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@ARTICLE{Schiffer:893979,
      author       = {Schiffer, Christian and Spitzer, Hannah and Kiwitz, Kai and
                      Unger, Nina and Wagstyl, Konrad and Evans, Alan C. and
                      Harmeling, Stefan and Amunts, Katrin and Dickscheid, Timo},
      title        = {{C}onvolutional neural networks for cytoarchitectonic brain
                      mapping at large scale},
      journal      = {NeuroImage},
      volume       = {240},
      issn         = {1053-8119},
      address      = {Orlando, Fla.},
      publisher    = {Academic Press},
      reportid     = {FZJ-2021-02964},
      pages        = {118327 -},
      year         = {2021},
      abstract     = {Human brain atlases provide spatial reference systems for
                      data characterizing brain organization at different levels,
                      coming from different brains. Cytoarchitecture is a basic
                      principle of the microstructural organization of the brain,
                      as regional differences in the arrangement and composition
                      of neuronal cells are indicators of changes in connectivity
                      and function. Automated scanning procedures and
                      observer-independent methods are prerequisites to reliably
                      identify cytoarchitectonic areas, and to achieve
                      reproducible models of brain segregation. Time becomes a key
                      factor when moving from the analysis of single regions of
                      interest towards high-throughput scanning of large series of
                      whole-brain sections. Here we present a new workflow for
                      mapping cytoarchitectonic areas in large series of cell-body
                      stained histological sections of human postmortem brains. It
                      is based on a Deep Convolutional Neural Network (CNN), which
                      is trained on a pair of section images with annotations,
                      with a large number of un-annotated sections in between. The
                      model learns to create all missing annotations in between
                      with high accuracy, and faster than our previous workflow
                      based on observer-independent mapping. The new workflow does
                      not require preceding 3D-reconstruction of sections, and is
                      robust against histological artefacts. It processes large
                      data sets with sizes in the order of multiple Terabytes
                      efficiently. The workflow was integrated into a web
                      interface, to allow access without expertise in deep
                      learning and batch computing. Applying deep neural networks
                      for cytoarchitectonic mapping opens new perspectives to
                      enable high-resolution models of brain areas, introducing
                      CNNs to identify borders of brain areas.},
      cin          = {INM-1},
      ddc          = {610},
      cid          = {I:(DE-Juel1)INM-1-20090406},
      pnm          = {5254 - Neuroscientific Data Analytics and AI (POF4-525) /
                      HBP SGA3 - Human Brain Project Specific Grant Agreement 3
                      (945539) / HIBALL - Helmholtz International BigBrain
                      Analytics and Learning Laboratory (HIBALL) (InterLabs-0015)
                      / DFG project 347572269 - Heterogenität von
                      Zytoarchitektur, Chemoarchitektur und Konnektivität in
                      einem großskaligen Computermodell der menschlichen
                      Großhirnrinde (347572269) / Helmholtz AI - Helmholtz
                      Artificial Intelligence Coordination Unit – Local Unit FZJ
                      (E.40401.62)},
      pid          = {G:(DE-HGF)POF4-5254 / G:(EU-Grant)945539 /
                      G:(DE-HGF)InterLabs-0015 / G:(GEPRIS)347572269 /
                      G:(DE-Juel-1)E.40401.62},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {34224853},
      UT           = {WOS:000693361400007},
      doi          = {10.1016/j.neuroimage.2021.118327},
      url          = {https://juser.fz-juelich.de/record/893979},
}