Towards Automated Semantic Segmentation in Mammography Images for Enhanced Clinical Applications.

Cesar A Sierra-Franco, Jan Hurtado, Victor de A Thomaz, Leonardo C da Cruz, Santiago V Silva, Greis Francy M Silva-Calpa, Alberto Raposo
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Abstract

Mammography images are widely used to detect non-palpable breast lesions or nodules, aiding in cancer prevention and enabling timely intervention when necessary. To support medical analysis, computer-aided detection systems can automate the segmentation of landmark structures, which is helpful in locating abnormalities and evaluating image acquisition adequacy. This paper presents a deep learning-based framework for segmenting the nipple, the pectoral muscle, the fibroglandular tissue, and the fatty tissue in standard-view mammography images. To the best of our knowledge, we introduce the largest dataset dedicated to mammography segmentation of key anatomical structures, specifically designed to train deep learning models for this task. Through comprehensive experiments, we evaluated various deep learning model architectures and training configurations, demonstrating robust segmentation performance across diverse and challenging cases. These results underscore the framework's potential for clinical integration. In our experiments, four semantic segmentation architectures were compared, all showing suitability for the target problem, thereby offering flexibility in model selection. Beyond segmentation, we introduce a suite of applications derived from this framework to assist in clinical assessments. These include automating tasks such as multi-view lesion registration and anatomical position estimation, evaluating image acquisition quality, measuring breast density, and enhancing visualization of breast tissues, thus addressing critical needs in breast cancer screening and diagnosis.

乳腺造影图像自动语义分割增强临床应用。
乳房x线摄影图像被广泛用于检测不可触及的乳房病变或结节,有助于预防癌症并在必要时及时干预。为了支持医学分析,计算机辅助检测系统可以自动分割地标结构,这有助于定位异常和评估图像采集的充分性。本文提出了一个基于深度学习的框架,用于分割乳头,胸肌,纤维腺组织和脂肪组织在标准视图乳房x线摄影图像。据我们所知,我们引入了最大的数据集,专门用于关键解剖结构的乳房x线摄影分割,专门用于为此任务训练深度学习模型。通过全面的实验,我们评估了各种深度学习模型架构和训练配置,在各种具有挑战性的情况下展示了强大的分割性能。这些结果强调了该框架在临床整合方面的潜力。在我们的实验中,比较了四种语义分割架构,它们都显示出对目标问题的适用性,从而提供了模型选择的灵活性。除了分割,我们介绍了一套应用程序衍生自这个框架,以协助临床评估。这些包括自动化任务,如多视图病变注册和解剖位置估计,评估图像采集质量,测量乳房密度,增强乳房组织的可视化,从而解决乳腺癌筛查和诊断的关键需求。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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