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@ARTICLE{Isensee:144600,
      author       = {F. Isensee$^*$ and M. Schell and I. Pflueger$^*$ and G.
                      Brugnara and D. Bonekamp$^*$ and U. Neuberger and A. Wick
                      and H.-P. Schlemmer$^*$ and S. Heiland and W. Wick$^*$ and
                      M. Bendszus and K. H. Maier-Hein$^*$ and P. Kickingereder},
      title        = {{A}utomated brain extraction of multisequence {MRI} using
                      artificial neural networks.},
      journal      = {Human brain mapping},
      volume       = {40},
      number       = {17},
      issn         = {1097-0193},
      address      = {New York, NY},
      publisher    = {Wiley-Liss},
      reportid     = {DKFZ-2019-02042},
      pages        = {4952-4964},
      year         = {2019},
      note         = {40(17):4952-4964},
      abstract     = {Brain extraction is a critical preprocessing step in the
                      analysis of neuroimaging studies conducted with magnetic
                      resonance imaging (MRI) and influences the accuracy of
                      downstream analyses. The majority of brain extraction
                      algorithms are, however, optimized for processing healthy
                      brains and thus frequently fail in the presence of
                      pathologically altered brain or when applied to
                      heterogeneous MRI datasets. Here we introduce a new,
                      rigorously validated algorithm (termed HD-BET) relying on
                      artificial neural networks that aim to overcome these
                      limitations. We demonstrate that HD-BET outperforms six
                      popular, publicly available brain extraction algorithms in
                      several large-scale neuroimaging datasets, including one
                      from a prospective multicentric trial in neuro-oncology,
                      yielding state-of-the-art performance with median
                      improvements of +1.16 to +2.50 points for the Dice
                      coefficient and -0.66 to -2.51 mm for the Hausdorff
                      distance. Importantly, the HD-BET algorithm, which shows
                      robust performance in the presence of pathology or
                      treatment-induced tissue alterations, is applicable to a
                      broad range of MRI sequence types and is not influenced by
                      variations in MRI hardware and acquisition parameters
                      encountered in both research and clinical practice. For
                      broader accessibility, the HD-BET prediction algorithm is
                      made freely available (www.neuroAI-HD.org) and may become an
                      essential component for robust, automated, high-throughput
                      processing of MRI neuroimaging data.},
      cin          = {E230 / E010 / B320 / L101},
      ddc          = {610},
      cid          = {I:(DE-He78)E230-20160331 / I:(DE-He78)E010-20160331 /
                      I:(DE-He78)B320-20160331 / I:(DE-He78)L101-20160331},
      pnm          = {315 - Imaging and radiooncology (POF3-315)},
      pid          = {G:(DE-HGF)POF3-315},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:31403237},
      doi          = {10.1002/hbm.24750},
      url          = {https://inrepo02.dkfz.de/record/144600},
}