Mammography images are widely used to detect non-palpable breast lesions or nodules, preventing cancer and providing the opportunity to plan interventions when necessary. The identification of some structures of interest is essential to make a diagnosis and evaluate image adequacy. Thus, computer-aided detection systems can be helpful in assisting medical interpretation by automatically segmenting these landmark structures. In this paper, we propose a deep learning-based framework for the segmentation of the nipple, the pectoral muscle, the fibroglandular tissue, and the fatty tissue on standard-view mammography images. We introduce a large private segmentation dataset and extensive experiments considering different deep-learning model architectures. Our experiments demonstrate accurate segmentation performance on variate and challenging cases, showing that this framework can be integrated into clinical practice.
@article{arxiv.2307.10296,
title = {Towards Automated Semantic Segmentation in Mammography Images},
author = {Cesar A. Sierra-Franco and Jan Hurtado and Victor de A. Thomaz and Leonardo C. da Cruz and Santiago V. Silva and Alberto B. Raposo},
journal= {arXiv preprint arXiv:2307.10296},
year = {2023}
}