ArticlePloS one2019
Automated localization and quality control of the aorta in cine CMR can significantly accelerate processing of the UK Biobank population data.
Article in PloS one, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed.
- Impact of Blood Pressure Self-Management on Vascular Remodeling After Hypertensive Pregnancy.Hypertension (Dallas, Tex. : 1979) · 2025Trial
- Clinical Phenotypes in Hypertension: A Data-Driven Approach to Risk Stratification.Hypertension (Dallas, Tex. : 1979) · 2026Article
- Brain-heart-eye axis revealed by multi-organ imaging genetics and proteomics.Nature biomedical engineering · 2026Article
- Cross-organ analysis reveals associations between vascular properties of the retina, the carotid and aortic arteries, and the brain.Communications medicine · 2026Article
- Artificial Intelligence in Biomedicine: A Systematic Review from Nanomedicine to Neurology and Hepatology.Pharmaceutics · 2025Review
- 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
- Review
- Ventricular volume asymmetry as a novel imaging biomarker for disease discrimination and outcome prediction.European heart journal open · 2024Article
- Neuroticism personality traits are linked to adverse cardiovascular phenotypes in the UK Biobank.European heart journal. Cardiovascular Imaging · 2023Article
- Characterizing the hypertensive cardiovascular phenotype in the UK Biobank.European heart journal. Cardiovascular Imaging · 2023Article
- Ischemic Heart Disease and Vascular Risk Factors Are Associated With Accelerated Brain Aging.JACC. Cardiovascular imaging · 2023Article
- Multi-organ imaging demonstrates the heart-brain-liver axis in UK Biobank participants.Nature communications · 2022Article
- Artificial Intelligence in Cardiology-A Narrative Review of Current Status.Journal of clinical medicine · 2022Review
- High heritability of ascending aortic diameter and trans-ancestry prediction of thoracic aortic disease.Nature genetics · 2022Article
- Associations of cognitive performance with cardiovascular magnetic resonance phenotypes in the UK Biobank.European heart journal. Cardiovascular Imaging · 2022Article
- Generalizable Framework for Atrial Volume Estimation for Cardiac CT Images Using Deep Learning With Quality Control Assessment.Frontiers in cardiovascular medicine · 2022Article
- Clinician's guide to trustworthy and responsible artificial intelligence in cardiovascular imaging.Frontiers in cardiovascular medicine · 2022Review
- Applications of artificial intelligence in cardiovascular imaging.Nature reviews. Cardiology · 2021Review
- Adverse cardiovascular magnetic resonance phenotypes are associated with greater likelihood of incident coronavirus disease 2019: findings from the UK Biobank.Aging clinical and experimental research · 2021Article
- Cardiovascular Effects of Unilateral Nephrectomy in Living Kidney Donors at 5 Years.Hypertension (Dallas, Tex. : 1979) · 2021Article
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19 authors.
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Abstract
introductionAortic distensibility can be calculated using semi-automated methods to segment the aortic lumen on cine CMR (Cardiovascular Magnetic Resonance) images. However, these methods require visual quality control and manual localization of the region of interest (ROI) of ascending (AA) and proximal descending (PDA) aorta, which limit the analysis in large-scale population-based studies. Using 5100 scans from UK Biobank, this study sought to develop and validate a fully automated method to 1) detect and locate the ROIs of AA and PDA, and 2) provide a quality control mechanism.
methodsThe automated AA and PDA detection-localization algorithm followed these steps: 1) foreground segmentation; 2) detection of candidate ROIs by Circular Hough Transform (CHT); 3) spatial, histogram and shape feature extraction for candidate ROIs; 4) AA and PDA detection using Random Forest (RF); 5) quality control based on RF detection probability. To provide the ground truth, overall image quality (IQ = 0-3 from poor to good) and aortic locations were visually assessed by 13 observers. The automated algorithm was trained on 1200 scans and Dice Similarity Coefficient (DSC) was used to calculate the agreement between ground truth and automatically detected ROIs.
resultsThe automated algorithm was tested on 3900 scans. Detection accuracy was 99.4% for AA and 99.8% for PDA. Aorta localization showed excellent agreement with the ground truth, with DSC ≥ 0.9 in 94.8% of AA (DSC = 0.97 ± 0.04) and 99.5% of PDA cases (DSC = 0.98 ± 0.03). AA×PDA detection probabilities could discriminate scans with IQ ≥ 1 from those severely corrupted by artefacts (AUC = 90.6%). If scans with detection probability < 0.75 were excluded (350 scans), the algorithm was able to correctly detect and localize AA and PDA in all the remaining 3550 scans (100% accuracy).
conclusionThe proposed method for automated AA and PDA localization was extremely accurate and the automatically derived detection probabilities provided a robust mechanism to detect low quality scans for further human review. Applying the proposed localization and quality control techniques promises at least a ten-fold reduction in human involvement without sacrificing any accuracy.
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