Robust Skull Stripping of Clinical Glioblastoma Multiforme Data

  • William Speier
  • Juan E. Iglesias
  • Leila El-Kara
  • Zhuowen Tu
  • Corey Arnold
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6893)

Abstract

Skull stripping is the first step in many neuroimaging analyses and its success is critical to all subsequent processing. Methods exist to skull strip brain images without gross deformities, such as those affected by Alzheimer’s and Huntington’s disease. However, there are no techniques for extracting brains affected by diseases that significantly disturb normal anatomy. Glioblastoma multiforme (GBM) is such a disease, as afflicted individuals develop large tumors that often require surgical resection. In this paper, we extend the ROBEX skull stripping method to extract brains from GBM images. The proposed method uses a shape model trained on healthy brains to be relatively insensitive to lesions inside the brain. The brain boundary is then searched for potential resection cavities using adaptive thresholding and the Random Walker algorithm corrects for leakage into the ventricles. The results show significant improvement over three popular skull stripping algorithms (BET, BSE and HWA) in a dataset of 48 GBM cases.

Keywords

Random Forest Glioblastoma Multiforme Resection Cavity Adaptive Thresholding Random Walker Algorithm 
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-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • William Speier
    • 1
  • Juan E. Iglesias
    • 1
  • Leila El-Kara
    • 1
  • Zhuowen Tu
    • 1
  • Corey Arnold
    • 1
  1. 1.University of CaliforniaLos AngelesUSA

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