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

Inherent Brain Segmentation Quality Control from Fully ConvNet Monte Carlo Sampling