BEaST: Brain extraction using multiresolution nonlocal segmentation.

Abstract : Brain extraction is an important step in the analysis of brain images. Variability in brain morphology and intensity characteristics due to different imaging sequences makes the development of a general pur- pose brain extraction algorithm challenging. Purpose: To address this issue, we propose a new robust method (BEaST) for brain extraction. Methods: The method is based on nonlocal segmentation embedded in a multiresolution framework. A library of 50 priors are semi-automatically constructed from the NIHPD, ICBM, and ADNI databases. Results: A mean Dice coefficient of 0.9834±0.0053 is obtained when performing leave-one-out cross validation. Validation using the online available Seg- mentation Validation Engine resulted in a top ranking position with a mean Dice coefficient of 0.9781±0.0047. Conclusions: The segmentation accuracy of the method is comparable to that of a recent label fusion ap- proach, while being 40 times faster and requiring a much smaller library of priors.
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Contributeur : Pierrick Coupé <>
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Dernière modification le : mardi 24 avril 2018 - 16:16:02
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  • HAL Id : hal-00614307, version 1

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Simon Eskildsen, Pierrick Coupé, Kelvin Leung, Vladimir Fonov, Nicolas Guizard, et al.. BEaST: Brain extraction using multiresolution nonlocal segmentation.. MICCAI Workshop on Multi-Atlas Labeling and Statistical Fusion, Sep 2011, Toronto, Canada. pp.97-108, 2011. 〈hal-00614307〉

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