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.

通过深度学习框架，我们在常规乳腺X线照片上提出了一个大型私有化分割数据集，并进行了大量实验以考虑不同的深度学习模型结构，从而准确地分割了乳头、胸肌、纤维腺体组织和脂肪组织，展示了该框架可以整合到临床实践中。

迈向乳腺X线摄影图像的自动语义分割