CRF-Based Brain Tumor Segmentation: Alleviating the Shrinking Bias

  • Raphael MeierEmail author
  • Urspeter Knecht
  • Roland Wiest
  • Mauricio Reyes
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10154)


This paper extends a previously published brain tumor segmentation method with a dense Conditional Random Field (CRF). Dense CRFs can overcome the shrinking bias inherent to many grid-structured CRFs. We focus on illustrating the impact of alleviating the shrinking bias on the performance of CRF-based brain tumor segmentation. The proposed segmentation method is evaluated using data from the MICCAI BRATS 2013 & 2015 data sets (up to 110 patient cases for testing) and compared to a baseline method using a grid-structured CRF. Improved segmentation performance for the complete and enhancing tumor was observed with respect to grid-structured CRFs.



This project has received funding from the European Unions Seventh Framework Programme for research, technological development and demonstration under grant agreement Nº600841.


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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Raphael Meier
    • 1
    Email author
  • Urspeter Knecht
    • 2
  • Roland Wiest
    • 2
  • Mauricio Reyes
    • 1
  1. 1.Institute for Surgical Technology and BiomechanicsUniversity of BernBernSwitzerland
  2. 2.Support Center for Advanced Neuroimaging – Institute for Diagnostic and Interventional NeuroradiologyUniversity Hospital and University of BernBernSwitzerland

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