% IMPORTANT: The following is UTF-8 encoded.  This means that in the presence
% of non-ASCII characters, it will not work with BibTeX 0.99 or older.
% Instead, you should use an up-to-date BibTeX implementation like “bibtex8” or
% “biber”.

@INPROCEEDINGS{Lothmann:1048760,
      author       = {Lothmann, Kimberley and Schiffer, Christian and Dickscheid,
                      Timo and Amunts, Katrin},
      title        = {{G}oing 3{D} with {AI}: {F}ull 3{D} {R}econstructions of
                      {C}ytoarchitectonic {M}aps in {B}ig{B}rain},
      reportid     = {FZJ-2025-04875},
      year         = {2025},
      abstract     = {<b>Introduction:</b><br>As part of the Julich-Brain Atlas
                      (Amunts et al., 2020), the BigBrain dataset (Amunts et al.,
                      2013) provides the first ultrahigh-resolution 3D model of
                      the human brain at 20 μm isotropic resolution,
                      reconstructed from 7,404 histological sections. It enables
                      cytoarchitectonic mapping at a level of detail that bridges
                      microscopic organization with macroscale imaging.
                      Traditionally, areas have been delineated on 2D sections,
                      limiting their integration into 3D brain reference
                      spaces.<br><br><b>Methods:</b><br>We developed a hybrid
                      workflow combining expert identification of cortical areas
                      with deep learningbased 3D reconstruction. Using the ATLaSUI
                      interface (Schiffer et al., 2021), neuroscientists annotated
                      every 10th – 15th histological section, providing training
                      data for the CytoNet model (manuscript in preparation).
                      CytoNet infers cortical layer continuity and areal
                      boundaries between annotated sections, avoiding geometric
                      interpolation and preserving cytoarchitectonic detail.
                      Largescale model inference and reconstruction were performed
                      on the JURECA-DC supercomputer (Jülich Supercomputing
                      Centre).<br><br><b>Results:</b><br>Currently, 33 cortical
                      BigBrain areas are publicly available through the siibra
                      tool suite and EBRAINS Knowledge Graph. An additional 56
                      cortical areas, including eight newly mapped regions, were
                      reconstructed at 20 μm resolution, expanding the
                      Julich-Brain Atlas to a total of 98 BigBrain areas. All maps
                      will be openly accessible via EBRAINS, enabling interactive
                      and programmatic exploration within a unified reference
                      framework.<br><br><b>Discussion:</b><br>Since areas were
                      reconstructed independently, minor overlaps can occur at
                      region borders, especially in highly folded cortical zones.
                      Sampling every 10th – 15th section may also introduce
                      interpolation artifacts in regions with steep
                      cytoarchitectonic transitions. Future work will focus on
                      multi-area optimization to reduce boundary inconsistencies
                      and improve 3D continuity. These ongoing developments
                      advance the integration of microstructural and macroscale
                      brain data within a coherent human brain reference
                      space.<br><br><b>Keywords:</b> BigBrain, Julich Brain Atlas,
                      Cytoarchitecture, 3D Reconstruction, Deep Learning, Human
                      Brain Atlas, EBRAINS<br><br><b>References</b><br>Amunts K,
                      Lepage C, Borgeat L, et al. BigBrain: An
                      ultrahigh-resolution 3D human brain model. Science.
                      2013;340(6139):1472-1475.
                      doi:10.1126/science.1235381<br>Amunts K, Mohlberg H, Bludau
                      S, Zilles K. Julich-Brain: A 3D probabilistic atlas of the
                      human brain’s cytoarchitecture. Science.
                      2020;369(6506):988-992.
                      doi:10.1126/science.abb4588<br>Schiffer C, Spitzer H, Kiwitz
                      K, et al. Convolutional neural networks for
                      cytoarchitectonic brain mapping at large scale. Neuroimage.
                      2021;240:118327.
                      doi:10.1016/j.neuroimage.2021.118327<br><br><b>Acknowledgments:</b><br>This
                      project received funding from the European Union’s Horizon
                      2020 Research and InnovationProgramme, grant agreement
                      101147319 (EBRAINS 2.0 Project), the Helmholtz Association
                      portfolio theme “Supercomputing and Modeling for the Human
                      Brain”, the Helmholtz Association’sInitiative and
                      Networking Fund through the Helmholtz International BigBrain
                      Analytics and LearningLaboratory (HIBALL) under the
                      Helmholtz International Lab grant agreement InterLabs-0015,
                      fromHELMHOLTZ IMAGING, a platform of the Helmholtz
                      Information $\\&$ Data Science Incubator [XBRAIN, grant
                      number: ZT-I-PF-4-061], and from the Deutsche
                      Forschungsgemeinschaft (DFG,German Research Foundation)
                      under the National Research Data Infrastructure – NFDI
                      46/1 –501864659.},
      month         = {Dec},
      date          = {2025-12-08},
      organization  = {EBRAINS summit 2025, Brüssel
                       (Belgien), 8 Dec 2025 - 11 Dec 2025},
      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) / EBRAINS 2.0 - EBRAINS 2.0: A Research
                      Infrastructure to Advance Neuroscience and Brain Health
                      (101147319) / HIBALL - Helmholtz International BigBrain
                      Analytics and Learning Laboratory (HIBALL) (InterLabs-0015)
                      / JL SMHB - Joint Lab Supercomputing and Modeling for the
                      Human Brain (JL SMHB-2021-2027) / X-BRAIN (ZT-I-PF-4-061) /
                      DFG project G:(GEPRIS)501864659 - NFDI4BIOIMAGE - Nationale
                      Forschungsdateninfrastruktur für Mikroskopie und
                      Bildanalyse (501864659)},
      pid          = {G:(DE-HGF)POF4-5254 / G:(DE-HGF)POF4-5251 /
                      G:(EU-Grant)101147319 / G:(DE-HGF)InterLabs-0015 /
                      G:(DE-Juel1)JL SMHB-2021-2027 / G:(DE-HGF)ZT-I-PF-4-061 /
                      G:(GEPRIS)501864659},
      typ          = {PUB:(DE-HGF)24},
      url          = {https://juser.fz-juelich.de/record/1048760},
}