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@ARTICLE{Kropp:1024072,
      author       = {Kropp, Jan-Oliver and Schiffer, Christian and Amunts,
                      Katrin and Dickscheid, Timo},
      title        = {{D}enoising {D}iffusion {P}robabilistic {M}odels for
                      {I}mage {I}npainting of {C}ell {D}istributions in the
                      {H}uman {B}rain},
      publisher    = {arXiv},
      reportid     = {FZJ-2024-01952},
      year         = {2023},
      abstract     = {Recent advances in imaging and high-performance computing
                      have made it possible to image the entire human brain at the
                      cellular level. This is the basis to study the multi-scale
                      architecture of the brain regarding its subdivision into
                      brain areas and nuclei, cortical layers, columns, and cell
                      clusters down to single cell morphology Methods for brain
                      mapping and cell segmentation exploit such images to enable
                      rapid and automated analysis of cytoarchitecture and cell
                      distribution in complete series of histological sections.
                      However, the presence of inevitable processing artifacts in
                      the image data caused by missing sections, tears in the
                      tissue, or staining variations remains the primary reason
                      for gaps in the resulting image data. To this end we aim to
                      provide a model that can fill in missing information in a
                      reliable way, following the true cell distribution at
                      different scales. Inspired by the recent success in image
                      generation, we propose a denoising diffusion probabilistic
                      model (DDPM), trained on light-microscopic scans of
                      cell-body stained sections. We extend this model with the
                      RePaint method to impute missing or replace corrupted image
                      data. We show that our trained DDPM is able to generate
                      highly realistic image information for this purpose,
                      generating plausible cell statistics and cytoarchitectonic
                      patterns. We validate its outputs using two established
                      downstream task models trained on the same data.},
      keywords     = {Image and Video Processing (eess.IV) (Other) / Computer
                      Vision and Pattern Recognition (cs.CV) (Other) / FOS:
                      Electrical engineering, electronic engineering, information
                      engineering (Other) / FOS: Computer and information sciences
                      (Other)},
      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) / Helmholtz AI
                      - Helmholtz Artificial Intelligence Coordination Unit –
                      Local Unit FZJ (E.40401.62) / DFG project 313856816 - SPP
                      2041: Computational Connectomics (313856816)},
      pid          = {G:(DE-HGF)POF4-5254 / G:(DE-HGF)InterLabs-0015 /
                      G:(DE-Juel-1)E.40401.62 / G:(GEPRIS)313856816},
      typ          = {PUB:(DE-HGF)25},
      doi          = {10.48550/arXiv.2311.16821},
      url          = {https://juser.fz-juelich.de/record/1024072},
}