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@INPROCEEDINGS{Schiffer:1031455,
      author       = {Schiffer, Christian and Amunts, Katrin and Dickscheid,
                      Timo},
      title        = {{C}yto{N}et: {A} {D}eep {N}eural {N}etwork for
                      {W}hole-brain {C}haracterization of {H}uman
                      {C}ytoarchitecture},
      school       = {Heinrich-Heine-University Düsseldorf},
      reportid     = {FZJ-2024-05674},
      year         = {2024},
      abstract     = {The characterization of cytoarchitecture in the human brain
                      provides an essential building block for the creation of a
                      high-resolution multi-modal brain atlas. Cytoarchitecture is
                      defined by the spatial organization of neuronal cells,
                      including their shape, density, size, cell type, as well as
                      their columnar and laminar arrangement, which differ between
                      brain regions. High-throughput light-microscopic scanning of
                      large, cell-body stained histological sections obtained by
                      sectioning postmortem human brains enables detailed
                      examination of cytoarchitectonic organizational principles
                      across multiple brain samples, which is mandatory to capture
                      the highly variable cytoarchitectonic organization. The
                      limited scalability of existing methods to image and analyze
                      datasets in the terabyte to petabyte range motivates current
                      developments of AI methods for data-driven characterization
                      and classification of human cytoarchitecture at large
                      scale.In this work, we present CytoNet, a deep neural
                      network model that enables data-driven characterization of
                      cytoarchitecture in the human brain. CytoNet is a
                      convolutional neural network that is trained on 200 000
                      image patches (2048px@2μm/px) extracted from 4115
                      histological sections of 9 postmortem brains. The model is
                      trained using a novel contrastive learning objective that
                      derives the similarity relationship between image samples
                      from their spatial distance in a common reference brain
                      space. Using this loss, CytoNet is trained to map spatially
                      close image samples, which likely show similar
                      cytoarchitectonic structures, to similar feature
                      representations.We demonstrate that feature representations
                      extracted by CytoNet allow classifying cytoarchitectonic
                      areas, predicting spatial and morphological features,
                      studying inter-individual variations, and enabling
                      data-driven quantification and query-based exploration of
                      microstructural principles at whole-brain level. Moreover,
                      we show that the latent space learned by CytoNet exhibits an
                      anatomically highly plausible structure that facilitates
                      intuitive exploration of brain organization. CytoNet
                      significantly extends existing methods for cytoarchitecture
                      analysis and thus provides the foundation for novel analysis
                      workflows that have the potential to facilitate studies
                      relating the brain’s microstructure to connectivity and
                      function.},
      month         = {Sep},
      date          = {2024-09-09},
      organization  = {8th BigBrain Workshop, Padua (Italy),
                       9 Sep 2024 - 11 Sep 2024},
      subtyp        = {After Call},
      cin          = {INM-1},
      cid          = {I:(DE-Juel1)INM-1-20090406},
      pnm          = {5254 - Neuroscientific Data Analytics and AI (POF4-525) /
                      5251 - Multilevel Brain Organization and Variability
                      (POF4-525) / HIBALL - Helmholtz International BigBrain
                      Analytics and Learning Laboratory (HIBALL) (InterLabs-0015)
                      / EBRAINS 2.0 - EBRAINS 2.0: A Research Infrastructure to
                      Advance Neuroscience and Brain Health (101147319) /
                      Helmholtz AI - Helmholtz Artificial Intelligence
                      Coordination Unit – Local Unit FZJ (E.40401.62) / X-BRAIN
                      (ZT-I-PF-4-061)},
      pid          = {G:(DE-HGF)POF4-5254 / G:(DE-HGF)POF4-5251 /
                      G:(DE-HGF)InterLabs-0015 / G:(EU-Grant)101147319 /
                      G:(DE-Juel-1)E.40401.62 / G:(DE-HGF)ZT-I-PF-4-061},
      typ          = {PUB:(DE-HGF)6},
      url          = {https://juser.fz-juelich.de/record/1031455},
}