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@MISC{Schiffer:911742,
      author       = {Schiffer, Christian and Kedo, O. and Amunts, Katrin and
                      Dickscheid, Timo},
      title        = {{U}ltrahigh resolution 3{D} cytoarchitectonic map of the
                      human amygdala created by a {D}eep-{L}earning assisted
                      workflow (v1)},
      publisher    = {EBRAINS},
      reportid     = {FZJ-2022-04994},
      year         = {2022},
      abstract     = {This dataset contains automatically created detailed map of
                      13 cytoarchitectonic subdivisions of the amygdala and 6
                      fiber bundles in the BigBrain dataset. The mappings were
                      created using Deep Convolutional Neural Networks based on
                      Schiffer et al 2021, which were trained on delineations on
                      at least every 15th section created based on Kedo et al
                      2018. Mappings are available on every section. Their quality
                      was observed by a trained neuroscientist to exclude sections
                      with low quality results from further processing. Automatic
                      mappings were transformed to the 3D reconstructed BigBrain
                      space using transformations used in Amunts et al 2013, which
                      were provided by Claude Lepage (McGill). Mappings on
                      individual sections were used to assemble 3D volumes of all
                      areas. Low quality results were replaced by interpolation
                      between nearest neighboring sections. The volumes were then
                      smoothed using a 3D median filter and largest connected
                      components were identified to remove false positive results
                      of the classification algorithm. The dataset consists of a
                      HDF5 file containing the volume in RAS dimension ordering
                      (20-micron isotropic resolution, dataset “volume”) and
                      an affine transformation matrix (dataset “affine”). An
                      additional dataset $“interpolation_info”$ contains an
                      integer vector with an integer value for each section which
                      indicates if a section was replaced by interpolation due to
                      low quality results (value 2) or not (value 1). Due to the
                      large size of the volume, it is recommended to view the data
                      online using the provided viewer link.},
      keywords     = {Neuroscience (Other)},
      cin          = {INM-1},
      cid          = {I:(DE-Juel1)INM-1-20090406},
      pnm          = {5251 - Multilevel Brain Organization and Variability
                      (POF4-525) / HBP SGA3 - Human Brain Project Specific Grant
                      Agreement 3 (945539)},
      pid          = {G:(DE-HGF)POF4-5251 / G:(EU-Grant)945539},
      typ          = {PUB:(DE-HGF)32},
      doi          = {10.25493/TKTP-7NR},
      url          = {https://juser.fz-juelich.de/record/911742},
}