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@INPROCEEDINGS{Boztoprak:1043521,
      author       = {Boztoprak, Zeynep and Matuschke, Felix and Oberstraß,
                      Alexander and Kooijmans, Roxana and Amunts, Katrin and Axer,
                      Markus and Dickscheid, Timo and Schiffer, Christian},
      title        = {{S}patial {C}ontrastive {L}earning for {A}nchoring
                      {H}istological {H}uman {B}rain {S}ections {W}ithin a
                      {R}eference 3{D} {M}odel},
      reportid     = {FZJ-2025-02897},
      year         = {2025},
      abstract     = {Analyzing the structural organization of the human brain
                      involves the study of nerve fibers that connect neurons and
                      whole brain regions. In addition, a deeper knowledge of the
                      arrangement of these anatomical connections, also known as
                      connectome, contributes to a better understanding of how the
                      brain processes information. 3D Polarized Light Imaging
                      (3D-PLI) exploits the birefringence of the myelin sheath to
                      visualize short- and long-range single nerve fibers and
                      fiber bundles, providing detailed images of fiber
                      organization at the microscopic level. However, analyzing
                      and interpreting the large amounts of complex data is
                      time-consuming and requires expert knowledge. Since the
                      complexity of nerve fiber organization is difficult to
                      capture in a scalable manner using traditional feature
                      extraction methods, we propose a data-driven approach to
                      learn characteristic features of fiber architecture.We train
                      a deep neural network to map high-resolution image patches
                      extracted from 3D-PLI sections to feature vectors that
                      encode the fiber architectonic properties in the image
                      patch.The model is trained on 500.000 patches (size
                      2048x2048 px at 2.66 um/px) from 26 3D-PLI human brain
                      sections using a contrastive learning approach. The method
                      is based on the assumption that spatially close image
                      patches have more structural similarity than more distant
                      pairs. The idea is to train a neural network that pulls
                      feature representations of similar inputs closer together
                      and pushes those of dissimilar inputs apart in feature
                      space. Similarity between two image patches is computed
                      using the Radial Basis Function (RBF) kernel applied to the
                      Euclidean distance between their corresponding 3D brain
                      coordinates.Our analysis shows that clustering in latent
                      space reveals distinctions between subcortical regions and
                      remaining tissue, and that atlas labeling reveals a grouping
                      of structures that aligns with brain regions and fiber
                      bundles. Evaluating coordinate regression and spatial
                      anchoring tasks demonstrates that the learned features
                      better preserve spatial relationships and achieve lower
                      Mean-Squared-Error (MSE) than classical texture features.
                      These results demonstrate that the learned representations
                      encode nerve fiber properties and structural information,
                      providing an important foundation for developing scalable
                      analysis methods for fiber architecture in the human brain.},
      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) /
                      X-BRAIN (ZT-I-PF-4-061) / HIBALL - Helmholtz International
                      BigBrain Analytics and Learning Laboratory (HIBALL)
                      (InterLabs-0015) / Helmholtz AI - Helmholtz Artificial
                      Intelligence Coordination Unit – Local Unit FZJ
                      (E.40401.62) / JL SMHB - Joint Lab Supercomputing and
                      Modeling for the Human Brain (JL SMHB-2021-2027) / EBRAINS
                      2.0 - EBRAINS 2.0: A Research Infrastructure to Advance
                      Neuroscience and Brain Health (101147319)},
      pid          = {G:(DE-HGF)POF4-5254 / G:(DE-HGF)ZT-I-PF-4-061 /
                      G:(DE-HGF)InterLabs-0015 / G:(DE-Juel-1)E.40401.62 /
                      G:(DE-Juel1)JL SMHB-2021-2027 / G:(EU-Grant)101147319},
      typ          = {PUB:(DE-HGF)24},
      doi          = {10.34734/FZJ-2025-02897},
      url          = {https://juser.fz-juelich.de/record/1043521},
}