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.