@misc{10498/30087,
year = {2020},
month = {3},
url = {http://hdl.handle.net/10498/30087},
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.},
publisher = {PERGAMON-ELSEVIER SCIENCE LTD},
keywords = {Breast cancer},
keywords = {Deep learning},
keywords = {H&E staining},
keywords = {Segmentation},
keywords = {Whole-Slide Imaging},
title = {Automatic segmentation of whole-slide H&E stained breast histopathology images using a deep convolutional neural network architecture},
doi = {10.1016/J.ESWA.2020.113387},
author = {Priego Torres, Blanca María and Sánchez Morillo, Daniel and Fernández Granero, Miguel Ángel and García-Rojo, Marcial},
}