Boundary-aware fully convolutional network for brain tumor segmentation

Haocheng Shen (Lead / Corresponding author), Ruixuan Wang, Jianguo Zhang, Stephen J. McKenna

Research output: Chapter in Book/Report/Conference proceedingConference contribution

78 Citations (Scopus)
835 Downloads (Pure)


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.

Original languageEnglish
Title of host publicationMedical Image Computing and Computer Assisted Intervention − MICCAI 2017 - 20th International Conference, Proceedings
Place of PublicationSwitzerland
PublisherSpringer Verlag
Number of pages9
ISBN (Electronic)9783319661858
ISBN (Print)9783319661841
Publication statusPublished - 2017
Event20th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2017 - Quebec City, Canada
Duration: 11 Sept 201713 Sept 2017

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference20th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2017
CityQuebec City


  • Deep learning
  • Multi-task learning
  • Tumor segmentation

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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