Ventana Medical Systems, Inc.

Auranuch Lorsakul

Joerg Bredno

Michael S Barnes

Srinivas Chukka

Anindya Sarkar

Yao Nie

Christoph Guetter

Ellen Suzue

Zuo Zhao

Kien Nguyen

Xingwei Wang

The present disclosure relates machine learning techniques for segmenting non-tumor regions in specimen images to support tumor detection and analysis. Particularly, aspects of the present disclosure are directed to accessing one or more images that comprise a non-target region (e.g., a non-tumor region) and a target region (e.g., a tumor region), predicting, by a two-dimensional segmentation model, segmentation maps for the non-target region based on discriminative features encoded from the one or more images, a segmentation mask for the one or more images based on the segmentation maps, applying the segmentation mask to the one or more images to generate non-target region masked images that exclude the non-target region from the one or more images, and classifying, by an image analysis model, a biological material or structure within the target region based on a set of features extracted from the non-target region masked images.

Non-tumor segmentation to support tumor detection and analysis

The present disclosure relates to techniques for transforming digital pathology images obtained by different slide scanners into a common format for image analysis. Particularly, aspects of the present disclosure are directed to obtaining a source image of a biological specimen, the source image is generated from a first type of scanner, inputting into a generator model a randomly generated noise vector and a latent feature vector from the source image as input data, generating, by the generator model, a new image based on the input data, inputting into a discriminator model the new image, generating, by the discriminator model, a probability for the new image being authentic or fake, determining whether the new image is authentic or fake based on the generated probability, and outputting the new image when the image is authentic.

Correcting differences in multi-scanners for digital pathology images using deep learning

The present disclosure is directed, among other things, to automated systems and methods for analyzing, storing, and/or retrieving information associated with biological objects having irregular shapes. In some embodiments, the systems and methods partition an input image into a plurality of sub-regions based on localized colors, textures, and/or intensities in the input image, wherein each sub-region represents biologically meaningful data.

Method of storing and retrieving digital pathology analysis results

The present application provides for systems and methods for detecting and estimating signals corresponding to one or more biomarkers in biological samples stained for the presence of protein and/or nucleic acid biomarkers. On particular aspect is directed to a method of estimating an amount of signal corresponding to at least one biomarker in an image of a biological sample. The method includes detecting isolated spots in a first image, deriving an optical density value of a representative isolated spot based on signal features from the detected isolated spots, estimating a number of predictive spots in signal aggregates in each of a plurality of sub-regions based on the derived optical density value of the representative isolated spot, and storing the estimated number of predictive spots and detected isolated spots in each of the plurality of generated sub-regions in a database.

Quantitation of signal in stain aggregates

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.

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Santa Clara, CA, United States of America

Auranuch Lorsakul