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@INPROCEEDINGS{Kropp:1031454,
      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},
      school       = {Heinrich-Heine-University Düsseldorf},
      reportid     = {FZJ-2024-05673},
      year         = {2024},
      abstract     = {Advances in microscopic imaging and high-performance
                      computation have made it possible to analyze the complex
                      cellular structure of the human brain in great detail. This
                      progress has greatly aided in brain mapping and cell
                      segmentation, leading to the development methods for
                      automated analysis of tissue architecture and cell
                      distribution in histological brain sections. 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 present a convolutional neural
                      network model that reconstructs missing or corrupted data
                      from surrounding tissue, while preserving precise cellular
                      distributions. Our approach is based on recent advancements
                      in image generation and involves utilizing a denoising
                      diffusion probabilistic model (DDPM) that is trained on
                      light-microscopy scans of cell-body stained histological
                      sections. We extend this model with the RePaint method to
                      impute missing or replace corrupted image data.To validate
                      the model, we propose two new validation metrics based on
                      two established deep learning models that were trained on
                      the same type of data. In validation, we want to confirm a)
                      the correct reproduction of cell statistics like cell size
                      and count, and b) the generation of plausible
                      cytoarchitectonic patterns, including brain area-specific
                      laminar and columnar organization . We compare cell
                      statistics using CPN, a cell segmentation model that
                      provides precise cell statistics. Additionally, a model
                      trained for cytoarchitecture classification is used to
                      validate the structure of the inpainted regions by comparing
                      how the inpainting process affects classification
                      performance.We find that images generated by the proposed
                      DDPM exhibit realistic and anatomically highly plausible
                      cell distributions, effectively filling in data gaps
                      resulting from histological artifacts. The model achieves
                      low errors in cell statistics of less than $10\%$ and high
                      accuracies in cytoarchitecture classification of above
                      $85\%,$ even with inpainted regions as large as $50\%$ of
                      the input patch. Our results demonstrate the potential of
                      the proposed generative model to improve the accuracy and
                      completeness of analysis workflows for histological brain
                      imaging data and to provide the basis for the development of
                      future whole-brain human brain atlases.},
      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) /
                      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)},
      pid          = {G:(DE-HGF)POF4-5254 / G:(DE-HGF)InterLabs-0015 /
                      G:(EU-Grant)101147319 / G:(DE-Juel-1)E.40401.62},
      typ          = {PUB:(DE-HGF)6},
      url          = {https://juser.fz-juelich.de/record/1031454},
}