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@PHDTHESIS{Spitzer:877613,
      author       = {Spitzer, Hannah},
      title        = {{A}utomatic {A}nalysis of {C}ortical {A}reas in {W}hole
                      {B}rain {H}istological {S}ections using {C}onvolutional
                      {N}eural {N}etworks},
      volume       = {218},
      school       = {Universität Düsseldorf},
      type         = {Dr.},
      address      = {Jülich},
      publisher    = {Forschungszentrum Jülich GmbH Zentralbibliothek, Verlag},
      reportid     = {FZJ-2020-02328},
      isbn         = {978-3-95806-469-0},
      series       = {Schriften des Forschungszentrums Jülich. Reihe
                      Schlüsseltechnologien / Key Technologies},
      pages        = {XII, 162 S.},
      year         = {2020},
      note         = {Universität Düsseldorf, Diss., 2020},
      abstract     = {The segregation of the human brain in cytoarchitectonic
                      areas is an important prerequisite for the allocation of
                      functional imaging, physiological, connectivity, molecular
                      and genetic data to structurally well-defined entities of
                      the human brain. Cytoarchitecture describes the spatial
                      distribution of cell bodies and their shape and size, and is
                      most appropriately studied at microscopic resolution based
                      on cell-body stained histological sections. To determine
                      boundaries between cytoarchitectonic areas, an
                      observer-independent method that uses image analysis and
                      multivariate statistical tools to capture changes in the
                      distribution of cell bodies is already established.
                      Nowadays, new technologies for high-throughput microscopy
                      allow rapid digitization of histological sections, which
                      increases the need for a fully automatic brain area
                      segmentation method. This task is extremely challenging due
                      to the high interindividual variability in cortical folding,
                      sectioning artifacts, limited labeled training data, and the
                      need for large input sizes for automatic methods. This work
                      shows that convolutional neural networks, a special class of
                      deep artificial neural networks, are suitable for automatic
                      brain area segmentation. It introduces a semantic
                      segmentation model that combines texture input given by
                      high-resolution extracts of the histological sections with
                      prior knowledge given by an existing probabilistic brain
                      area atlas, the JuBrain atlas. This atlas prior helps the
                      model to localize the texture input in the brain and allows
                      it to make topologically correct brain area predictions. To
                      overcome the limited amount of brain area annotations, the
                      model can be pre-trained on a modified task for which
                      training data is easier to obtain. Pre-training the model on
                      a self-supervised task based on predicting the spatial
                      distance between image patches extracted from sections of
                      the same brain significantly increases the segmentation
                      performance and enables the prediction of several brain
                      areas in previously unseen brains. The self-supervised model
                      learns a compact internal feature representation of the
                      input using the inherent structure of the brain, without
                      having explicit access to the concept of brain areas.
                      Extensive evaluations indicate that these features encode
                      cytoarchitectonic properties. This is remarkable result
                      which allows the data-driven analysis of the structure of
                      the entire brain. Although the presented model is not yet
                      robust enough for automatic annotation of all areas in
                      complete human brains, it is already leveraged for practical
                      use by training specialized multi-scale models to propagate
                      brain area labels from annotated sections to spatially close
                      sections. This workflow has the potential to speed up
                      current brain mapping projects by reducing the workload of
                      the neuroscientists and produces previously unattainable
                      high-resolution 3D views of single brain areas.},
      cin          = {INM-1},
      cid          = {I:(DE-Juel1)INM-1-20090406},
      pnm          = {574 - Theory, modelling and simulation (POF3-574) / HBP
                      SGA1 - Human Brain Project Specific Grant Agreement 1
                      (720270) / HBP SGA2 - Human Brain Project Specific Grant
                      Agreement 2 (785907)},
      pid          = {G:(DE-HGF)POF3-574 / G:(EU-Grant)720270 /
                      G:(EU-Grant)785907},
      typ          = {PUB:(DE-HGF)3 / PUB:(DE-HGF)11},
      urn          = {urn:nbn:de:0001-2020072216},
      url          = {https://juser.fz-juelich.de/record/877613},
}