Persistent homology for the automatic classification of prostate cancer aggressiveness in histopathology images

Peter Lawson; Jordan Schupbach; Brittany Terese Fasy; John W. Sheppard

doi:10.1117/12.2513137

18 March 2019 Persistent homology for the automatic classification of prostate cancer aggressiveness in histopathology images

Peter Lawson, Jordan Schupbach, Brittany Terese Fasy, John W. Sheppard

Author Affiliations +

Proceedings Volume 10956, Medical Imaging 2019: Digital Pathology; 109560G (2019) https://doi.org/10.1117/12.2513137
Event: SPIE Medical Imaging, 2019, San Diego, California, United States

Abstract

In this study, we present an automated approach to classify prostate cancer (PCa) whole slide images (WSIs) as high or low cancer aggressiveness using features derived from persistent homology, a tool of topological data analysis (TDA). This extends previous work on the use of these features for representing the characteristics of prostate cancer architecture in region of interest (ROI) images, and demonstrates the value of features derived from persistent homology to predict cancer aggressiveness of WSIs on an ROI basis. We compute persistence on ROI images and summarize persistence as a persistence image. Using this summary we construct a random forest classifier to predict cancer aggressiveness. We demonstrate the potential of persistent homology to capture the architectural differences between low and high grade prostate cancers in a feature representation that lends itself well to machine learning approaches.

Conference Presentation

Citation Download Citation

Peter Lawson, Jordan Schupbach, Brittany Terese Fasy, and John W. Sheppard "Persistent homology for the automatic classification of prostate cancer aggressiveness in histopathology images", Proc. SPIE 10956, Medical Imaging 2019: Digital Pathology, 109560G (18 March 2019); https://doi.org/10.1117/12.2513137

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