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@ARTICLE{Kohlmann:132617,
      author       = {P. Kohlmann and J. Strehlow and B. Jobst$^*$ and S. Krass
                      and J.-M. Kuhnigk and A. Anjorin and O. Sedlaczek$^*$ and S.
                      Ley and H.-U. Kauczor$^*$ and M. O. Wielpütz$^*$},
      title        = {{A}utomatic lung segmentation method for {MRI}-based lung
                      perfusion studies of patients with chronic obstructive
                      pulmonary disease.},
      journal      = {International journal of computer assisted radiology and
                      surgery},
      volume       = {10},
      number       = {4},
      issn         = {1861-6429},
      address      = {Berlin},
      publisher    = {Springer},
      reportid     = {DKFZ-2018-00277},
      pages        = {403 - 417},
      year         = {2015},
      abstract     = {A novel fully automatic lung segmentation method for
                      magnetic resonance (MR) images of patients with chronic
                      obstructive pulmonary disease (COPD) is presented. The main
                      goal of this work was to ease the tedious and time-consuming
                      task of manual lung segmentation, which is required for
                      region-based volumetric analysis of four-dimensional MR
                      perfusion studies which goes beyond the analysis of small
                      regions of interest.The first step in the automatic
                      algorithm is the segmentation of the lungs in morphological
                      MR images with higher spatial resolution than corresponding
                      perfusion MR images. Subsequently, the segmentation mask of
                      the lungs is transferred to the perfusion images via
                      nonlinear registration. Finally, the masks for left and
                      right lungs are subdivided into a user-defined number of
                      partitions. Fourteen patients with two time points resulting
                      in 28 perfusion data sets were available for the preliminary
                      evaluation of the developed methods.Resulting lung
                      segmentation masks are compared with reference segmentations
                      from experienced chest radiologists, as well as with total
                      lung capacity (TLC) acquired by full-body plethysmography.
                      TLC results were available for thirteen patients. The
                      relevance of the presented method is indicated by an
                      evaluation, which shows high correlation between
                      automatically generated lung masks with corresponding
                      ground-truth estimates.The evaluation of the developed
                      methods indicates good accuracy and shows that automatically
                      generated lung masks differ from expert segmentations about
                      as much as segmentations from different experts.},
      cin          = {E010 / E015},
      ddc          = {610},
      cid          = {I:(DE-He78)E010-20160331 / I:(DE-He78)E015-20160331},
      pnm          = {315 - Imaging and radiooncology (POF3-315)},
      pid          = {G:(DE-HGF)POF3-315},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:24989967},
      doi          = {10.1007/s11548-014-1090-0},
      url          = {https://inrepo02.dkfz.de/record/132617},
}