Towards Automated Semantic Segmentation in Mammography Images

TL;DR

This paper presents a deep learning framework for automatic segmentation of key structures in mammography images, aiding diagnosis and clinical workflows.

Contribution

It introduces a large private dataset and evaluates various deep learning architectures for accurate mammogram segmentation.

Findings

Achieved accurate segmentation on challenging cases

Demonstrated potential for clinical integration

Evaluated multiple deep learning models

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.