ArticleScientific reports2022
Automated imaging-based abdominal organ segmentation and quality control in 20,000 participants of the UK Biobank and German National Cohort Studies.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed, 16 citations in OpenAlex.
- Nationwide organ volume distributions and cross-sectional age-associated differences in abdominal CT from Japan.Japanese journal of radiology · 2026Article
- VIBESegmentator: full body MRI segmentation for the NAKO and UK Biobank.European radiology · 2026Article
- Evaluation of Projection Images for Visual Quality Control of Automated Left and Right Lung Segmentations on T1-Weighted MRI in Large-Scale Clinical Cohort Studies.Tomography (Ann Arbor, Mich.) · 2025Article
- Paraspinal myosteatosis is associated with COPD: a cross-sectional MRI analysis from the population-based KORA cohort.Respiratory research · 2025Article
- Standardized pancreatic MRI-T1 measurement methods: comparison between manual measurement and a semi-automated pipeline with automatic quality control.The British journal of radiology · 2025Article
- Tumour-informed liquid biopsies to monitor advanced melanoma patients under immune checkpoint inhibition.Nature communications · 2024Article
- Deep learning-derived splenic radiomics, genomics, and coronary artery disease.medRxiv : the preprint server for health sciences · 2024Article
- MRI of kidney size matters.Magma (New York, N.Y.) · 2024Review
- A Primer for Utilizing Deep Learning and Abdominal MRI Imaging Features to Monitor Autosomal Dominant Polycystic Kidney Disease Progression.Biomedicines · 2024Review
- Automated image quality assessment for selecting among multiple magnetic resonance image acquisitions in the German National Cohort study.Scientific reports · 2023Article
- Renal MRI: From Nephron to NMR Signal.Journal of magnetic resonance imaging : JMRI · 2023Review
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Authors and funding
25 authors at 17 institutions in 2 countries.
Funding
Abstract
Large epidemiological studies such as the UK Biobank (UKBB) or German National Cohort (NAKO) provide unprecedented health-related data of the general population aiming to better understand determinants of health and disease. As part of these studies, Magnetic Resonance Imaging (MRI) is performed in a subset of participants allowing for phenotypical and functional characterization of different organ systems. Due to the large amount of imaging data, automated image analysis is required, which can be performed using deep learning methods, e. g. for automated organ segmentation. In this paper we describe a computational pipeline for automated segmentation of abdominal organs on MRI data from 20,000 participants of UKBB and NAKO and provide results of the quality control process. We found that approx. 90% of data sets showed no relevant segmentation errors while relevant errors occurred in a varying proportion of data sets depending on the organ of interest. Image-derived features based on automated organ segmentations showed relevant deviations of varying degree in the presence of segmentation errors. These results show that large-scale, deep learning-based abdominal organ segmentation on MRI data is feasible with overall high accuracy, but visual quality control remains an important step ensuring the validity of down-stream analyses in large epidemiological imaging studies.
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