to determine a WSI-level tissue or cell morphology classification or regression for a test WSI.

Method for segmenting a color image and digital microscope

The present invention relates to a method for automatically segmenting a color image composed of pixels having been captured for example using a digital microscope. The method provides for a color to be selected representing a region to be segmented of the color image. The colors assigned to the pixels and the color selected are in accordance with a first color model. A clearance for each of the pixels is determined representing a distance between the selected color and the color of the corresponding pixel. The colors assigned to the pixels and the color selected are in accordance with a perception-oriented second color model. One of the pixels is assigned to the segmented region when the clearance assigned to the corresponding pixel is below a settable threshold value and when the hue assigned to the corresponding pixel is within a settable interval around the hue assigned to the selected color.

Image measurement apparatus, image measurement method, and image measuring program storage medium

A partial image extracting unit extracts images of a predetermined size and constant magnification from a tissue region. A mask generating unit generates a mask for removing a region not intended for measurement from the tissue region for each extracted image. A complete mask generating unit generates a temporary complete mask in which the masks generated for each of the images are integrated together, and generates a complete mask in which close portions among unmasked portions in the temporary complete mask are unified into one or more target regions. A measuring unit measures information pertaining to an object to be measured included in the image, and this information is measured for each of the images extracted by the partial image extracting unit. A region information calculating unit calculates, for each target region, information pertaining to the object to be measured from the measured information and from the complete mask.

MOBILE PHONE-BASED MINIATURE MICROSCOPIC IMAGE ACQUISITION DEVICE AND IMAGE STITCHING AND RECOGNITION METHODS

A mobile phone-based miniature microscopic image acquisition device, and image stitching and recognition methods are provided. The acquisition device comprises a support, wherein a mobile phone fixing table is provided on the support. A microscope head is provided below a camera of a mobile phone. A slide holder is provided below the microscope head, and an lighting source is provided below the slide holder. A scanning movement is performed between the slide holder and the microscope head along X and Y axes, so that images of a slide are acquired into the mobile phone. The slide sample images acquired into the mobile phone can be stitched and recognized, and can be uploaded to the cloud to be processed by cloud AI, thereby significantly improving the accuracy and efficiency of cell recognition, greatly reducing the medical cost, and ensuring more remote medical institutions can apply such technology for diagnosis. 14. A mobile phone-based miniature microscopic image acquisition device , comprising a support () , wherein ,{'b': 1', '4', '11, 'a mobile phone fixing table () is provided on the support () for fixing a mobile phone ();'}{'b': 3', '11', '5', '3', '8', '5, 'a microscope head () is provided below a camera of the mobile phone (), a slide holder () is provided below the microscope head (), and a lighting source () is provided below the slide holder (); and'}{'b': 5', '3', '7', '11, 'a scanning movement is performed between the slide holder () and the microscope head () along X axis and Y axis, so that images of a slide () are collected into the mobile phone (),'}{'b': 3', '32', '1', '32', '1', '32', '11, 'the microscope head () comprises a cantilever rod () mounted on the mobile phone fixing table (), one end of the cantilever rod () is fixedly connected to the mobile phone fixing table (), and a microscope lens is provided on the other end of the cantilever rod (); and the microphone lens is located below the camera of the mobile phone (),'}{'b': 1', '108', ...

IMAGE PROCESSING DEVICE, IMAGE PROCESSING METHOD, AND PROGRAM

An image processing device comprises pixel group setting part that sets plurality of pixel groups including first and second pixel groups, which are not connected to each other, based on image data of first image which shows target object including linear portion; pair setting part that performs setting pair of first and second pixel groups including pixel within predetermined range from first pixel group to calculate connecting degree of second pixel group with respect to first pixel group; and calculation part that calculates connecting degree of pair, wherein predetermined range is search range set based on first distance between first and second pixel groups, or search range set on based on first angle formed between first line segment corresponding to first pixel group and second line segment corresponding to second pixel group, and calculation part calculates first distance or first angle using first distance or first angle. 1. An image processing device comprising:a pixel group setting part configured to obtain an image data of a first image which shows a target object including a linear portion from an imaging device, and configured to set a plurality of pixel groups including a first pixel group and a second pixel group, which are not connected to each other, based on the image data when setting a plurality of pixel groups corresponding to the first image in the imaging device;a pair setting part configured to perform setting of a pair of the first pixel group and the second pixel group including a pixel within a predetermined range from the first pixel group, among the plurality of pixel groups, in order to calculate a connecting degree of the second pixel group with respect to the first pixel group; anda calculation part configured to calculate the connecting degree of the pair set by the pair setting part,wherein the predetermined range is a search range set on the basis of a first distance between the first pixel group and the second pixel group, or a ...

Display Apparatus Capable of Image Scanning and Driving Method Thereof

According to an embodiment, a display apparatus capable of image scanning and a driving method thereof is provided. According to an embodiment, a display apparatus capable of image scanning, which includes a plurality of contact sensors each of which includes an N-type transistor, a P-type transistor connected to a gate electrode of the N-type transistor, and a sensing capacitor one end of which is connected to a drain electrode of the N-type transistor, the plurality of contact sensors being arranged so as not to cover a unit color pixel area of a color filter layer; a first scan line to which a selective signal is periodically applied, the first scan line being connected to a source electrode of the N-type transistor; a second scan line to which a selective signal is applied followed by the first scan line, the second scan line being connected to the gate electrode and source electrode of the P-type transistor; and a readout line connected to the drain electrode of the N-type transistor.

Video and audio processing based multimedia synchronization system and method of creating the same

Various embodiments facilitate multimedia synchronization based on video processing and audio processing. In one embodiment, a multimedia synchronization system is provided to synchronize video and audio content by performing video processing on the video content, audio processing on the audio content, and a synchronization process. The video processing and the audio processing generate recognized lip movement and recognized speech, respectively. The synchronization process determines a match between lip movement of the recognized lip movement and speech of the recognized speech, and synchronizes the video content and the audio content based on the match.

Culture imaging system

A system for the characterization of a colony of microorganisms includes a coherent light source configured to provide coherent light of one or more wavelengths along a common optical path. A holder is configured to operationally arrange a substrate so that the colony of microorganisms on a surface of the substrate is positioned to receive the coherent light. Scattered light is generated from the colony of microorganisms receiving coherent light. A first image capture device is configured to receive the scattered light and generate a scatter image from the microorganism colony irradiated by the coherent light. The system also includes a magnifying lens configured to magnify the colony of microorganisms. A second image capture device is configured to capture a light image of the colony of microorganisms magnified by the magnifying lens. Methods of assigning organisms to categories with like organisms without necessarily identifying the organisms are also described.

Lab on chip cartridge

A cartridge for an optical analysis, including a supporting body, having a first face and a second face opposite to one another; a plurality of wells adapted to receive a biological solution to be analyzed, the wells extending in the supporting body on the first face; at least one biocompatible layer extending inside each well; an anti-reflection layer extending on the first face outside the wells; and a reflection layer extending inside each well. Methods of using same are also provided.

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

An image processing method in a diagnosis assisting device that diagnoses lesions from a picked-up image, the method including A) performing an image correction on the picked-up image for diagnosis, and B) obtaining an input image to an identifier that identifies diseases based on the picked-up image having undergone the image correction. In A), when a brightness correction is performed as the image correction, 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, a brightness histogram is created relative to the measuring area, a correction gain value is calculated based on a peak value of the created brightness histogram, and each of pixels in a color space is corrected by using the calculated correction gain value. 1. An image processing method in a diagnosis assisting device that diagnoses lesions from a picked-up image , the method comprising:A) performing an image correction on the picked-up image for diagnosis, andB) obtaining an input image to an identifier that identifies diseases based on the picked-up image having undergone the image correction,wherein in A), when a brightness correction is performed as the image correction, 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, a brightness histogram is created relative to the measuring area, a correction gain value is calculated based on a peak value of the created brightness histogram, and each of pixels in a color space is corrected by using the calculated correction gain value.2. The image processing method according to claim 1 , wherein:in A), at least two pixels among the pixels in the color space are multiplied by the same correction gain value so as not to change a color phase and to obtain a correction gain, and each of the pixels in the color space is corrected, andin B), an average pixel value of the picked-up image is ...

Method and apparatus for filling images captured by array cameras

Image filling utilizing image data captured by an array of cameras having different camera viewpoints. Image data, such as a pixel value or gradient value associated with a spatial point in a source region of an image is transferred to the same or another image to fill a target region. Visual artifacts may be reduced by filling portions of the target region visible from other viewpoints with expanded source patches to reduce the size of the target region to be inpainted. In embodiments, a shifted mask corresponding to the target region is determined for each supplementary image based on an estimate of foreground disparity. In further embodiments, partially occluded regions are detected based on an estimate of background disparity. Source patches may be expanded based on a baseline between camera viewpoints into large coherent regions that agree well with the target region boundary may be filled without hallucinating image data from similar patches.

Image analyzing device, image analyzing method, and computer program product

According to an embodiment, an image analyzing device includes a first acquirer, a constructor, a first calculator, a second calculator, and a third calculator. The first acquirer is configured to acquire image information on a joint of a subject and bones connected to the joint. The constructor is configured to construct a three-dimensional shape of the bones and the joint, and relation characteristics between a load and deformation in the bones and the joint from the image information. The first calculator is configured to calculate a positional relation between the bones connected to the joint. The second calculator is configured to calculate acting force a muscle acting on the bones connected to the joint based on the positional relation. The third calculator is configured to calculate first stress acting on the joint based on the three-dimensional shape, the relation characteristics, and the acting force.

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.

Information processing apparatus, information processing system, information processing method, program, and recording medium

The present invention provides an information processing apparatus capable of selecting a region of cancer cells in any tissue sample image and easily and accurately counting the number of the cancer cells. The information processing apparatus is an information processing apparatus 100 including: an acquisition unit 110 for acquiring image data obtained by reading a tissue sample image 150 obtained by putting a mark 151 specifying a selected area 152 on an image obtained by immunostaining and then imaging a biological tissue; and a counting unit 120 for counting the number of cancer cells in the selected area 152 specified by the mark 151 based on the image data of the tissue sample image 150 acquired by the acquisition unit 110 , wherein a diagnosis based on the tissue sample image 150 is supported.

CELL COUNTING OR CELL CONFLUENCE WITH RESCALED INPUT IMAGES

Various examples relate to determining a number and/or a confluency of cells in a microscopy image. To that end, the microscopy image is firstly rescaled and then processed. 1. A method , comprising:acquiring a light-microscope image which images a multiplicity of cells,adapting a size of the light-microscope image, such that a size of a predefined cell structure of the multiplicity of cells corresponds to a predefined reference value, andand after adapting the size: determining an estimation of at least one of a number or a confluency of the cells on the basis of the light-microscope image.2. The method as claimed in claim 1 , furthermore comprising:using a first machine-learned algorithm, which adapts the size of the light-microscope image by means of an image-to-image transformation on the basis of the light-microscope image.3. The method as claimed in claim 1 , furthermore comprising:using a first machine-learned algorithm, which determines a scaling factor for adapting the size by means of an image-to-scalar transformation on the basis of the light-microscope image.4. The method as claimed in claim 3 ,wherein the first machine-learned algorithm acquires a plurality of image excerpts of the light-microscope image and wherein the scaling factor is determined on the basis of an averaging in relation to the plurality of image excerpts for the light-microscope image.5. The method as claimed in claim 1 , furthermore comprising:localizing the cell structure in the light-microscope image,on the basis of said localizing, determining an average size of the cell structure in the light-microscope image, anddetermining a scaling factor for adapting the size on the basis of a ratio of the average size and the predefined reference value.6. The method as claimed in claim 5 ,wherein localizing the cell structure and determining the average size are carried out for a plurality of image excerpts of the light-microscope image and the average size is determined on the basis of an ...

Method for grouping cells according to density and electronic device employing method

A method of grouping certain cell densities to establish the number and volume of cells appearing in an image input the image into a self-encoder having a preset number of a density grouping models to obtain a preset number of reconstructed images. The image and each reconstructed image are input into a twin network model of the density grouping model corresponding to each reconstructed image, and a first error value is calculated between the image and each reconstructed image. A minimum first error value in the first error value set is determined, and a density range corresponding to the density grouping model corresponding to minimum first error value is taken as the density range. An electronic device and a non-volatile storage medium performing the above-described method are also disclosed.

Target cell statistical method, apparatus, and system

A target cell statistical method, apparatus and system are provided. A cell image of a blood specimen is acquired by a cell image analysis apparatus. The blood specimen is derived from a blood sample to be tested. A number of target cells and a number of reference cells in the cell image are automatically identified by the cell image analysis apparatus. A number of reference cells in the blood sample to be tested is acquired by the cell image analysis apparatus, and a number of target cells in the blood sample to be tested is calculated by the cell image analysis apparatus, based on the number of target cells and the number of reference cells in the cell image and the number of reference cells in the blood sample to be tested.

MINIATURE MICROSCOPIC CELL IMAGE ACQUISITION DEVICE AND IMAGE RECOGNITION METHOD

A miniature microscopic cell image acquisition device and an image recognition method are provided. The miniature microscopic cell image acquisition device comprises a support, wherein a movable module platform is provided on the support, and a camera module is provided on the module platform. A microscope head that is relatively fixed is provided below a camera of the camera module, a slide holder is provided below the microscope head, and a lighting source is provided below the slide holder. A scanning drive module is provided between the slide holder and the camera module to perform a scanning movement along X and Y axes, so that the slide holder and the camera module make a scanning movement along the X and Y axes, and images of a slide are acquired by the camera module in a scanning manner. 14. A miniature microscopic cell image acquisition device , comprising a support () , wherein{'b': 2', '4', '1', '2, 'a movable module platform () is provided on the support (), and a camera module () is provided on the module platform ();'}{'b': 3', '111', '1', '5', '3', '8', '5, 'a microscope head () that is relatively fixed is provided below a camera () of the camera module (), a slide holder () is provided below the microscope head (), and a lighting source () is provided below the slide holder (); and'}{'b': 5', '1', '5', '1', '7', '1, 'a scanning drive module is provided between the slide holder () and the camera module () to perform a scanning movement along X axis and Y axis, so that the slide holder () and the camera module () make a scanning movement along the X axis and Y axis, and images of a slide () are collected by the camera module () in a scanning manner.'}2. The miniature microscopic cell image acquisition device according to claim 1 , wherein claim 1 ,{'b': 3', '32', '2', '32', '2, 'the microscope head () comprises a cantilever rod () mounted on the module platform (), one end of the cantilever rod () is fixedly connected to the module platform (), and a ...

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.

Apparatus, method, and system for image-based human embryo cell classification

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

Image Processing Device, In-Focus Position Specifying Method, and In-Focus Position Specifying Program

An image processing device includes an inputter, a hardware processor, and a storage. The inputter is to input a morphological image, and a plurality of fluorescent images which having focal planes different at a predetermined interval in a height direction of the tissue sample in a same range as the morphological image and representing expression of the biological substance in the tissue sample with a fluorescent bright spot. The hardware processor extracts a cell region, specifies a focal plane most in focus as an in-focus plane for each cell region, specifies a coordinate in the in-focus plane of the cell region, extracts a fluorescent bright spot region from a fluorescent image in a focal plane corresponding to the in-focus plane, and calculates a luminance value or a number of the fluorescent nanoparticle in the fluorescent bright spot region. The storage stores the in-focus plane and the coordinate.

Image analysis method, image analysis apparatus, and image analysis program for analyzing cell with deep learning algorithm

An image analysis method according one or more embodiments may analyze a form of a cell using a deep learning algorithm with a structure of a neural network. The image analysis method may include: generating data for analysis from an image for analysis in which an analysis target cell is captured; inputting the data for analysis into the deep learning algorithm; and generating data indicating a form of the analysis target cell using the deep learning algorithm.

Analysis apparatus, stratum age estimation apparatus, analysis method, stratum age estimation method, and program

An analysis apparatus ( 100 ) includes an image acquisition unit ( 110 ) and an analysis unit ( 120 ). The image acquisition unit ( 110 ) acquires image data of a microfossil in a sample collected from a stratum. The analysis unit ( 120 ) analyzes the image data acquired by the image acquisition unit ( 110 ) using a machine learning result to analyze a taxon or kind of the microfossil in the image data.

Fully automatic, template-free particle picking for electron microscopy

Systems and methods are described for the fully automatic, template-free locating and extracting of a plurality of two-dimensional projections of particles in a micrograph image. A set of reference images is automatically assembled from a micrograph image by analyzing the image data in each of a plurality of partially overlapping windows and identifying a subset of windows with image data satisfying at least one statistic criterion compared to other windows. A normalized cross-correlation is then calculated between the image data in each reference image and the image data in each of a plurality of query image windows. Based on this cross-correlation analysis, a plurality of locations in the micrograph is automatically identified as containing a two-dimensional projection of a different instance of the particle of the first type. The two-dimensional projections identified in the micrograph are then used to determine the three-dimensional structure of the particle.

Systems and methods for encoding image features of high-resolution digital images of biological specimens

An image analysis system for analyzing biological specimen images is disclosed. The system may include: a superpixel generator configured to obtain a biological specimen image and group pixels of the biological specimen image into a plurality of superpixels; a feature extractor configured to extract, from each superpixel in the plurality of superpixels, a feature vector comprising a plurality of image features; a clustering engine configured to assign the plurality of superpixels to a predefined number of clusters, each cluster being characterized by a centroid vector of feature vectors of superpixels assigned to the cluster; and a storage interface configured to store, for each superpixel in the plurality of superpixels, clustering information identifying the one cluster to which the superpixel is assigned. The system may also include a graph engine configured construct a graph based on the stored information, and use the graph to perform a graph-based image processing task.

Systems and methods of managing medical images

Computer-implemented methods and systems are provided for managing one or more images. An example method for indexing involves operating a processor to, divide an image portion of an image of the one or more images into a plurality of patches, for each patch of the image: detect one or more features of the patch; and assign the patch to at least one cluster of a plurality of clusters of the image, based on the one or more features of the patch. The processor is operable to, for each cluster of the image, select at least one patch from the cluster as at least one representative patch for the cluster; for each representative patch, generate an encoded representation based on one or more features of the representative patch; and generate an index identifier for the image based on the encoded representations of the representative patches of the image.

Medical image registration guided by target lesion

Machine logic (for example, software) for registering multiple medical images, each showing a common lesion, with each other. In performing this registration, registration points are chosen to be both: (i) outside of image portion that is potentially compromised by the lesion (in any of the multiple images); and (ii) as close to the lesion as possible. However, in at least one of the images the extent of the lesion is not known—so, in order to accommodate this uncertainty about the lesion boundaries, lesion predicting machine logic rules are used to predict the size, shape and/or location of the lesion. Machine learning is used to intermittently adjust and improve the lesion predicting machine logic rules.

Discriminatively Weighted Multi-Scale Local Binary Patterns

Apparatus and methods associated with detecting prostate cancer (CaP) in a magnetic resonance (MR) image of a prostate of a CaP patient are described. One example apparatus includes logics that acquire an image of a prostate, learn a weighted vector, detect salient features in the image of the prostate, and generate a heatmap that facilitates detecting CaP. An image acquisition logic acquires a T2 weighted MR image of a prostate. A learning logic learns a weighted vector based on a set of positive LBP descriptors and a set of negative LBP descriptors extracted from the image at multiple scales. A salient feature detection logic detects salient features in the image based on the weighted vector and a pixel-by-pixel weighted Hamming matching of the image. A prediction logic generates a statistical probability heatmap based on the weighted vector and the weighted Hamming matching of the image.

Methods and systems for analysis of fibers and branching structures within an image of a sample

A computer-implemented method and system of fiber analysis and reconstruction of a fiber-containing biological sample perform efficient and accurate analysis of fiber morphology. The images of objects comprising fiber and/or branching structure are obtained by camera and/or other imaging devices. Variable modules are operable to obtain initial tracing seeds via adaptive threshold method for identifying tracing seeds candidates by local Principal Component Analysis (PCA) calculation and sorting out the seeds with low and high score. Each single fiber segment of interest is traced repeatedly utilizing PCA fitting calculation, and individual fiber segments are assembled by performing crossover matching calculation.

Line-based image registration and cross-image annotation devices, systems and methods

The disclosure relates to devices, systems and methods for image registration and annotation. The devices include computer software products for aligning whole slide digital images on a common grid and transferring annotations from one aligned image to another aligned image on the basis of matching tissue structure. The systems include computer-implemented systems such as work stations and networked computers for accomplishing the tissue-structure based image registration and cross-image annotation. The methods include processes for aligning digital images corresponding to adjacent tissue sections on a common grid based on tissue structure, and transferring annotations from one of the adjacent tissue images to another of the adjacent tissue images. The basis for alignment may be a line-based registration process, wherein sets of lines are computed on the boundary regions computed for the two images, where the boundary regions are obtained using information from two domains—soft-weighted foreground images and gradient magnitude images. The binary mask image, based on whose boundary the line features are computed, may be generated by combining two binary masks—a first binary mask is obtained on thresholding a soft-weighted (continuous valued) foreground image, which is computed based on the stain content in an image, while a second binary mask is obtained after thresholding a gradient magnitude domain image, where the gradient is computed from the grayscale image obtained from the color image.

Target Biological Substance Analysis Method And Analysis System

An analysis method of a target biological substance for analyzing a tissue specimen with a fluorescence microscope is provided. The tissue specimen is stained with immunostaining reagents including different kinds of fluorescent nanoparticles. According to the analysis method, a luminance ratio of each of the immunostaining reagents for each of filter sets is calculated; and number of fluorescent bright points is measured in the tissue specimen for each of the filter sets for calculating number of fluorescent bright points for each of the immunostaining reagents based on the luminance ratio.

Cell Contour Formation Apparatus and Method of the Same, and Non-Transitory Contour Readable Storage Medium Storing a Cell Contour Formation Program

An cell contour formation apparatus includes a cell image acquiring unit, a subband image creating unit, a features calculating unit, a correcting unit, a contour forming unit. The subband image creating unit creates subband images including a low frequency image and a high frequency image. The features calculating unit calculates a local texture features from the high frequency image. The correcting unit corrects the high frequency image on the basis of the pixel value of the low frequency image and the texture features. The contour forming unit forms contours of cells included in the cell group on the basis of the corrected high frequency image.

Systems and methods for calibrating, configuring and validating an imaging device or system for multiplex tissue assays

A system and method for characterization and/or calibration of performance of a multispectral imaging (MSI) system equipping the MSI system for use with a multitude of different fluorescent specimens while being independent on optical characteristics of a specified specimen and providing an integrated system level test for the MSI system. A system and method are adapted to additionally evaluate and express operational parameters performance of the MSI system in terms of standardized units and/or to determine the acceptable detection range of the MSI system.

Cell analysis in body fluids, particularly blood

Disclosed are devices and methods for analyzing an analyte, such as white blood cells in liquid samples.

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 step to generate a first output series of images, and processes the first series of images in accordance with second image processing step to generate a second output series of images. The human interface module displays at least one image plane of the first output series of images and at least one image plane of the second output series of images on a display associated with the user computer.

Culture medium information registration system, colony detection device, program and sanitary management system

The sanitary management system S includes a plurality of portable communication terminal devices 10, a manager terminal device 20 and a server device 40. The server device 40: (1) extracts two or more feature values related to the color of each pixel constituting a culture medium image; (2) executes statistical analysis based on distribution of pixels of the culture medium image by using a coordinate system prescribed by the extracted two or more feature values of each pixel, and calculates a linear function prescribing relation of the two or more feature values in each pixel; and (3) detects the colonies in the culture medium image based on the calculated linear function and counts the number of colonies.

Medical image segmentation method, image segmentation method, and related apparatus and system

The present disclosure provides a medical image segmentation method. The medical image segmentation method includes acquiring a to-be-processed medical image set, the to-be-processed medical image set including a plurality of to-be-processed medical images corresponding to different time points, processing the to-be-processed medical image set in a time dimension according to the to-be-processed medical images and the time points corresponding to the to-be-processed medical images to obtain a temporal dynamic image, and extracting a target region feature from the temporal dynamic image by using a medical image segmentation model, to acquire a target region.

Non-linear imaging systems and methods for assisted reproductive technologies

The present invention generally relates to non-linear imaging systems and methods for use in various assisted reproductive technologies and other applications. In one aspect, the present invention is generally directed to two-photon and other non-linear microscope techniques that can be used to determine the status or maturity state of eggs or egg follicles, for example by determining the presence of polar bodies within the egg follicles. In some embodiments, endogenous metabolites, such as NADH or FAD, can be determined, and in some cases without externally altering those metabolite concentrations or without adding other compounds. Other embodiments of the invention are also directed to computer programs or systems that can be used to determine the status or maturity state of eggs or egg follicles, kits involving such systems, or the like.

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.