Boundary-Aware Fully Convolutional Network for Brain Tumor Segmentation

  • Haocheng Shen
  • Ruixuan Wang
  • Jianguo Zhang
  • Stephen J. McKenna
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10434)


We propose a novel, multi-task, fully convolutional network (FCN) architecture for automatic segmentation of brain tumor. This network extracts multi-level contextual information by concatenating hierarchical feature representations extracted from multimodal MR images along with their symmetric-difference images. It achieves improved segmentation performance by incorporating boundary information directly into the loss function. The proposed method was evaluated on the BRATS13 and BRATS15 datasets and compared with competing methods on the BRATS13 testing set. Segmented tumor boundaries obtained were better than those obtained by single-task FCN and by FCN with CRF. The method is among the most accurate available and has relatively low computational cost at test time.


Deep learning Tumor segmentation Multi-task learning 



This work was supported partially by the National Natural Science Foundation of China (No. 61628212).


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Haocheng Shen
    • 1
  • Ruixuan Wang
    • 1
  • Jianguo Zhang
    • 1
  • Stephen J. McKenna
    • 1
  1. 1.Computing, School of Science and EngineeringUniversity of DundeeDundeeUK

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