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)


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.


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.


  1. 1.
    Fennema-Notestine, C., Ozyurt, I., Clark, C., Morris, S., Bischoff-Grethe, A., Bondi, M., Jernigan, T., Fischl, B., Segonne, F., Shattuck, D., Leahy, R., Rex, D., Toga, A., Zou, K., BIRN, Brown, G.: Quantitative evaluation of automated skull-stripping methods applied to contemporary and legacy images: effects of diagnosis, bias correction, and slice location. Human Brain Mapping 27(2), 99–113 (2006)CrossRefGoogle Scholar
  2. 2.
    Shattuck, D., Sandor-Leahy, S., Schaper, K., Rottenberg, D., Leahy, R.: Magnetic resonance image tissue classification using a partial volume model. NeuroImage 13(5), 856–876 (2001)CrossRefGoogle Scholar
  3. 3.
    Segonne, F., Dale, A., Busa, E., Glessner, M., Salat, D., Hahn, H., Fischl, B.: A hybrid approach to the skull stripping problem in MRI. Neuroimage 22(3), 1060–1075 (2004)CrossRefGoogle Scholar
  4. 4.
    Smith, S.: Fast robust automated brain extraction. Human Brain Mapping 17(3), 143–155 (2002)CrossRefGoogle Scholar
  5. 5.
    Iglesias, J., Liu, C., Thompson, P., Tu, Z.: Robust brain extraction across datasets and comparison with publicly available methods. IEEE Transactions on Medical Imaging (in press, 2011)Google Scholar
  6. 6.
    Grady, L.: Random walks for image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence 28(11), 1768–1783 (2006)CrossRefGoogle Scholar
  7. 7.
    Breiman, L.: Random forests. Machine Learning 45(1), 5–32 (2001)MathSciNetCrossRefzbMATHGoogle Scholar
  8. 8.
    Cootes, T., Taylor, C., Cooper, D., Graham, J.: Active shape models-their training and application. Computer Vision and Image Understanding 61(1), 38–59 (1995)CrossRefGoogle Scholar
  9. 9.
    Li, K., Wu, X., Chen, D., Sonka, M.: Optimal surface segmentation in volumetric images-a graph-theoretic approach. IEEE Transactions on Pattern Analysis and Machine Intelligence 28(1), 119–134 (2006)CrossRefGoogle Scholar
  10. 10.
    Sadananthan, S., Zheng, W., Chee, M., Zagorodnov, V.: Skull stripping using graph cuts. NeuroImage 49(1), 225–239 (2010)CrossRefGoogle Scholar
  11. 11.
    Mikheev, A., Nevsky, G., Govindan, S., Grossman, R., Rusinek, H.: Fully automatic segmentation of the brain from T1-weighted MRI using Bridge Burner algorithm. Journal of Magnetic Resonance Imaging 27(6), 1235–1241 (2008)CrossRefGoogle Scholar
  12. 12.
    Piper, J., Granum, E.: Computing distance transformations in convex and non-convex domains. Pattern Recognition 20(6), 599–615 (1987)CrossRefGoogle Scholar

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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