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@INPROCEEDINGS{Conjeti:145639,
author = {Conjeti, Sailesh},
title = {{I}nherent {B}rain {S}egmentation {Q}uality {C}ontrol from
{F}ully {C}onv{N}et {M}onte {C}arlo {S}ampling},
reportid = {DZNE-2020-00969},
year = {2018},
abstract = {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.},
month = {Sep},
date = {2018-09-16},
organization = {MICCAI 2018, Granada (Spain), 16 Sep
2018 - 16 Sep 2018},
subtyp = {Other},
cin = {AG Reuter},
cid = {I:(DE-2719)1040310},
pnm = {345 - Population Studies and Genetics (POF3-345)},
pid = {G:(DE-HGF)POF3-345},
typ = {PUB:(DE-HGF)6},
url = {https://pub.dzne.de/record/145639},
}