Eirik Østmo / Torger Grytå

VI seminar 2022 #16 – Automated Grading of Bladder Cancer using Deep Learning

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Automated Grading of Bladder Cancer using Deep Learning

Presenter: Rune Wetteland, PhD Candidate, University of Stavanger (UiS).

Abstract: Urothelial carcinoma is the most common type of bladder cancer and is among the cancer types with the highest recurrence rate and lifetime treatment cost per patient. Diagnosed patients are stratified into risk groups, mainly based on the histological grade and stage. However, it is well known that correct grading of bladder cancer suffers from intra- and interobserver variability and inconsistent reproducibility between pathologists, potentially leading to under- or overtreatment of the patients. The economic burden, unnecessary patient suffering, and additional load on the health care system illustrate the importance of developing new tools to aid pathologists.

A pipeline for automated diagnostic grading is proposed, called TRI-grade. First, a tissue segmentation method is utilized to find the diagnostically relevant urothelium tissue. Then, a parameterized tile extraction method is used to extract tiles from the urothelium regions at three magnification levels (25x, 100x, and 400x). The extracted tiles form the training, validation, and test data used to train and test the diagnostic model.

The final system outputs a segmented tissue image showing all the tissue regions in the WSI, a WHO grade heatmap indicating low- and high-grade carcinoma regions, and finally, a slide-level WHO grade prediction.

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