  Brain extraction or skull stripping of magnetic resonance images (MRI) is an
essential step in neuroimaging studies, the accuracy of which can severely
affect subsequent image processing procedures. Current automatic brain
extraction methods demonstrate good results on human brains, but are often far
from satisfactory on nonhuman primates, which are a necessary part of
neuroscience research. To overcome the challenges of brain extraction in
nonhuman primates, we propose a fully-automated brain extraction pipeline
combining deep Bayesian convolutional neural network (CNN) and fully connected
three-dimensional (3D) conditional random field (CRF). The deep Bayesian CNN,
Bayesian SegNet, is used as the core segmentation engine. As a probabilistic
network, it is not only able to perform accurate high-resolution pixel-wise
brain segmentation, but also capable of measuring the model uncertainty by
Monte Carlo sampling with dropout in the testing stage. Then, fully connected
3D CRF is used to refine the probability result from Bayesian SegNet in the
whole 3D context of the brain volume. The proposed method was evaluated with a
manually brain-extracted dataset comprising T1w images of 100 nonhuman
primates. Our method outperforms six popular publicly available brain
extraction packages and three well-established deep learning based methods with
a mean Dice coefficient of 0.985 and a mean average symmetric surface distance
of 0.220 mm. A better performance against all the compared methods was verified
by statistical tests (all p-values<10-4, two-sided, Bonferroni corrected). The
maximum uncertainty of the model on nonhuman primate brain extraction has a
mean value of 0.116 across all the 100 subjects...
