Evidence map›Paper›PMID 40946969›Full record

ArticleJournal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance

Cardiac magnetic resonance imaging in the German National Cohort (NAKO): Automated segmentation of short-axis cine images and post-processing quality control.

Peter M Full, Robin T Schirrmeister, Manuel Hein, Maximilian F Russe, Marco Reisert, Clemens Ammann, Karin Halina Greiser, Thoralf Niendorf, Tobias Pischon, Jeanette Schulz-Menger and 5 more

Abstract readMulticenter Study
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

15 authors.

Peter M FullDivision of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany; Medical Faculty Heidelberg, Heidelberg University, Heidelberg, Germany.
Robin T SchirrmeisterMedical Physics, Department of Diagnostic and Interventional Radiology, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany.
Manuel HeinDepartment of Cardiology and Angiology, University Heart Center Freiburg - Bad Krozingen, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany.
Maximilian F RusseDepartment of Diagnostic and Interventional Radiology, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany.
Marco ReisertDepartment of Diagnostic and Interventional Radiology, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany.
Clemens AmmannCharité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany; Working Group on Cardiovascular Magnetic Resonance, Experimental and Clinical Research Center (ECRC), a joint cooperation between the Charité - Universitätsmedizin Berlin and the Max-Delbrück-Center for Molecular Medicine, Berlin, Germany; German Centre for Cardiovascular Research (DZHK), Partner Site Berlin, Berlin, Germany; Department of Cardiology and Nephrology, HELIOS Hospital Berlin-Buch, Berlin, Germany.
Karin Halina GreiserDivision of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany.
Thoralf NiendorfBerlin Ultrahigh Field Facility (B.U.F.F.), Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin, Germany.
Tobias PischonCharité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany; Max-Delbrück-Center for Molecular Medicine in the Helmholtz Association (MDC), Molecular Epidemiology Research Group, Berlin, Germany; Max-Delbrück-Center for Molecular Medicine in the Helmholtz Association (MDC), Biobank Technology Platform, Berlin, Germany.
Jeanette Schulz-MengerCharité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany; Working Group on Cardiovascular Magnetic Resonance, Experimental and Clinical Research Center (ECRC), a joint cooperation between the Charité - Universitätsmedizin Berlin and the Max-Delbrück-Center for Molecular Medicine, Berlin, Germany; German Centre for Cardiovascular Research (DZHK), Partner Site Berlin, Berlin, Germany; Department of Cardiology and Nephrology, HELIOS Hospital Berlin-Buch, Berlin, Germany.
Klaus H Maier-HeinDivision of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany.
Fabian BambergDepartment of Diagnostic and Interventional Radiology, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany.
Susanne RospleszczDepartment of Diagnostic and Interventional Radiology, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany.
Christopher L SchlettDepartment of Diagnostic and Interventional Radiology, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany.
Christopher SchuppertDepartment of Diagnostic and Interventional Radiology, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany. Electronic address: christopher.schuppert@uniklinik-freiburg.de.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe 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.

methodsA 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, end-systolic volumes, and LV myocardial mass. Statistical and visual quality control was performed, including an expert assessment of outliers in morphofunctional parameters, inter- and intra-ventricular phase differences, and LV 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.

resultsThe segmentation model generated complete data for 29,609 of 29,908 participants (99.0%), of whom 5082 (17.0%) underwent visual assessment. Quality assurance yielded a final sample of 26,899 (89.9%) participants with excellent or good quality, excluding 1875 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 (95.0%) participants could be included.

conclusionThe implemented pipeline enabled automated segmentation of an extensive CMR dataset and integrated thorough quality control measures, providing a comprehensive and reliable data resource for quantitative analyses with diminished risk of bias.

Indexed as

Deep LearningHeart DiseasesImage Interpretation, Computer-AssistedMagnetic Resonance Imaging, CineVentricular Function, LeftAgedAutomationDatabases, FactualFemaleGermanyHumansMaleMiddle AgedPredictive Value of TestsProspective StudiesQuality ControlArtificial intelligenceCardiac MR imagingGerman National CohortPopulation imagingQuality control

Identifiers

PMID40946969
PMCPMC13265423

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.