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arXiv · 2307.10296

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
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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.

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Cesar A. Sierra-Franco, Jan Hurtado, Victor de A. Thomaz, Leonardo C. da Cruz, Santiago V. Silva, Alberto B. Raposo. 2023-07-18. Towards Automated Semantic Segmentation in Mammography Images. https://arxiv.org/abs/2307.10296

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