  We introduce inherent measures for effective quality control of brain
segmentation based on a Bayesian fully convolutional neural network, using
model uncertainty. Monte Carlo samples from the posterior distribution are
efficiently generated using dropout at test time. Based on these samples, we
introduce next to a voxel-wise uncertainty map also three metrics for
structure-wise uncertainty. We then incorporate these structure-wise
uncertainty in group analyses as a measure of confidence in the observation.
Our results show that the metrics are highly correlated to segmentation
accuracy and therefore present an inherent measure of segmentation quality.
Furthermore, group analysis with uncertainty results in effect sizes closer to
that of manual annotations. The introduced uncertainty metrics can not only be
very useful in translation to clinical practice but also provide automated
quality control and group analyses in processing large data repositories.
