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@INPROCEEDINGS{Mohlberg:1048812,
      author       = {Mohlberg, Hartmut and Lepage, Claude Y. and Toussaint,
                      Paule-J. and Wenzel, Susanne and Lewis, Lindsay B. and
                      Wagstyl, Konrad and Evans, Alan C. and Amunts, Katrin},
      title        = {3{D} reconstruction of {B}ig{B}rain2: {E}xpertise and new
                      tools for the next generation of {B}ig{B}rains},
      reportid     = {FZJ-2025-04920},
      year         = {2025},
      abstract     = {BigBrain2 is a second BigBrain dataset that complements and
                      builds on our expertise from the first BigBrain [1],
                      providing new insights into variations between brains at
                      whole-brain and cytoarchitectonic level. The brain
                      (30-year-old male donor) was formalin-fixed,
                      paraffin-embedded, and sectioned coronally (20 μm) into
                      7676 sections. Each section was stained for cell bodies
                      (Merker stain). The sections were scanned at 10 μm in-plane
                      (flatbed scanner) and 1 μm in-plane.<br><br>We have
                      developed a new approach to assist the labour-intensive
                      process of correcting the artefacts in the histological
                      images, and, subsequently, to reconstruct the
                      high-resolution 3D volume, with correction for staining
                      imbalances. Despite a significantly improved wet-lab
                      processing pipeline, sectioning and histological preparation
                      of a whole brain at this thickness remains a challenging
                      task, leading to a heterogeneity in the extent and severity
                      of artefacts, rendering a fully automated repair process of
                      all sections impracticable.<br><br>Initially, the 10 μm
                      sections were resampled at 20 μm in-plane, to match the
                      section thickness, and every fifth section (5-series) was
                      repaired manually, ensuring data provenance tracking [2]. An
                      initial 3D reconstruction at 100 μm has been created [3].
                      Remaining sections were processed sequentially; larger
                      artefacts were identified and manually corrected [4]. For
                      the remaining artefacts, each section was registered to the
                      two nearest repaired sections of the 5-series, from which a
                      virtual reference image was interpolated at the position of
                      the target section. Smaller artefacts (e.g., missing data)
                      were corrected by interpolating good tissue from the
                      reference section in place of the missing tissue in manually
                      identified areas.<br><br>To support the 3D reconstruction,
                      tissue masks were created in an automated fashion using the
                      nnU-Net algorithm [5], with a combination of global and
                      local training sets. The approach was extended to obtain a
                      tissue classification for white matter, grey matter, and
                      layer-1 on the repaired histological sections, every 100 μm
                      apart, using a training set of 77 sections 2 mm apart.
                      Unlike global 3D tissue classification, nnU-Net provided a
                      fast and robust 2D segmentation insensitive to staining
                      imbalances and could suitably distinguish layer-1 from white
                      matter, despite both tissue classes showing overlapping
                      cell-body stain intensity distributions.<br><br>The masked
                      repaired sections were aligned to the post-mortem MRI of the
                      fixed brain in an iterative process by 3D registration of
                      the stacked images to the MRI, followed by 2D registration
                      of the individual images to the sliced MRI, while gradually
                      increasing the complexity of 2D and 3D registration from
                      rigid-body to affine to non-linear. These global iterations
                      helped resolve the lower-frequency alignment errors causing
                      ‘jaggies', as were present in BigBrain1, and accounted for
                      tissue compression and shrinkage during histological
                      processing. Finally, section-to-section non-linear 2D
                      alignment (without MRI) was performed to resolve
                      high-frequency alignment errors. Optical-balancing was
                      applied to correct for staining imbalances across the brain
                      volume.<br><br>The new pipeline resulted in a first
                      high-quality 3D reconstruction of the histological images,
                      currently available at 100 μm. The dataset was further
                      enriched by cortical surfaces and annotations. As part of
                      the Julich Brain Atlas [6] 126 cortical and subcortical
                      structures have been annotated in the histological sections
                      with a resolution of 20 μm. The hippocampus [7] has been
                      mapped in the 3D reconstructed data
                      set.<br><br><b>References:</b><ol><li>Amunts K. et al.
                      (2013) BigBrain: An Ultrahigh-Resolution 3D Human Brain
                      Model. Science.</li><li>Lepage C. et al. (2023) 3D
                      reconstruction of BigBrain2: Progress report on updated
                      processing pipeline and application to existing annotations
                      and cortical surfaces. BigBrain Workshop 2023. EBRAINS data
                      release reference (submitted 2025).</li><li>Mohlberg H. et
                      al. (2024) 3D reconstruction of BigBrain2: Progress report
                      on semi-automated repairs of histological sections, BigBrain
                      Workshop 2024.</li><li>Isensee F. et al. (2021) nnU-Net: a
                      self-configuring method for deep learning-based biomedical
                      image segmentation. Nat Methods.</li><li>Amunts, K. et al.
                      (2020). Julich-Brain: A 3D Probabilistic Atlas of the Human
                      Brain’s Cytoarchitecture. Science</li><li>DeKraker, J.
                      (2023) HippoMaps: structural and functional mapping of the
                      hippocampus follows a 2D organization. BigBrain Workshop
                      2023</li></ol>},
      month         = {Oct},
      date          = {2025-10-27},
      organization  = {9th BigBrain Workshop - HIBALL Closing
                       Symposium, Berlin (Belgium), 27 Oct
                       2025 - 29 Oct 2025},
      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)
                      / Helmholtz AI - Helmholtz Artificial Intelligence
                      Coordination Unit – Local Unit FZJ (E.40401.62) / EBRAINS
                      2.0 - EBRAINS 2.0: A Research Infrastructure to Advance
                      Neuroscience and Brain Health (101147319)},
      pid          = {G:(DE-HGF)POF4-5251 / G:(DE-HGF)POF4-5254 /
                      G:(DE-HGF)InterLabs-0015 / G:(DE-Juel-1)E.40401.62 /
                      G:(EU-Grant)101147319},
      typ          = {PUB:(DE-HGF)24},
      url          = {https://juser.fz-juelich.de/record/1048812},
}