A Dilated Residual Hierarchically Fashioned Segmentation Framework for Extracting Gleason Tissues and Grading Prostate Cancer from Whole Slide Images

TL;DR

This paper introduces a novel hierarchical segmentation framework utilizing hybrid dilation and a specialized loss function to accurately extract Gleason tissues and grade prostate cancer from large-scale whole slide images, outperforming existing methods.

Contribution

It presents a new hierarchical segmentation approach with hybrid dilation factors and a three-tiered loss function for improved Gleason tissue extraction and prostate cancer grading.

Findings

Outperforms state-of-the-art methods by 3.22% in mean intersection-over-union.

Achieves 6.91% higher F1 score in grading prostate cancer.

Evaluated on a large dataset of over 10,000 whole slide scans.

Abstract

Prostate cancer (PCa) is the second deadliest form of cancer in males, and it can be clinically graded by examining the structural representations of Gleason tissues. This paper proposes \RV{a new method} for segmenting the Gleason tissues \RV{(patch-wise) in order to grade PCa from the whole slide images (WSI).} Also, the proposed approach encompasses two main contributions: 1) A synergy of hybrid dilation factors and hierarchical decomposition of latent space representation for effective Gleason tissues extraction, and 2) A three-tiered loss function which can penalize different semantic segmentation models for accurately extracting the highly correlated patterns. In addition to this, the proposed framework has been extensively evaluated on a large-scale PCa dataset containing 10,516 whole slide scans (with around 71.7M patches), where it outperforms state-of-the-art schemes by 3.22% (in terms of mean intersection-over-union) for extracting the Gleason tissues and 6.91% (in terms of F1 score) for grading the progression of PCa.