Simple paradigm for extra-cerebral tissue removal: Algorithm and analysis

Aaron Carass, Jennifer Cuzzocreo, M. Bryan Wheeler, Pierre Louis Bazin, Susan M. Resnick, Jerry L. Prince

Research output: Contribution to journalArticlepeer-review

Abstract

Extraction of the brain-i.e. cerebrum, cerebellum, and brain stem-from T1-weighted structural magnetic resonance images is an important initial step in neuroimage analysis. Although automatic algorithms are available, their inconsistent handling of the cortical mantle often requires manual interaction, thereby reducing their effectiveness. This paper presents a fully automated brain extraction algorithm that incorporates elastic registration, tissue segmentation, and morphological techniques which are combined by a watershed principle, while paying special attention to the preservation of the boundary between the gray matter and the cerebrospinal fluid. The approach was evaluated by comparison to a manual rater, and compared to several other leading algorithms on a publically available data set of brain images using the Dice coefficient and containment index as performance metrics. The qualitative and quantitative impact of this initial step on subsequent cortical surface generation is also presented. Our experiments demonstrate that our approach is quantitatively better than six other leading algorithms (with statistical significance on modern T1-weighted MR data). We also validated the robustness of the algorithm on a very large data set of over one thousand subjects, and showed that it can replace an experienced manual rater as preprocessing for a cortical surface extraction algorithm with statistically insignificant differences in cortical surface position.

Original languageEnglish (US)
Pages (from-to)1982-1992
Number of pages11
JournalNeuroImage
Volume56
Issue number4
DOIs
StatePublished - Jun 15 2011

Keywords

  • Brain extraction
  • Medical image processing
  • Segmentation
  • Skull stripping
  • Watershed principle

ASJC Scopus subject areas

  • Neurology
  • Cognitive Neuroscience

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