Automatic Brain Tumor Segmentation with Domain Adaptation

  • Lutao Dai
  • Tengfei Li
  • Hai Shu
  • Liming Zhong
  • Haipeng Shen
  • Hongtu ZhuEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11384)


Deep convolution neural networks, in particular, the encoder-decoder networks, have been extensively used in image segmentation. We develop a deep learning approach for tumor segmentation by combining a modified U-Net and its domain-adapted version (DAU-Net). We divide training samples into two domains according to preliminary segmentation results, and then equip the modified U-Net with domain adaptation structure to obtain a domain invariant feature representation. Our proposed segmentation approach is applied to the BraTS 2018 challenge for brain tumor segmentation, and achieves the mean dice score of 0.91044, 0.85057 and 0.80536 for whole tumor, tumor core and enhancing tumor, respectively, on the challenge’s validation data set.


Confusion loss Domain adaptation Encoder-decoder network Brain tumor Segmentation 



This research was partially supported by Ministry of Science and Technology Major Project of China 2017YFC1310903, University of Hong Kong Stanley Ho Alumni Challenge Fund, University Research Committee Seed Funding Award 104004215, US National Science Foundation grants DMS-1407655, and NIH grants MH086633 and MH116527.


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Lutao Dai
    • 1
  • Tengfei Li
    • 2
  • Hai Shu
    • 3
  • Liming Zhong
    • 3
    • 4
  • Haipeng Shen
    • 1
  • Hongtu Zhu
    • 2
    Email author
  1. 1.Faculty of Business and EconomicsThe University of Hong KongPok Fu LamHong Kong
  2. 2.The Biomedical Research Imaging Center, Department of Radiology and Department of BiostatisticsUniversity of North Carolina at Chapel HillChapel HillUSA
  3. 3.Department of BiostatisticsThe University of Texas MD Anderson Cancer CenterHoustonUSA
  4. 4.Guangdong Provincial Key Laboratory of Medical Image Processing, School of Biomedical EngineeringSouthern Medical UniversityGuangzhouChina

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