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@ARTICLE{Full:304591,
      author       = {P. Full$^*$ and R. T. Schirrmeister and M. Hein and M. F.
                      Russe and M. Reisert and C. Ammann and K.-H. Greiser$^*$ and
                      T. Niendorf and T. Pischon and J. Schulz-Menger and K.
                      Maier-Hein$^*$ and F. Bamberg and S. Rospleszcz and C. L.
                      Schlett and C. Schuppert},
      title        = {{C}ardiac {M}agnetic {R}esonance {I}maging in the {G}erman
                      {N}ational {C}ohort ({NAKO}): {A}utomated {S}egmentation of
                      {S}hort-{A}xis {C}ine {I}mages and {P}ost-{P}rocessing
                      {Q}uality {C}ontrol.},
      journal      = {Journal of cardiovascular magnetic resonance},
      volume       = {nn},
      issn         = {1097-6647},
      address      = {[Amsterdam]},
      publisher    = {Elsevier},
      reportid     = {DKFZ-2025-01914},
      pages        = {nn},
      year         = {2025},
      note         = {#EA:E230# / epub},
      abstract     = {The prospective, multicenter German National Cohort (NAKO)
                      provides a unique dataset of cardiac magnetic resonance
                      (CMR) cine images. Effective processing of these images
                      requires a robust segmentation and quality control
                      pipeline.A deep learning model for semantic segmentation,
                      based on the nnU-Net architecture, was applied to full-cycle
                      short-axis cine images from 29,908 baseline participants.
                      The primary objective was to determine data on structure and
                      function for both ventricles (LV, RV), including
                      end-diastolic volumes (EDV), end-systolic volumes (ESV), and
                      LV myocardial mass. Quality control measures included a
                      visual assessment of outliers in morphofunctional
                      parameters, inter- and intra-ventricular phase differences,
                      and time-volume curves (TVC). These were adjudicated using a
                      five-point rating scale, ranging from five (excellent) to
                      one (non-diagnostic), with ratings of three or lower subject
                      to exclusion. The predictive value of outlier criteria for
                      inclusion and exclusion was evaluated using receiver
                      operating characteristics analysis.The segmentation model
                      generated complete data for 29,609 participants (incomplete
                      in $1.0\%),$ of which 5,082 cases $(17.0\%)$ underwent
                      visual assessment. Quality assurance yielded a sample of
                      26,899 $(90.8\%)$ participants with excellent or good
                      quality, excluding 1,875 participants due to image quality
                      issues and 835 participants due to segmentation quality
                      issues. TVC was the strongest single discriminator between
                      included and excluded participants (AUC: 0.684). Of the
                      two-category combinations, the pairing of TVC and phases
                      provided the greatest improvement over TVC alone (AUC
                      difference: 0.044; p<0.001). The best performance was
                      observed when all three categories were combined (AUC:
                      0.748). By extending the quality-controlled sample to
                      include mid-level 'acceptable' quality ratings, a total of
                      28,413 $(96.0\%)$ participants could be included.The
                      implemented pipeline facilitated the automated segmentation
                      of an extensive CMR dataset, integrating quality control
                      measures. This methodology ensures that ensuing quantitative
                      analyses are conducted with a diminished risk of bias.},
      keywords     = {Artificial intelligence (Other) / Cardiac MR imaging
                      (Other) / German National Cohort (Other) / Population
                      imaging (Other) / Quality control (Other)},
      cin          = {E230 / C020},
      ddc          = {610},
      cid          = {I:(DE-He78)E230-20160331 / I:(DE-He78)C020-20160331},
      pnm          = {315 - Bildgebung und Radioonkologie (POF4-315)},
      pid          = {G:(DE-HGF)POF4-315},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:40946969},
      doi          = {10.1016/j.jocmr.2025.101958},
      url          = {https://inrepo02.dkfz.de/record/304591},
}