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Fast robust automated brain extraction

Human Brain Mapping · 2002 · Vol. 17(3) · pp. 143–155
Stephen M. Smith

Abstract

An automated method for segmenting magnetic resonance head images into brain and non-brain has been developed. It is very robust and accurate and has been tested on thousands of data sets from a wide variety of scanners and taken with a wide variety of MR sequences. The method, Brain Extraction Tool (BET), uses a deformable model that evolves to fit the brain's surface by the application of a set of locally adaptive model forces. The method is very fast and requires no preregistration or other pre-processing before being applied. We describe the new method and give examples of results and the results of extensive quantitative testing against "gold-standard" hand segmentations, and two other popular automated methods.

Medical Image Segmentation TechniquesAdvanced Neural Network ApplicationsMedical Imaging Techniques and ApplicationsComputer scienceArtificial intelligenceSegmentationSet (abstract data type)Automated methodGold standard (test)Pattern recognition (psychology)Variety (cybernetics)Computer visionImage processing

MeSH terms

AlgorithmsAnimalsBrainHumansMagnetic Resonance Imaging

Funding

  • Università degli Studi di Siena
  • Medical Research Council
Citations
10,749
FWCI
38.29
field-weighted impact
References
14
Percentile
100%
vs. same field & year
Citations per year
References
Snakes: Active contour models
International Journal of Computer Vision · 1988 · 16,976 citations
Cortical Surface-Based Analysis
NeuroImage · 1999 · 11,343 citations
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