% IMPORTANT: The following is UTF-8 encoded.  This means that in the presence
% of non-ASCII characters, it will not work with BibTeX 0.99 or older.
% Instead, you should use an up-to-date BibTeX implementation like “bibtex8” or
% “biber”.

@ARTICLE{Oberstrass:1023471,
      author       = {Oberstrass, Alexander and Muenzing, Sascha E. A. and Niu,
                      Meiqi and Palomero-Gallagher, Nicola and Schiffer, Christian
                      and Axer, Markus and Amunts, Katrin and Dickscheid, Timo},
      title        = {{S}elf-{S}upervised {R}epresentation {L}earning for {N}erve
                      {F}iber {D}istribution {P}atterns in 3{D}-{PLI}},
      publisher    = {arXiv},
      reportid     = {FZJ-2024-01704},
      year         = {2024},
      abstract     = {A comprehensive understanding of the organizational
                      principles in the human brain requires, among other factors,
                      well-quantifiable descriptors of nerve fiber architecture.
                      Three-dimensional polarized light imaging (3D-PLI) is a
                      microscopic imaging technique that enables insights into the
                      fine-grained organization of myelinated nerve fibers with
                      high resolution. Descriptors characterizing the fiber
                      architecture observed in 3D-PLI would enable downstream
                      analysis tasks such as multimodal correlation studies,
                      clustering, and mapping. However, best practices for
                      observer-independent characterization of fiber architecture
                      in 3D-PLI are not yet available. To this end, we propose the
                      application of a fully data-driven approach to characterize
                      nerve fiber architecture in 3D-PLI images using
                      self-supervised representation learning. We introduce a
                      3D-Context Contrastive Learning (CL-3D) objective that
                      utilizes the spatial neighborhood of texture examples across
                      histological brain sections of a 3D reconstructed volume to
                      sample positive pairs for contrastive learning. We combine
                      this sampling strategy with specifically designed image
                      augmentations to gain robustness to typical variations in
                      3D-PLI parameter maps. The approach is demonstrated for the
                      3D reconstructed occipital lobe of a vervet monkey brain. We
                      show that extracted features are highly sensitive to
                      different configurations of nerve fibers, yet robust to
                      variations between consecutive brain sections arising from
                      histological processing. We demonstrate their practical
                      applicability for retrieving clusters of homogeneous fiber
                      architecture and performing data mining for interactively
                      selected templates of specific components of fiber
                      architecture such as U-fibers.},
      keywords     = {Computer Vision and Pattern Recognition (cs.CV) (Other) /
                      FOS: Computer and information sciences (Other)},
      cin          = {INM-1},
      cid          = {I:(DE-Juel1)INM-1-20090406},
      pnm          = {5254 - Neuroscientific Data Analytics and AI (POF4-525) /
                      Helmholtz AI - Helmholtz Artificial Intelligence
                      Coordination Unit – Local Unit FZJ (E.40401.62) / HBP SGA3
                      - Human Brain Project Specific Grant Agreement 3 (945539) /
                      JL SMHB - Joint Lab Supercomputing and Modeling for the
                      Human Brain (JL SMHB-2021-2027) / HIBALL - Helmholtz
                      International BigBrain Analytics and Learning Laboratory
                      (HIBALL) (InterLabs-0015)},
      pid          = {G:(DE-HGF)POF4-5254 / G:(DE-Juel-1)E.40401.62 /
                      G:(EU-Grant)945539 / G:(DE-Juel1)JL SMHB-2021-2027 /
                      G:(DE-HGF)InterLabs-0015},
      typ          = {PUB:(DE-HGF)25},
      doi          = {10.48550/ARXIV.2401.17207},
      url          = {https://juser.fz-juelich.de/record/1023471},
}