Parameter Learning for CRF-Based Tissue Segmentation of Brain Tumors

  • Raphael Meier
  • Venetia Karamitsou
  • Simon Habegger
  • Roland Wiest
  • Mauricio Reyes
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9556)


In this work, we investigated the potential of a recently proposed parameter learning algorithm for Conditional Random Fields (CRFs). Parameters of a pairwise CRF are estimated via a stochastic subgradient descent of a max-margin learning problem. We compared the performance of our brain tumor segmentation method using parameter learning to a version using hand-tuned parameters. Preliminary results on a subset of the BRATS2015 training set show that parameter learning leads to comparable or even improved performance. In addition, we also performed experiments to study the impact of the composition of training data on the final segmentation performance. We found that models trained on mixed data sets achieve reasonable performance compared to models trained on stratified data.


Brain tumor segmentation Structured learning Decision forest Conditional random field 



This project has received funding from the European Unions Seventh Framework Programme for research, technological development and demonstration under grant agreement No. 600841.


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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Raphael Meier
    • 1
  • Venetia Karamitsou
    • 1
  • Simon Habegger
    • 2
    • 3
  • Roland Wiest
    • 2
    • 3
  • Mauricio Reyes
    • 1
  1. 1.Institute for Surgical Technologies and BiomechanicsUniversity of BernBernSwitzerland
  2. 2.Support Center for Advanced Neuroimaging – Institute for Diagnostic and Interventional NeuroradiologyUniversity HospitalAugustaUSA
  3. 3.University of BernBernSwitzerland

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