Robust deformable-surface-based skull-stripping for large-scale studies.

Yaping Wang, Jingxin Nie, Pew Thian Yap, Feng Shi, Lei Guo, Dinggang Shen

Research output: Chapter in Book/Report/Conference proceedingChapter

77 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationMedical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
Pages635-642
Number of pages8
Volume14
EditionPt 3
Publication statusPublished - 2011 Dec 1

ASJC Scopus subject areas

  • Medicine(all)

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  • Cite this

    Wang, Y., Nie, J., Yap, P. T., Shi, F., Guo, L., & Shen, D. (2011). Robust deformable-surface-based skull-stripping for large-scale studies. In Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention (Pt 3 ed., Vol. 14, pp. 635-642)