%0 Journal Article
%A Full, Peter
%A Schirrmeister, Robin T
%A Hein, Manuel
%A Russe, Maximilian F
%A Reisert, Marco
%A Ammann, Clemens
%A Greiser, Karin-Halina
%A Niendorf, Thoralf
%A Pischon, Tobias
%A Schulz-Menger, Jeanette
%A Maier-Hein, Klaus
%A Bamberg, Fabian
%A Rospleszcz, Susanne
%A Schlett, Christopher L
%A Schuppert, Christopher
%T Cardiac Magnetic Resonance Imaging in the German National Cohort (NAKO): Automated Segmentation of Short-Axis Cine Images and Post-Processing Quality Control.
%J Journal of cardiovascular magnetic resonance
%V nn
%@ 1097-6647
%C [Amsterdam]
%I Elsevier
%M DKFZ-2025-01914
%P nn
%D 2025
%Z #EA:E230# / epub
%X 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
%K Artificial intelligence (Other)
%K Cardiac MR imaging (Other)
%K German National Cohort (Other)
%K Population imaging (Other)
%K Quality control (Other)
%F PUB:(DE-HGF)16
%9 Journal Article
%$ pmid:40946969
%R 10.1016/j.jocmr.2025.101958
%U https://inrepo02.dkfz.de/record/304591