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@INPROCEEDINGS{SalinasMedina:1018407,
      author       = {Salinas-Medina, Alejandro and DeKraker, Jordan and
                      Oberstraß, Alexander and Bernhardt, Boris and Toussaint,
                      Paule-J and Liu, Xue S. and Evans, Alan C.},
      title        = {{S}caling for {B}ig {D}ata: {A}n {E}nhanced {S}urface
                      {R}econstruction of the {H}ippocampus {L}everaging
                      {D}elaunay {T}riangulation for {H}igh-{R}esolution {M}apping
                      from {U}nfolded to {N}ative {S}paces},
      reportid     = {FZJ-2023-04788},
      year         = {2023},
      abstract     = {The ability to process and analyze large and complex
                      neuroimaging datasets is crucial to develop computer
                      simulations of the brain. A focus of significant interest is
                      to model the hippocampus, a brain structure integral to
                      memory formation and emotional regulation. This study
                      introduces a state-of-the-art algorithmic pipeline for big
                      data scaling and enhanced surface reconstruction of the
                      hippocampus, utilizing Delaunay Triangulation for
                      high-resolution surface generation.Initially, the pipeline
                      addresses the challenge of upscaling low-resolution surface
                      models by leveraging Delaunay Triangulation. This
                      algorithmic approach not only maintains but enhances
                      anatomical detail, allowing for a high-resolution
                      representation of the hippocampus' complex topology. This
                      process is particularly advantageous for large datasets,
                      making it scalable and big data-compatible.Following the
                      surface enhancement, the pipeline employs a specialized warp
                      field algorithm to transform the high-resolution surface
                      from an unfolded space to a native space. This ensures
                      compatibility with existing volumetric datasets and enhances
                      the granularity and precision of subsequent analyses. An
                      extensive set of validation checks ensures the warp field's
                      fidelity, safeguarding the integrity of the transformed
                      model.Finally, the warped high-resolution surface is
                      integrated with BigBrain hippocampal volumetric data through
                      a robust volume-to-surface mapping algorithm. This step
                      harmonizes the dual approaches of surface-based and
                      volume-based analyses, allowing for comprehensive, nuanced
                      exploration of hippocampal structure and function.The
                      pipeline is implemented in widely-accepted neuroimaging
                      formats like NIFTI and GIFTI, ensuring seamless integration
                      with existing analytical tools and datasets. Preliminary
                      results indicate a significant advancement in the accuracy
                      and depth of hippocampal analyses. This scalable approach is
                      versatile and holds promise for a myriad of applications,
                      from basic neuroscience research to advanced investigations
                      into neurodegenerative diseases and cognitive disorders.
                      Overall, the pipeline sets a new precedent for
                      high-resolution, big data-compatible computational analysis
                      of complex brain structures.},
      month         = {Oct},
      date          = {2023-10-04},
      organization  = {7th BigBrain Workshop, Reykjavík
                       (Iceland), 4 Oct 2023 - 6 Oct 2023},
      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)},
      pid          = {G:(DE-HGF)POF4-5254 / G:(DE-HGF)InterLabs-0015},
      typ          = {PUB:(DE-HGF)6},
      url          = {https://juser.fz-juelich.de/record/1018407},
}