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@INPROCEEDINGS{Kropp:1043527,
      author       = {Kropp, Jan-Oliver and Schiffer, Christian and Amunts,
                      Katrin and Dickscheid, Timo},
      title        = {{S}tep by {S}tep: {T}owards a gapless 1 micron {B}ig{B}rain
                      with {D}iffusion {M}odels},
      reportid     = {FZJ-2025-02903},
      year         = {2025},
      abstract     = {Advances in microscopic imaging and high-performance
                      computing allow analyzing the complex cellular structure of
                      the human brain in great detail. This progress has greatly
                      aided in brain mapping and cell segmentation, and the
                      development of automated analysis methods. However,
                      histological image data can contain data gaps due to
                      inevitable processing artifacts, which, despite careful
                      precautions, may arise during histological lab work, such as
                      missing sections, tissue tears, or inconsistent staining.To
                      address this issue, we presented a convolutional neural
                      network model that reconstructs corrupted data from
                      surrounding tissue, while preserving precise cellular
                      distributions. Our approach uses a denoising diffusion
                      probabilistic model trained on light-microscopy scans of
                      cell-body stained histological sections. We extended this
                      model with the RePaint method to impute corrupted image
                      data. We evaluate its performance with established deep
                      learning models trained on the same type of histological
                      data.A key challenge of our initial model was its difficulty
                      in accurately reconstructing tissue boundaries and larger
                      anatomical structures such as blood vessels. We address
                      these challenges by an enhanced diffusion-based model that
                      incorporates contextual information from adjacent sections
                      of the brain. This model integrates three tissue patches
                      from neighboring sections using a siamese network
                      architecture with cross-attention mechanisms. Leveraging
                      spatially aligned information across consecutive sections,
                      our approach achieves a more anatomically coherent
                      reconstruction.We demonstrate that our model significantly
                      improves realism and anatomical plausibility of
                      reconstructed cellular distributions, as measured by both
                      cell density prediction and brain area classification tasks.
                      The error in predicted cell density was reduced to below
                      $5\%$ across large inpainting regions, marking a notable
                      improvement over previous approaches. The model reliably
                      preserves tissue borders and reconstructs larger structures
                      like blood vessels, which are crucial for accurate
                      cytoarchitectonic mapping.These findings underscore the
                      potential of generative deep learning models for
                      cytoarchitectonic research, opening new avenues for the
                      automated reconstruction of histological data. Beyond
                      inpainting small regions, our approach paves the way for the
                      reconstruction of entirely missing brain slices, offering a
                      powerful tool for bridging data gaps in high-resolution
                      brain mapping efforts.},
      month         = {Jun},
      date          = {2025-06-03},
      organization  = {Helmholtz AI Conference 2025,
                       Karlsruhe (Germany), 3 Jun 2025 - 5 Jun
                       2025},
      subtyp        = {After Call},
      cin          = {INM-1},
      cid          = {I:(DE-Juel1)INM-1-20090406},
      pnm          = {5254 - Neuroscientific Data Analytics and AI (POF4-525) /
                      Helmholtz AI - Helmholtz Artificial Intelligence
                      Coordination Unit – Local Unit FZJ (E.40401.62) / 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) / JL SMHB - Joint Lab
                      Supercomputing and Modeling for the Human Brain (JL
                      SMHB-2021-2027)},
      pid          = {G:(DE-HGF)POF4-5254 / G:(DE-Juel-1)E.40401.62 /
                      G:(DE-HGF)InterLabs-0015 / G:(EU-Grant)101147319 /
                      G:(DE-Juel1)JL SMHB-2021-2027},
      typ          = {PUB:(DE-HGF)24},
      url          = {https://juser.fz-juelich.de/record/1043527},
}