University of Minnesota

Jonathan Henriksen

Gregory John Metzger

Stephen C Schmechel

Joseph S Koopmeiners

Ethan Yize Leng

Stephen Dankbar

Automated, machine learning-based systems are described for the analysis and annotation (i.e., detection or delineation) of prostate cancer (PCa) on histologically-stained pathology slides of prostatectomy specimens. A technical framework is described for automating the annotation of predicted PCa that is based on, for example, automated spatial alignment and colorimetric analysis of both H&E and IHC whole-slide images (WSIs). The WSIs may, as one example, be stained with a particular triple-antibody cocktail against high-molecular weight cytokeratin (HMWCK), p63, and &#x3b1;-methylacyl CoA racemase (AMACR).

Automated detection and annotation of prostate cancer on histopathology slides

A computerized method for immunohistochemistry analysis of tissue utilizes digital images of multiple adjacent tissue sections aligned within a computerized software and processed with an algorithm to quantify a two-dimensional IHC signature score for each respective slide image. In various embodiments, the IHC score is performed over several adjacent sections and further processed to produce a three-dimensional IHC quantification referred to as an IHC signature map.

Growing community of inventors

Eden Praire, MN, United States of America

Jonathan Henriksen