Brain extraction of MR images is an essential step in neuroimaging, but current brain extraction methods are often far from satisfactory on nonhuman primates. To overcome this challenge, we propose a fully-automated brain extraction framework combining deep Bayesian convolutional neural network and fully connected three-dimensional conditional random field. It is not only able to perform accurate brain extraction in a fully three-dimensional context, but also capable of generating uncertainty on each prediction. The proposed method outperforms six popular methods on a 100-subject dataset, and a better performance was verified by different metrics and statistical tests (Bonferroni corrected p-values<10-4).
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