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@ARTICLE{Oberstrass:1042642,
      author       = {Oberstrass, Alexander and Vaca, Esteban and Upschulte, Eric
                      and Niu, Meiqi and Palomero-Gallagher, Nicola and Grässel,
                      David and Schiffer, Christian and Axer, Markus and Amunts,
                      Katrin and Dickscheid, Timo},
      title        = {{F}rom {F}ibers to {C}ells: {F}ourier-{B}ased
                      {R}egistration {E}nables {V}irtual {C}resyl {V}iolet
                      {S}taining {F}rom 3{D} {P}olarized {L}ight {I}maging},
      journal      = {arXiv},
      publisher    = {arXiv},
      reportid     = {FZJ-2025-02623},
      year         = {2025},
      abstract     = {Comprehensive assessment of the various aspects of the
                      brain's microstructure requires the use of complementary
                      imaging techniques. This includes measuring the spatial
                      distribution of cell bodies (cytoarchitecture) and nerve
                      fibers (myeloarchitecture). The gold standard for
                      cytoarchitectonic analysis is light microscopic imaging of
                      cell-body stained tissue sections. To reveal the 3D
                      orientations of nerve fibers, 3D Polarized Light Imaging
                      (3D-PLI) has been introduced as a reliable technique
                      providing a resolution in the micrometer range while
                      allowing processing of series of complete brain sections.
                      3D-PLI acquisition is label-free and allows subsequent
                      staining of sections after measurement. By post-staining for
                      cell bodies, a direct link between fiber- and
                      cytoarchitecture can potentially be established within the
                      same section. However, inevitable distortions introduced
                      during the staining process make a nonlinear and cross-modal
                      registration necessary in order to study the detailed
                      relationships between cells and fibers in the images. In
                      addition, the complexity of processing histological sections
                      for post-staining only allows for a limited number of
                      samples. In this work, we take advantage of deep learning
                      methods for image-to-image translation to generate a virtual
                      staining of 3D-PLI that is spatially aligned at the cellular
                      level. In a supervised setting, we build on a unique dataset
                      of brain sections, to which Cresyl violet staining has been
                      applied after 3D-PLI measurement. To ensure high
                      correspondence between both modalities, we address the
                      misalignment of training data using Fourier-based
                      registration methods. In this way, registration can be
                      efficiently calculated during training for local image
                      patches of target and predicted staining. We demonstrate
                      that the proposed method enables prediction of a Cresyl
                      violet staining from 3D-PLI, matching individual cell
                      instances.},
      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          = {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)
                      / JL SMHB - Joint Lab Supercomputing and Modeling for the
                      Human Brain (JL SMHB-2021-2027) / HBP SGA3 - Human Brain
                      Project Specific Grant Agreement 3 (945539) / EBRAINS 2.0 -
                      EBRAINS 2.0: A Research Infrastructure to Advance
                      Neuroscience and Brain Health (101147319) / DFG project
                      G:(GEPRIS)313856816 - SPP 2041: Computational Connectomics
                      (313856816)},
      pid          = {G:(DE-HGF)POF4-5251 / G:(DE-HGF)POF4-5254 /
                      G:(DE-HGF)InterLabs-0015 / G:(DE-Juel1)JL SMHB-2021-2027 /
                      G:(EU-Grant)945539 / G:(EU-Grant)101147319 /
                      G:(GEPRIS)313856816},
      typ          = {PUB:(DE-HGF)25},
      doi          = {10.48550/arXiv.2505.11394},
      url          = {https://juser.fz-juelich.de/record/1042642},
}