Improved Breast Mass Segmentation in Mammograms with Conditional Residual U-Net

  • Heyi LiEmail author
  • Dongdong Chen
  • William H. Nailon
  • Mike E. Davies
  • David Laurenson
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11040)


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.


Mammogram mass segmentation Structured prediction Deep residual learning 


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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Heyi Li
    • 1
    Email author
  • Dongdong Chen
    • 1
  • William H. Nailon
    • 2
  • Mike E. Davies
    • 1
  • David Laurenson
    • 1
  1. 1.Institute for Digital CommunicationsUniversity of EdinburghEdinburghUK
  2. 2.Oncology Physics Department, Edinburgh Cancer CentreWestern General HospitalEdinburghUK

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