Towards Automated Semantic Segmentation in Mammography Images
Cesar A. Sierra-Franco Jan Hurtado Victor de A. Thomaz Leonardo C. da Cruz Santiago V. Silva Alberto B. Raposo
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.