Deep Learning Based Segmentation of Blood Vessels from H&E Stained Oesophageal Adenocarcinoma Whole-Slide Images
This work addresses the labor-intensive task of manually quantifying blood vessels in cancer pathology images, potentially aiding in cancer progression and treatment analysis, but it appears incremental as it builds on existing segmentation models with a new guiding technique.
The paper tackled the problem of segmenting blood vessels from H&E stained oesophageal adenocarcinoma whole-slide images, which is challenging due to heterogeneous appearances, by proposing a novel approach using guiding maps to improve segmentation models, resulting in improved segmentation accuracy as demonstrated by quantitative and qualitative results.
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.