Automatic segmentation of whole-slide H&E stained breast histopathology images using a deep convolutional neural network architecture

Abstract

In this research, we propose a processing pipeline for the automatic segmentation of stained BC images presenting different types of histopathological patterns. Experimental results on a collection of patches of breast cancer images demonstrate how the designed processing pipeline performs properly regardless of the size, texture or any other colour-shape features typical of the malignant carcinomas considered in this study. The estimated segmentation accuracy and frequency-weighted intersection over union ( FWIoU ) were 95.62%, 92.52%, respectively. Additionally, a web-based platform which includes a slide-viewer and an annotation tool was developed. The automatic segmentation method proposed in this work was integrated into this platform and currently, it is being used as a decision-support tool by pathologists.