Ventana Medical Systems, Inc.

Kien Nguyen

Christophe Chefd'hotel

Srinivas Chukka

Ting Chen

Joerg Bredno

Christoph Guetter

Michael S Barnes

Anindya Sarkar

XiuZhong Wang

Described herein are computer-implemented methods for analysis of a tissue sample. An example method includes: annotating the whole tumor regions or set of tumorous sub-regions either on a biomarker image or an H&E image (e.g. from an adjacent serial section of the biomarker image); registering at least a portion of the biomarker image to the H&E image; detecting different cellular and regional tissue structures within the registered H&E image; computing a probability map based on the different detected structures within the registered H&E image; deriving nuclear metrics from each of the biomarker and H&E images; deriving probability metrics from the probability map; and classifying tumor nuclei in the biomarker image based on the computed nuclear and probability metrics.

Computer scoring based on primary stain and immunohistochemistry images related application data

Systems and methods discussed herein include, among other things, a method comprising quantifying analyte staining of a biological compartment in a region in which said staining is intermixed with analyte staining of an analytically-distinct distinct biological compartment. Disclosed systems and methods include, for example, a system and method for identifying membrane staining of an analyte of interest in regions where diffuse membrane staining is intermixed with cytoplasmic staining and/or punctate staining is disclosed. Disclosed systems and methods include, for example, a system and method for quantifying membrane staining of an analyte of interest in tissue or cytological samples having regions in which membrane staining is intermixed with cytoplasmic staining and/or punctate staining.

Automated analysis of cellular samples having intermixing of analytically distinct patterns of analyte staining

An image analysis system and method classify cells in a tissue image. The system and method may extract at least one image feature characterizing an object in the tissue image. Based on the extracted image feature, cells may be classified according to at least one predefined membrane pattern. For each classified cell, a class label that identifies a class to which the classified cell belongs may be outputted.

System and method for classifying cells in tissue images based on membrane features

An image analysis system and method to generate selective stain segmentation images for at least one cell type of interest within a stained tissue image are presented. Cell membranes in the tissue image are detected to generate a corresponding membrane mask image. Cell nuclei are classified to generate a classified cells image. Selective stain segmentation images are generated for the at least one cell type of interest based on the membrane mask image and the classified cells image.

System and method for generating selective stain segmentation images for cell types of interest

A system and a method for unveiling poorly visible or lightly colored nuclei in an input image are disclosed. An input image is fed to a color deconvolution module for deconvolution into two color channels that are processed separately before being combined. The input image is deconvolved into two separate images: a stain image and a counter stain image. A complement of the stain image is generated in order to clearly reflect the locations of the poorly visible or light-colored nuclei. The complement image and the counter stain image are optionally normalized and then combined and segmented, to generate an output image with clearly defined nuclei. Alternatively, the complement of the stain image and the counter stain image can optionally be normalized, and then segmented prior to being combined to generate the output image.

Automated methods and systems for detecting cells in stained specimen images

Computer scoring based on primary stain and immunohistochemistry images

Methods and systems for generating a heat map that reduces bias in selecting FOVs are disclosed. Some disclosed methods include annotating a primary stained image, registering the annotation to a secondary serial specific stained image, using an image analysis algorithm to compute a scoring criteria specific to the tissue and assay type for tiled regions in the image, using a sliding window in the annotated tumor region to compute values for each pixel in a heat map which correlate to the specific scoring criteria, displaying the heat map at low resolution, ranking and selecting hot spots, selecting FOVs from the hot spot regions which results in displaying the slide-level score for the FOVs. The systems comprise, among other things, software configured to perform the referenced method.

Devices, systems and methods for automated quantitative scoring of digitized tissue images

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

Adaptive classification for whole slide tissue segmentation

Systems and methods for automated analysis of histochemical or cytological samples stained for analytes having complex staining patterns are presented. Analyte staining of a biological compartment in a region is quantified, in which said staining is intermixed with analyte staining of an analytically-distinct biological compartment. The systems and methods further include identifying membrane staining of an analyte of interest in regions where diffuse membrane staining is intermixed with cytoplasmic staining and/or punctate staining is disclosed. The systems and methods further include quantifying membrane staining of an analyte of interest in tissue or cytological samples having regions in which membrane staining is intermixed with cytoplasmic staining and/or punctate staining.

Disclosed, among other things, is a computer device and computer-implemented method of classifying cells within an image of a tissue sample comprising providing the image of the tissue sample as input; computing nuclear feature metrics from features of nuclei within the image; computing contextual information metrics based on nuclei of interest with the image; classifying the cells within the image using a combination of the nuclear feature metrics and contextual information metrics.

Classifying nuclei in histology images

Methods, systems, and apparatuses for automatically identifying glandular regions and tubule regions in a breast tissue sample are provided. An image of breast tissue is analyzed to detect nuclei and lumen candidates, identify tumor nuclei and true lumen from the candidates, and group tumor nuclei with neighboring tumor nuclei and lumina to define tubule glandular regions and non-tubule glandular regions of the image. Learnt supervised classifiers, such as random forest classifiers, can be applied to identify and classify the tumor nuclei and true lumina. Graph-cut methods can be applied to group the tumor nuclei and lumina and to define the tubule glandular regions and non-tubule glandular regions. The analysis can be applied to whole slide images and can resolve tubule areas with multiple layers of nuclei.

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Ho Chi Minh, Vietnam

Kien Nguyen