# Towards Automated Semantic Segmentation in Mammography Images

arXiv: https://arxiv.org/abs/2307.10296 · HTML: https://mlsanity.com/paper/2307.10296/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 43 references that are in the map.
- First posted on arXiv 18 Jul 2023.

## Builds on (most cited first, 1 of 1)

- [On segmentation of pectoralis muscle in digital mammograms by means of deep learning](https://mlsanity.com/paper/2008.12904/) (2020)

## Abstract (arXiv)

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.