TY  - CONF
AU  - Conjeti, Sailesh
TI  - Inherent Brain Segmentation Quality Control from Fully ConvNet Monte Carlo Sampling
M1  - DZNE-2020-00969
PY  - 2018
AB  - 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.
T2  - MICCAI 2018
CY  - 16 Sep 2018 - 16 Sep 2018, Granada (Spain)
Y2  - 16 Sep 2018 - 16 Sep 2018
M2  - Granada, Spain
LB  - PUB:(DE-HGF)6
UR  - https://pub.dzne.de/record/145639
ER  -