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@INPROCEEDINGS{Oberstra:1033579,
      author       = {Oberstraß, Alexander and DeKraker, Jordan and
                      Palomero-Gallagher, Nicola and Muenzing, Sascha E. A. 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},
      reportid     = {FZJ-2024-06459},
      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). 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. 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. Subsequently, PCA is performed to reduce
                      feature dimensionality for visualization and improve
                      computational stability in further analysis.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         = {Nov},
      date          = {2024-11-19},
      organization  = {INM Retreat 2024, Jülich (Germany),
                       19 Nov 2024 - 20 Nov 2024},
      subtyp        = {After Call},
      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)
                      / 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)POF4-5251 / 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)24},
      url          = {https://juser.fz-juelich.de/record/1033579},
}