# Towards Automated Semantic Segmentation in Mammography Images Source: [Towards Automated Semantic Segmentation in Mammography Images]() Collection index: [llms.txt]() Data updated: 2025-10-27T06:24:25.429Z Authors: Cesar A. Sierra-Franco, Jan Hurtado, Victor de A. Thomaz, Leonardo C. da Cruz, Santiago V. Silva, Alberto B. Raposo Published: 2023-07-18T15:04:42.000Z ## Abstract 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()