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.

本文介绍了基于贝叶斯全卷积神经网络的脑分割的有效质量控制的固有度量方法，使用模型不确定性进行有效蒙特卡罗采样来生成体素级不确定性图和三个结构级不确定度量标准，并将这些结构级不确定性度量标准加入到群体分析中作为信心度量，因此提供自动化质量控制和群体分析的方法，可用于大规模数据库的处理。