Improving Whole Slide Segmentation Through Visual Context - A Systematic Study

TL;DR

This paper systematically compares various neural network architectures to evaluate how multi-scale visual context improves the dense segmentation of histology images, demonstrating its importance in tissue analysis.

Contribution

It provides a comprehensive analysis of different architectures incorporating multi-scale information, highlighting the significance of visual context in histology segmentation.

Findings

Multi-scale context improves segmentation accuracy

Visual context is crucial for tissue architecture assessment

Systematic comparison of architectures reveals best practices

Abstract

While challenging, the dense segmentation of histology images is a necessary first step to assess changes in tissue architecture and cellular morphology. Although specific convolutional neural network architectures have been applied with great success to the problem, few effectively incorporate visual context information from multiple scales. With this paper, we present a systematic comparison of different architectures to assess how including multi-scale information affects segmentation performance. A publicly available breast cancer and a locally collected prostate cancer datasets are being utilised for this study. The results support our hypothesis that visual context and scale play a crucial role in histology image classification problems.