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@MISC{Oberstra:1034468,
      author       = {Oberstraß, Alexander and Muenzing, Sascha E. A. and Niu,
                      Meiqi and Palomero-Gallagher, Nicola and Schiffer, Christian
                      and Jorgensen, Matthew J. and Woods, Roger and Axer, Markus
                      and Amunts, Katrin and Dickscheid, Timo},
      title        = {{D}eep texture features characterizing fiber architecture
                      in the vervet monkey occipital lobe (v1)},
      publisher    = {EBRAINS},
      reportid     = {FZJ-2024-07234},
      year         = {2024},
      abstract     = {This dataset contains features for characterizing fiber
                      architecture in three-dimensional polarized light imaging
                      (3D-PLI) for the right occipital lobe from a single vervet
                      monkey brain, comprising 234 brain sections. The data
                      include volumetric PCA projections of deep texture features,
                      clusterings, as well as measures of cortical morphology,
                      such as curvature, cortical depth, white matter depth, and
                      section obliqueness. PCA projections form 20-dimensional
                      vectors, each representing a square image patch of 169 μm,
                      sampled at a particular section and location in the brain.
                      Morphological features were obtained through a cortex
                      segmentation of the volume to explore correlations between
                      extracted features and morphology. Deep texture features
                      were extracted using a self-supervised 3D-Context
                      Contrastive Learning (CL-3D) model, trained on
                      high-resolution microscopic 3D-PLI images. These features
                      reveal patterns of fiber architecture, including
                      distinctions between gray and white matter,
                      myeloarchitectonic layer structures, fiber bundles, fiber
                      crossings, and fiber fannings. Of the 234 sections, 117 were
                      used to train the feature extraction model, and the
                      remaining 117 are provided for analysis. All volumes are
                      spatially aligned with the Average MRI Vervet Atlas of the
                      UCLA Brain Mapping Center using affine matrices. The
                      alignment allows each texture feature to be assigned to a 3D
                      coordinate in this space, referring to the centroid of the
                      patch, for comparison with other data.},
      keywords     = {Neuroscience (Other)},
      cin          = {INM-1},
      cid          = {I:(DE-Juel1)INM-1-20090406},
      pnm          = {5251 - Multilevel Brain Organization and Variability
                      (POF4-525) / HIBALL - Helmholtz International BigBrain
                      Analytics and Learning Laboratory (HIBALL) (InterLabs-0015)
                      / 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) / JL SMHB - Joint Lab Supercomputing and
                      Modeling for the Human Brain (JL SMHB-2021-2027)},
      pid          = {G:(DE-HGF)POF4-5251 / G:(DE-HGF)InterLabs-0015 /
                      G:(EU-Grant)945539 / G:(EU-Grant)101147319 / G:(DE-Juel1)JL
                      SMHB-2021-2027},
      typ          = {PUB:(DE-HGF)32},
      doi          = {10.25493/78A4-KTU},
      url          = {https://juser.fz-juelich.de/record/1034468},
}