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@INPROCEEDINGS{Schiffer:1033582,
      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},
      reportid     = {FZJ-2024-06462},
      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 one million
                      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 and cortical layers, 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         = {Nov},
      date          = {2024-11-19},
      organization  = {INM Retreat 2024, Jülich (Germany),
                       19 Nov 2024 - 20 Nov 2024},
      subtyp        = {After Call},
      cin          = {INM-1},
      cid          = {I:(DE-Juel1)INM-1-20090406},
      pnm          = {5251 - Multilevel Brain Organization and Variability
                      (POF4-525) / 5254 - Neuroscientific Data Analytics and AI
                      (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)POF4-5251 / G:(DE-HGF)POF4-5254 /
                      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)24},
      url          = {https://juser.fz-juelich.de/record/1033582},
}