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@INPROCEEDINGS{Oberstra:1031453,
      author       = {Oberstraß, Alexander and DeKraker, Jordan and Münzing,
                      Sascha and Evans, Alan C. and Axer, Markus and Amunts,
                      Katrin and Dickscheid, Timo},
      title        = {{A}nalyzing {R}egional {O}rganization of the {H}uman
                      {H}ippocampus in 3{D}-{PLI} {U}sing {C}ontrastive {L}earning
                      and {G}eometric {U}nfolding},
      school       = {Heinrich-Heine-University Düsseldorf},
      reportid     = {FZJ-2024-05672},
      year         = {2024},
      abstract     = {Quantifiable and interpretable descriptors of nerve fiber
                      architecture at microscopic resolution are an important
                      basis for a deeper understanding of human brain
                      architecture. 3D polarized light imaging (3D-PLI) provides
                      detailed insights into the course and geometry of nerve
                      fibers in whole postmortem brain sections, represented in
                      large datasets. The large amounts of data, combined with
                      complex textures in 3D-PLI images, however, make analysis
                      challenging and limit access to data annotations. To this
                      end, we propose using self-supervised contrastive learning
                      to extract deep texture features for fiber architecture in
                      3D-PLI. We use the texture features to analyze the regional
                      organization of the human hippocampus in combination with
                      geometric unfolding to reduce the effects of its folded
                      topology and project the features to a canonical reference
                      space.We analyze the fiber architecture of a human
                      hippocampus of an 87-year-old male, measured with a
                      polarizing microscope (PM) at 1.3 µm in-plane resolution on
                      60 µm thick brain sections. The volume comprises 545 brain
                      sections, each 26757 × 22734 pixels in size. We apply
                      contrastive learning to learn robust and descriptive
                      representations by contrasting similar (positive) and
                      dissimilar (negative) pairs of texture examples. Here, we
                      leverage the volume reconstruction of individual brain
                      sections in the learning objective to identify positive
                      pairs based on a fixed distance between example image
                      patches either in-plane (CL-2D) or across brain sections in
                      3D (CL-3D) (Fig. 1A). The objective is used to train a
                      width-reduced ResNet-50 architecture on the full
                      hippocampus, extracting 256 texture features for square
                      patches of 128 pixels size (166 µm). After training,
                      inference is performed using a sliding window approach to
                      generate feature maps for whole brain sections (Fig. 1B). To
                      analyze the folded architecture of the hippocampus, we apply
                      HippUnfold and sample features from the feature maps at
                      multiple depths of the pyramidal layer of the hippocampal
                      Cornu ammonis (CA) region and the subicular complex (Fig.
                      1C). Subsequently, PCA is performed to reduce feature
                      dimensionality for visualization and improve computational
                      stability in further analysis (Fig. 1E).To assess how well
                      the deep texture features reflect the regional organization
                      of the hippocampus, we perform k-means clustering for 6
                      clusters and compare the results with subfield labels.
                      Clusters in CL-3D features show good visual agreement with
                      hippocampal CA1 - CA4 regions and the subicular complex. In
                      terms of mutual information (0.72), they align more clearly
                      compared to clustering of baseline characterizations based
                      on fractional anisotropy and mean transmittance (0.40), as
                      well as CL-2D (0.61).Without any supervisory signal, CL-3D
                      features form a well-structured embedding space, following
                      the general regional organization pattern of the hippocampus
                      and additionally highlight an expected functional
                      rostro-caudal heterogeneity. Projecting deep texture
                      features to unfolded space using HippUnfold enables
                      subsequent comparison with diverse modalities. This work
                      thus lays the foundation for incorporating 3D-PLI texture
                      information into a comprehensive multimodal mapping of the
                      human hippocampus.},
      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/1031453},
}