Deep Learning Based Segmentation of Blood Vessels from H&E Stained Oesophageal Adenocarcinoma Whole-Slide Images

TL;DR

This paper introduces a novel guiding map technique to enhance deep learning segmentation of blood vessels in H&E stained esophageal cancer images, addressing challenges of limited data and heterogeneous vessel appearance.

Contribution

It presents a new guiding map method that improves blood vessel segmentation accuracy in computational pathology, especially with limited labeled data.

Findings

Improved segmentation accuracy demonstrated through quantitative results.

Guiding maps help models learn representative features of blood vessels.

Method shows promise for broader tissue type applications.

Abstract

Blood vessels (BVs) play a critical role in the Tumor Micro-Environment (TME), potentially influencing cancer progression and treatment response. However, manually quantifying BVs in Hematoxylin and Eosin (H&E) stained images is challenging and labor-intensive due to their heterogeneous appearances. We propose a novel approach of constructing guiding maps to improve the performance of state-of-the-art segmentation models for BV segmentation, the guiding maps encourage the models to learn representative features of BVs. This is particularly beneficial for computational pathology, where labeled training data is often limited and large models are prone to overfitting. We have quantitative and qualitative results to demonstrate the efficacy of our approach in improving segmentation accuracy. In future, we plan to validate this method to segment BVs across various tissue types and investigate the role of cellular structures in relation to BVs in the TME.