Inherent Brain Segmentation Quality Control from Fully ConvNet Monte Carlo Sampling

Understand

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.