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@ARTICLE{Bielak:152998,
      author       = {L. Bielak$^*$ and N. Wiedenmann$^*$ and N. Nicolay$^*$ and
                      T. Lottner and J. Fischer and H. Bunea$^*$ and A.-L. Grosu
                      and M. Bock$^*$},
      title        = {{A}utomatic {T}umor {S}egmentation {W}ith a {C}onvolutional
                      {N}eural {N}etwork in {M}ultiparametric {MRI}: {I}nfluence
                      of {D}istortion {C}orrection.},
      journal      = {Tomography},
      volume       = {5},
      number       = {3},
      issn         = {2379-139X},
      address      = {Ann Arbor, Michigan},
      publisher    = {Grapho Publications},
      reportid     = {DKFZ-2020-00095},
      pages        = {292 - 299},
      year         = {2019},
      abstract     = {Precise tumor segmentation is a crucial task in radiation
                      therapy planning. Convolutional neural networks (CNNs) are
                      among the highest scoring automatic approaches for tumor
                      segmentation. We investigate the difference in segmentation
                      performance of geometrically distorted and corrected
                      diffusion-weighted data using data of patients with head and
                      neck tumors; 18 patients with head and neck tumors underwent
                      multiparametric magnetic resonance imaging, including T2w,
                      T1w, T2*, perfusion (ktrans), and apparent diffusion
                      coefficient (ADC) measurements. Owing to strong geometrical
                      distortions in diffusion-weighted echo planar imaging in the
                      head and neck region, ADC data were additionally distortion
                      corrected. To investigate the influence of geometrical
                      correction, first 14 CNNs were trained on data with
                      geometrically corrected ADC and another 14 CNNs were trained
                      using data without the correction on different samples of 13
                      patients for training and 4 patients for validation each.
                      The different sets were each trained from scratch using
                      randomly initialized weights, but the training data
                      distributions were pairwise equal for corrected and
                      uncorrected data. Segmentation performance was evaluated on
                      the remaining 1 test-patient for each of the 14 sets. The
                      CNN segmentation performance scored an average Dice
                      coefficient of 0.40 ± 0.18 for data including
                      distortion-corrected ADC and 0.37 ± 0.21 for uncorrected
                      data. Paired t test revealed that the performance was not
                      significantly different (P = .313). Thus, geometrical
                      distortion on diffusion-weighted imaging data in patients
                      with head and neck tumor does not significantly impair CNN
                      segmentation performance in use.},
      cin          = {L601},
      ddc          = {610},
      cid          = {I:(DE-He78)L601-20160331},
      pnm          = {899 - ohne Topic (POF3-899)},
      pid          = {G:(DE-HGF)POF3-899},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:31572790},
      pmc          = {pmc:PMC6752289},
      doi          = {10.18383/j.tom.2019.00010},
      url          = {https://inrepo02.dkfz.de/record/152998},
}