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@ARTICLE{Faber:165528,
      author       = {Faber, Jennifer and Kügler, David and Bahrami, Emad and
                      Heinz, Lea-Sophie and Timmann, Dagmar and Ernst, Thomas M
                      and Deike-Hofmann, Katerina and Klockgether, Thomas and van
                      de Warrenburg, Bart and van Gaalen, Judith and Reetz,
                      Kathrin and Romanzetti, Sandro and Oz, Gulin and Joers,
                      James M and Diedrichsen, Jorn and Reuter, Martin and Giunti,
                      Paola and Garcia-Moreno, Hector and Jacobi, Heike and Jende,
                      Johann and de Vries, Jeroen and Povazan, Michal and Barker,
                      Peter B and Steiner, Katherina Marie and Krahe, Janna},
      collaboration = {Group, ESMI MRI Study},
      title        = {{C}ereb{N}et: {A} fast and reliable deep-learning pipeline
                      for detailed cerebellum sub-segmentation.},
      journal      = {NeuroImage},
      volume       = {264},
      issn         = {1053-8119},
      address      = {Orlando, Fla.},
      publisher    = {Academic Press},
      reportid     = {DZNE-2022-01674},
      pages        = {119703},
      year         = {2022},
      abstract     = {Quantifying the volume of the cerebellum and its lobes is
                      of profound interest in various neurodegenerative and
                      acquired diseases. Especially for the most common
                      spinocerebellar ataxias (SCA), for which the first antisense
                      oligonculeotide-base gene silencing trial has recently
                      started, there is an urgent need for quantitative, sensitive
                      imaging markers at pre-symptomatic stages for stratification
                      and treatment assessment. This work introduces CerebNet, a
                      fully automated, extensively validated, deep learning method
                      for the lobular segmentation of the cerebellum, including
                      the separation of gray and white matter. For training,
                      validation, and testing, T1-weighted images from 30
                      participants were manually annotated into cerebellar lobules
                      and vermal sub-segments, as well as cerebellar white matter.
                      CerebNet combines FastSurferCNN, a UNet-based 2.5D
                      segmentation network, with extensive data augmentation, e.g.
                      realistic non-linear deformations to increase the anatomical
                      variety, eliminating additional preprocessing steps, such as
                      spatial normalization or bias field correction. CerebNet
                      demonstrates a high accuracy (on average 0.87 Dice and
                      1.742mm Robust Hausdorff Distance across all structures)
                      outperforming state-of-the-art approaches. Furthermore, it
                      shows high test-retest reliability (average ICC >0.97 on
                      OASIS and Kirby) as well as high sensitivity to disease
                      effects, including the pre-ataxic stage of spinocerebellar
                      ataxia type 3 (SCA3). CerebNet is compatible with FreeSurfer
                      and FastSurfer and can analyze a 3D volume within seconds on
                      a consumer GPU in an end-to-end fashion, thus providing an
                      efficient and validated solution for assessing cerebellum
                      sub-structure volumes. We make CerebNet available as
                      source-code (https://github.com/Deep-MI/FastSurfer).},
      keywords     = {Humans / Image Processing, Computer-Assisted: methods /
                      Deep Learning / Magnetic Resonance Imaging: methods /
                      Reproducibility of Results / Cerebellum: diagnostic imaging
                      / CerebNet (Other) / Cerebellum (Other) / Computational
                      neuroimaging (Other) / Deep learning (Other)},
      cin          = {Patient Studies (Bonn) / AG Reuter / AG Radbruch / Clinical
                      Research (Bonn)},
      ddc          = {610},
      cid          = {I:(DE-2719)1011101 / I:(DE-2719)1040310 /
                      I:(DE-2719)5000075 / I:(DE-2719)1011001},
      pnm          = {353 - Clinical and Health Care Research (POF4-353) / 354 -
                      Disease Prevention and Healthy Aging (POF4-354)},
      pid          = {G:(DE-HGF)POF4-353 / G:(DE-HGF)POF4-354},
      typ          = {PUB:(DE-HGF)16},
      pmc          = {pmc:PMC9771831},
      pubmed       = {pmid:36349595},
      doi          = {10.1016/j.neuroimage.2022.119703},
      url          = {https://pub.dzne.de/record/165528},
}