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@ARTICLE{Schiffer:893920,
      author       = {Schiffer, Christian and Harmeling, Stefan and Amunts,
                      Katrin and Dickscheid, Timo},
      title        = {2{D} histology meets 3{D} topology: {C}ytoarchitectonic
                      brain mapping with {G}raph {N}eural {N}etworks},
      reportid     = {FZJ-2021-02930},
      year         = {2021},
      abstract     = {Cytoarchitecture describes the spatial organization of
                      neuronal cells in the brain, including their arrangement
                      into layers and columns with respect to cell density,
                      orientation, or presence of certain cell types. It allows to
                      segregate the brain into cortical areas and subcortical
                      nuclei, links structure with connectivity and function, and
                      provides a microstructural reference for human brain
                      atlases. Mapping boundaries between areas requires to scan
                      histological sections at microscopic resolution. While
                      recent high-throughput scanners allow to scan a complete
                      human brain in the order of a year, it is practically
                      impossible to delineate regions at the same pace using the
                      established gold standard method. Researchers have recently
                      addressed cytoarchitectonic mapping of cortical regions with
                      deep neural networks, relying on image patches from
                      individual 2D sections for classification. However, the 3D
                      context, which is needed to disambiguate complex or
                      obliquely cut brain regions, is not taken into account. In
                      this work, we combine 2D histology with 3D topology by
                      reformulating the mapping task as a node classification
                      problem on an approximate 3D midsurface mesh through the
                      isocortex. We extract deep features from cortical patches in
                      2D histological sections which are descriptive of
                      cytoarchitecture, and assign them to the corresponding nodes
                      on the 3D mesh to construct a large attributed graph. By
                      solving the brain mapping problem on this graph using graph
                      neural networks, we obtain significantly improved
                      classification results. The proposed framework lends itself
                      nicely to integration of additional neuroanatomical priors
                      for mapping.},
      cin          = {INM-1},
      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)
                      / 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:(DE-Juel-1)E.40401.62},
      typ          = {PUB:(DE-HGF)25},
      eprint       = {2103.05259},
      howpublished = {arXiv:2103.05259},
      archivePrefix = {arXiv},
      SLACcitation = {$\%\%CITATION$ = $arXiv:2103.05259;\%\%$},
      url          = {https://juser.fz-juelich.de/record/893920},
}