ArticleIEEE transactions on medical imaging2018
Deep Learning for Quantification of Epicardial and Thoracic Adipose Tissue From Non-Contrast CT.
Article in IEEE transactions on medical imaging, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 72 papers, 2 of them syntheses that pooled it.
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Who cites it
72 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Development and application of artificial intelligence in cardiac imaging.The British journal of radiology · 2020Pooled it
- Automated Segmentation of Tissues Using CT and MRI: A Systematic Review.Academic radiology · 2019Pooled it
- Epicardial adipose tissue, myocardial remodelling and adverse outcomes in asymptomatic aortic stenosis: a post hoc analysis of a randomised controlled trial.Heart (British Cardiac Society) · 2025Trial
- Machine learning to predict the long-term risk of myocardial infarction and cardiac death based on clinical risk, coronary calcium, and epicardial adipose tissue: a prospective study.Cardiovascular research · 2020Trial
- Deep Learning-Based Quantification of Epicardial Adipose Tissue Volume and Attenuation Predicts Major Adverse Cardiovascular Events in Asymptomatic Subjects.Circulation. Cardiovascular imaging · 2020Trial
- Reference-Label Construction and Annotation Reporting in CT- and MRI-Based Adipose Tissue Segmentation: A Systematic Review and Evidence Map.Journal of imaging informatics in medicine · 2026Review
- Automated Deep Learning Segmentation and Quantification of Epicardial Adipose Tissue from Coronary Computed Tomography Angiography: Validation and Clinical Implications.Journal of imaging informatics in medicine · 2026Article
- Role of artificial intelligence in developing predictive models for major adverse cardiovascular outcomes using CCTA adipose tissue characteristics: a systematic review and meta-analysis.European heart journal. Digital health · 2026Review
- Role of systemic and epicardial adipose tissue in cardiometabolic disease.Nature reviews. Cardiology · 2026Review
- Review
- Validation of a deep learning approach for epicardial adipose tissue segmentation in computed tomography.The international journal of cardiovascular imaging · 2026Article
- First Clinical Use of a Novel AI-Based Imaging Tool to Enhance CTO PCI Planning.JACC. Case reports · 2026Article
- Epicardial and paracardial adipose tissue quantification in short-axis cardiac cine MRI using deep learning.Magma (New York, N.Y.) · 2026Article
- Article
- Fine-grained epicardial adipose tissue segmentation in cardiac CT images with position priors and edge enhancement.Biomedical engineering letters · 2026Article
- The Multiscale Heterogeneity of Adipose Tissue in Health and Disease.Research (Washington, D.C.) · 2026Review
- Mapping and ablation outcomes of extra-pulmonary vein triggers in atrial fibrillation: single-centre retrospective study with consistent provocation protocol.Europace : European pacing, arrhythmias, and cardiac electrophysiology : journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology · 2025Observational
- AI-Driven segmentation and morphogeometric profiling of epicardial adipose tissue in type 2 diabetes.Cardiovascular diabetology · 2025Article
- Quantifying the Unknowns of Plaque Morphology: The Role of Topological Uncertainty in Coronary Artery Disease.Mayo Clinic proceedings. Digital health · 2025Article
- Subclinical Atherosclerosis and Cardiovascular Events Among Patients With Colorectal Cancer.Cancer medicine · 2025Article
12 more citing papers are in PubMed but not listed here.
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10 authors.
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Abstract
Epicardial adipose tissue (EAT) is a visceral fat deposit related to coronary artery disease. Fully automated quantification of EAT volume in clinical routine could be a timesaving and reliable tool for cardiovascular risk assessment. We propose a new fully automated deep learning framework for EAT and thoracic adipose tissue (TAT) quantification from non-contrast coronary artery calcium computed tomography (CT) scans. The first multi-task convolutional neural network (ConvNet) is used to determine heart limits and perform segmentation of heart and adipose tissues. The second ConvNet, combined with a statistical shape model, allows for pericardium detection. EAT and TAT segmentations are then obtained from outputs of both ConvNets. We evaluate the performance of the method on CT data sets from 250 asymptomatic individuals. Strong agreement between automatic and expert manual quantification is obtained for both EAT and TAT with median Dice score coefficients of 0.823 (inter-quartile range (IQR): 0.779-0.860) and 0.905 (IQR: 0.862-0.928), respectively; with excellent correlations of 0.924 and 0.945 for EAT and TAT volumes. Computations are performed in <6 s on a standard personal computer for one CT scan. Therefore, the proposed method represents a tool for rapid fully automated quantification of adipose tissue and may improve cardiovascular risk stratification in patients referred for routine CT calcium scans.
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