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@INPROCEEDINGS{Lhrs:824919,
      author       = {Lührs, Anna and Bücker, Oliver and Axer, Markus},
      title        = {{T}owards {L}arge-{S}cale {F}iber {O}rientation {M}odels of
                      the {B}rain – {A}utomation and {P}arallelization of a
                      {S}eeded {R}egion {G}rowing {S}egmentation of
                      {H}igh-{R}esolution {B}rain {S}ection {I}mages},
      volume       = {10087},
      address      = {Cham},
      publisher    = {Springer International Publishing},
      reportid     = {FZJ-2016-07420},
      isbn         = {978-3-319-50861-0 (print)},
      series       = {Lecture Notes in Computer Science},
      pages        = {28 - 42},
      year         = {2016},
      comment      = {Brain-Inspired Computing},
      booktitle     = {Brain-Inspired Computing},
      abstract     = {To understand the microscopical organization of the human
                      brain including cellular and fiber architectures, it is a
                      necessary prerequisite to build virtual models of the brain
                      on a sound biological basis. 3D Polarized Light Imaging
                      (3D-PLI) provides a window to analyze the fiber architecture
                      and the fibers’ intricate inter-connections at microscopic
                      resolutions. Considering the complexity and the pure size of
                      the human brain with its nearly 86 billion nerve cells,
                      3D-PLI is challenging with respect to data handling and
                      analysis in the TeraByte to PetaByte ranges, and inevitably
                      requires supercomputing facilities. Parallelization and
                      automation of image processing steps open up new
                      perspectives to speed up the generation of new high
                      resolution models of the human brain to provide
                      groundbreaking insights into the brain’s three-dimensional
                      micro architecture. Here, we will describe the
                      implementation and the performance of a parallelized
                      semi-automated seeded region growing algorithm used to
                      classify tissue and background components in up to one
                      million 3D-PLI images acquired from an entire human brain.
                      This algorithm represents an important element of a complex
                      UNICORE-based analysis workflow ultimately aiming at the
                      extraction of spatial fiber orientations from 3D-PLI
                      measurements.},
      month         = {Jul},
      date          = {2015-07-06},
      organization  = {International Workshop on
                       Brain-Inspired Computing, Cetraro
                       (Italy), 6 Jul 2015 - 10 Jul 2015},
      cin          = {JSC / INM-1},
      ddc          = {004},
      cid          = {I:(DE-Juel1)JSC-20090406 / I:(DE-Juel1)INM-1-20090406},
      pnm          = {511 - Computational Science and Mathematical Methods
                      (POF3-511) / 574 - Theory, modelling and simulation
                      (POF3-574) / SMHB - Supercomputing and Modelling for the
                      Human Brain (HGF-SMHB-2013-2017) / HBP - The Human Brain
                      Project (604102) / SLNS - SimLab Neuroscience
                      (Helmholtz-SLNS)},
      pid          = {G:(DE-HGF)POF3-511 / G:(DE-HGF)POF3-574 /
                      G:(DE-Juel1)HGF-SMHB-2013-2017 / G:(EU-Grant)604102 /
                      G:(DE-Juel1)Helmholtz-SLNS},
      typ          = {PUB:(DE-HGF)8 / PUB:(DE-HGF)7},
      doi          = {10.1007/978-3-319-50862-7_3},
      url          = {https://juser.fz-juelich.de/record/824919},
}