Robust Deformable-Surface-Based Skull-Stripping for Large-Scale Studies

  • Yaping Wang
  • Jingxin Nie
  • Pew-Thian Yap
  • Feng Shi
  • Lei Guo
  • Dinggang Shen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6893)


Skull-stripping refers to the separation of brain tissue from non-brain tissue, such as the scalp, skull, and dura. In large-scale studies involving a significant number of subjects, a fully automatic method is highly desirable, since manual skull-stripping requires tremendous human effort and can be inconsistent even after sufficient training. We propose in this paper a robust and effective method that is capable of skull-stripping a large number of images accurately with minimal dependence on the parameter setting. The key of our method involves an initial skull-stripping by co-registration of an atlas, followed by a refinement phase with a surface deformation scheme that is guided by prior information obtained from a set of real brain images. Evaluation based on a total of 831 images, consisting of normal controls (NC) and patients with mild cognitive impairment (MCI) or Alzheimer’s Disease (AD), from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database indicates that our method performs favorably at a consistent overall overlap rate of approximately 98% when compared with expert results. The software package will be made available to the public to facilitate neuroimaging studies.


Mild Cognitive Impairment Active Contour Brain Mask Brain Extraction Tool Brain Boundary 
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

  • Yaping Wang
    • 1
    • 2
  • Jingxin Nie
    • 2
  • Pew-Thian Yap
    • 2
  • Feng Shi
    • 2
  • Lei Guo
    • 1
  • Dinggang Shen
    • 2
  1. 1.School of AutomationNorthwestern Polytechnical UniversityXi’anChina
  2. 2.Department of Radiology and BRICUniversity of North Carolina at Chapel HillUSA

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