%0 Journal Article 
%A Priego Torres, Blanca María
%A Sánchez Morillo, Daniel
%A Fernández Granero, Miguel Ángel
%A García-Rojo, Marcial
%T Automatic segmentation of whole-slide H&E stained breast histopathology images using a deep convolutional neural network architecture
%D 2020
%@ 0957-4174
%U http://hdl.handle.net/10498/30087
%X 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.
%K Breast cancer
%K Deep learning
%K H&E staining
%K Segmentation
%K Whole-Slide Imaging
%~ Universidad de Cádiz