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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},
}