Patient-Specific Semi-supervised Learning for Postoperative Brain Tumor Segmentation

  • Raphael Meier
  • Stefan Bauer
  • Johannes Slotboom
  • Roland Wiest
  • Mauricio Reyes
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8673)


In contrast to preoperative brain tumor segmentation, the problem of postoperative brain tumor segmentation has been rarely approached so far. We present a fully-automatic segmentation method using multimodal magnetic resonance image data and patient-specific semi-supervised learning. The idea behind our semi-supervised approach is to effectively fuse information from both pre- and postoperative image data of the same patient to improve segmentation of the postoperative image. We pose image segmentation as a classification problem and solve it by adopting a semi-supervised decision forest. The method is evaluated on a cohort of 10 high-grade glioma patients, with segmentation performance and computation time comparable or superior to a state-of-the-art brain tumor segmentation method. Moreover, our results confirm that the inclusion of preoperative MR images lead to a better performance regarding postoperative brain tumor segmentation.


Postoperative Image Residual Tumor Volume Decision Forest Brain Tumor Image Leaf Statistic 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Raphael Meier
    • 1
  • Stefan Bauer
    • 1
    • 2
  • Johannes Slotboom
    • 2
  • Roland Wiest
    • 2
  • Mauricio Reyes
    • 1
  1. 1.Institute for Surgical Technologies and BiomechanicsUniversity of BernSwitzerland
  2. 2.InselspitalBern University HospitalSwitzerland

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