We explore the use of deep learning for breast mass segmentation in mammograms. By integrating the merits of residual learning and probabilistic graphical modelling with standard U-Net, we propose a new deep network, Conditional Residual U-Net (CRU-Net), to improve the U-Net segmentation performance. Benefiting from the advantage of probabilistic graphical modelling in the pixel-level labelling, and the structure insights of a deep residual network in the feature extraction, the CRU-Net provides excellent mass segmentation performance. Evaluations based on INbreast and DDSM-BCRP datasets demonstrate that the CRU-Net achieves the best mass segmentation performance compared to the state-of-art methodologies. Moreover, neither tedious pre-processing nor post-processing techniques are not required in our algorithm.
@article{arxiv.1808.08885,
title = {Improved Breast Mass Segmentation in Mammograms with Conditional Residual U-net},
author = {Heyi Li and Dongdong Chen and Bill Nailon and Mike Davies and Dave Laurenson},
journal= {arXiv preprint arXiv:1808.08885},
year = {2018}
}
Comments
To appear in MICCAI 2018, Breast Image Analysis Workshop