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Combining Generative Models for Multifocal Glioma Segmentation and Registration

  • Dongjin Kwon
  • Russell T. Shinohara
  • Hamed Akbari
  • Christos Davatzikos
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8673)

Abstract

In this paper, we propose a new method for simultaneously segmenting brain scans of glioma patients and registering these scans to a normal atlas. Performing joint segmentation and registration for brain tumors is very challenging when tumors include multifocal masses and have complex shapes with heterogeneous textures. Our approach grows tumors for each mass from multiple seed points using a tumor growth model and modifies a normal atlas into one with tumors and edema using the combined results of grown tumors. We also generate a tumor shape prior via the random walk with restart, utilizing multiple tumor seeds as initial foreground information. We then incorporate this shape prior into an EM framework which estimates the mapping between the modified atlas and the scans, posteriors for each tissue labels, and the tumor growth model parameters. We apply our method to the BRATS 2013 leaderboard dataset to evaluate segmentation performance. Our method shows the best performance among all participants.

Keywords

Spatial Probability Tumor Seed Tumor Shape Random Walk With Restart Tumor Parameter 
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

  • Dongjin Kwon
    • 1
  • Russell T. Shinohara
    • 2
    • 1
  • Hamed Akbari
    • 1
  • Christos Davatzikos
    • 1
  1. 1.Center for Biomedical Image Computing and AnalyticsUniversity of PennsylvaniaUSA
  2. 2.Department of Biostatistics and EpidemiologyUniversity of PennsylvaniaUSA

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