Ventana Medical Systems, Inc.

Roche Diagnostics Operations, Inc.

Christoph Guetter

Kien Nguyen

Michael S Barnes

Joerg Bredno

Srinivas Chukka

Anindya Sarkar

Auranuch Lorsakul

XiuZhong Wang

Ellen Suzue

Kirill Tarasov

Andreas Reichert

Raghavan Venugopal

Daniel Russakoff

A computer implemented method for identifying at least one peak in a mass spectrometry response curve is provided comprising: a) providing at least one mass spectrometry response curve by using at least one mass spectrometry device; b) evaluating the mass spectrometry response curve by using at least one trained model thereby identifying a start point and an end point of at least one peak of the mass spectrometry response curve, wherein the model was trained using a deep learning regression architecture.

Computer-implemented method for identifying at least one peak in a mass spectrometry response curve

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

A digital pathology system and associated method and computer program product provide a quantitative analysis of entire tissue slides as well as intuitive, effective, fast, and precise quantification of biomarker expressions across relevant areas of the entire tissue slides. The digital pathology system enables a novel workflow that allows the user to efficiently outline clinically relevant morphology in its entirety, including solid tumor areas. Quantitative analysis results are then efficiently and intuitively provided to the user for all tissue content (i.e., millions of cells) within seconds. This efficiency is made possible by a pre-computation step that computes and stores image analysis results for later retrieval. Visualizing vast amount of data effectively is another component of the system that provides information and confidence to the user about the biomarker expression levels.

Digital pathology system and associated workflow for providing visualized whole-slide image analysis

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Alameda, CA, United States of America

Christoph Guetter