Fully Connected Crf With Data-driven Prior for Multi-class Brain Tumor Segmentation

Haocheng Shen, Jianguo Zhang

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

10 Citations (Scopus)
358 Downloads (Pure)


Grid conditional random fields (CRFs) are widely applied in both natural and medical image segmentation tasks. However, they only consider the label coherence in neighborhood pixels or regions, which limits their ability to model long-range connections within the image and generally results in excessive smoothing of tumor boundaries. In this paper, we present a novel method for brain tumor segmentation in MR images based on fully-connected CRF (FC-CRF) model that establishes pairwise potentials on all pairs of pixels in the images. We employ a hierarchical approach to differentiate different structures of tumor and further formulate a FC-CRF model with learned data-driven prior knowledge of tumor core. The methods were evaluated on the testing and leaderboard set of Brain Tumor Image Segmentation Benchmark (BRATS) 2013 challenge. The precision of segmented tumor boundaries is improved significantly and the results are competitive compared to the start-of-the-arts.
Original languageEnglish
Title of host publication2017 IEEE International Conference on Image Processing (ICIP)
Subtitle of host publication17-20 Sept. 2017
Number of pages5
ISBN (Electronic)9781509021758
ISBN (Print)9781509021765
Publication statusPublished - 22 Feb 2018
EventThe International Conference on Image Processing 2017 - China National Convention Center, Beijing, China
Duration: 17 Sept 201720 Dec 2017

Publication series

NameIEEE Conference Proceedings
ISSN (Electronic)2381-8549


ConferenceThe International Conference on Image Processing 2017
Abbreviated titleIEEE ICIP 2017


  • Tumors
  • Image segmentation
  • Brain modeling
  • Training
  • Testing
  • Three-dimensional displays
  • Kernel
  • CRF
  • Prior
  • Brain tumor segmentation

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition


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