Ventana Medical Systems, Inc.

Suhas Hanmantrao Patil

Sujit Siddheshwar Chivate

Lokesh Tatke

Suraj Somwanshi

Abhijeet Sharadchandra Gholap

Prithviraj Jadhav

Gauri A Gholap

Chiruvolu V K Rao

Sanford H Barsky

Madhura Vipra

Bikash Sabata

Gurunath Kamble

Srinivas Chukka

Anindya Sarkar

Ronald L Allen

Lou Dietz

Olcay Sertel

Jayant Abhyankar

A facility includes systems and methods for providing a learning-based image analysis approach for the automated detection, classification, and counting of objects (e.g., cell nuclei) within digitized pathology tissue slides. The facility trains an object classifier using a plurality of reference sample slides. Subsequently, and in response to receiving a scanned image of a slide containing tissue data, the facility separates the whole slide into a background region and a tissue region using image segmentation techniques. The facility identifies dominant color regions within the tissue data and identifies seed points within those regions using, for example, a radial symmetry based approach. Based at least in part on those seed points, the facility generates a tessellation, each distinct area in the tessellation corresponding to a distinct detected object. These objects are then classified using the previously-trained classifier. The facility uses the classified objects to score slides.

Tissue object-based machine learning system for automated scoring of digital whole slides

Provided herein are systems methods including a design of a microscope slide scanner for digital pathology applications which provides high quality images and automated batch-mode operation at low cost. The instrument architecture is advantageously based on a convergence of high performance, yet low cost, computing technologies, interfaces and software standards to enable high quality digital microscopy at very low cost. Also provided is a method based in part on a stitching method that allows for dividing an image into a number of overlapping tiles and reconstituting the image with a magnification without substantial loss of accuracy. A scanner is employed in capturing snapshot images. The method allows for overlapping images captured in consecutive snapshots.

Digital microscope slide scanning system and methods

A method and system for automated detection of immunohistochemical (IHC) patterns. The method and system is able to automatically differentiate an epithelial cell part from a non-epithelial cell part of a digital image of a tissue sample to which an IHC compound has been applied. The method and system help to automatically correct errors made in a manual interpretation of a pathological or other manual analysis of tissue samples for diseases such as a human cancer.

Method and system for automated detection of immunohistochemical (IHC) patterns

A method and system for morphology based mitosis identification and classification of digital images. Luminance parameters such as intensity, etc. from a digital image of a biological sample (e.g., tissue cells) to which a chemical compound (e.g., a marker dye) has been applied are analyzed and corrected if necessary. Morphological parameters (e.g., size, elongation ratio, parallelism, boundary roughness, convex hull shape, etc.) from individual components within the biological sample are analyzed. A medical conclusion (e.g., type and count of mitotic cells) or a life science and biotechnology experiment conclusion is determined from the analyzed luminance and morphological parameters. The method and system may be used to develop applications for automatically obtaining a medical diagnosis (e.g., a carcinoma diagnosis).

Method and system for morphology based mitosis identification and classification of digital images

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Katraj, India

Suhas Hanmantrao Patil