ArticleJournal of medical imaging (Bellingham, Wash.)2016
Automatic pericardium segmentation and quantification of epicardial fat from computed tomography angiography.
Article in Journal of medical imaging (Bellingham, Wash.), 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.
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Who cites it
15 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Automated Segmentation of Tissues Using CT and MRI: A Systematic Review.Academic radiology · 2019Pooled it
- Fine-grained epicardial adipose tissue segmentation in cardiac CT images with position priors and edge enhancement.Biomedical engineering letters · 2026Article
- Deep learning-based quantification of epicardial adipose tissue volume from non-contrast computed tomography images: a multi-centre study.European heart journal. Digital health · 2025Article
- Coronary artery calcifications are not associated with epicardial adipose tissue volume and attenuation on computed tomography in 1,945 individuals with various degrees of glucose disorders.International journal of cardiology. Heart & vasculature · 2025Article
- An enhanced deep learning method for the quantification of epicardial adipose tissue.Scientific reports · 2024Article
- Review
- Quantification of Epicardial Adipose Tissue Volume and Attenuation for Cardiac CT Scans Using Deep Learning in a Single Multi-Task Framework.Reviews in cardiovascular medicine · 2022Article
- Machine learning applications in cardiac computed tomography: a composite systematic review.European heart journal open · 2022Review
- Artificial intelligence based automatic quantification of epicardial adipose tissue suitable for large scale population studies.Scientific reports · 2021Article
- Development of artificial intelligence in epicardial and pericoronary adipose tissue imaging: a systematic review.European journal of hybrid imaging · 2021Review
- Artificial intelligence in medical imaging: A radiomic guide to precision phenotyping of cardiovascular disease.Cardiovascular research · 2020Review
- Fully Automated CT Quantification of Epicardial Adipose Tissue by Deep Learning: A Multicenter Study.Radiology. Artificial intelligence · 2019Article
- CoreSlicer: a web toolkit for analytic morphomics.BMC medical imaging · 2019Article
- Deep Learning for Quantification of Epicardial and Thoracic Adipose Tissue From Non-Contrast CT.IEEE transactions on medical imaging · 2018Article
- Machine Learning Approaches in Cardiovascular Imaging.Circulation. Cardiovascular imaging · 2017Review
Corrections and comments
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Authors and funding
8 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Recent findings indicate a strong correlation between the risk of future heart disease and the volume of adipose tissue inside of the pericardium. So far, large-scale studies have been hindered by the fact that manual delineation of the pericardium is extremely time-consuming and that existing methods for automatic delineation lack accuracy. An efficient and fully automatic approach to pericardium segmentation and epicardial fat volume (EFV) estimation is presented, based on a variant of multi-atlas segmentation for spatial initialization and a random forest classifier for accurate pericardium detection. Experimental validation on a set of 30 manually delineated computer tomography angiography volumes shows a significant improvement on state-of-the-art in terms of EFV estimation [mean absolute EFV difference: 3.8 ml (4.7%), Pearson correlation: 0.99] with run times suitable for large-scale studies (52 s). Further, the results compare favorably with interobserver variability measured on 10 volumes.
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Registered trials
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