ArticleEuropean radiology2014
Automated quantification of epicardial adipose tissue using CT angiography: evaluation of a prototype software.
Article in European radiology, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 2 of them syntheses that pooled it.
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Who cites it
12 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Automated Segmentation of Tissues Using CT and MRI: A Systematic Review.Academic radiology · 2019Pooled it
- Prognostic value of epicardial fat volume measurements by computed tomography: a systematic review of the literature.European radiology · 2015 · on this mapPooled it
- Deep-Learning for Epicardial Adipose Tissue Assessment With Computed Tomography: Implications for Cardiovascular Risk Prediction.JACC. Cardiovascular imaging · 2023Article
- Associations of aortic stiffness and intra-aortic flow parameters with epicardial adipose tissue in patients with type-2 diabetes.Frontiers in clinical diabetes and healthcare · 2023Article
- Spectral analysis of ultrasound radiofrequency backscatter for the identification of epicardial adipose tissue.Journal of medical imaging (Bellingham, Wash.) · 2022Article
- Automated Quality-Controlled Cardiovascular Magnetic Resonance Pericardial Fat Quantification Using a Convolutional Neural Network in the UK Biobank.Frontiers in cardiovascular medicine · 2021Article
- Greasing the Skids: Deep Learning for Fully Automated Quantification of Epicardial Fat.Radiology. Artificial intelligence · 2019Article
- Development and evaluation of a method for segmentation of cardiac, subcutaneous, and visceral adipose tissue from Dixon magnetic resonance images.Journal of medical imaging (Bellingham, Wash.) · 2019Article
- Automatic pericardium segmentation and quantification of epicardial fat from computed tomography angiography.Journal of medical imaging (Bellingham, Wash.) · 2016Article
- Quantification of epicardial adipose tissue in coronary calcium score and CT coronary angiography image data sets: comparison of attenuation values, thickness and volumes.The British journal of radiology · 2016Article
- Segmentation and quantification of adipose tissue by magnetic resonance imaging.Magma (New York, N.Y.) · 2016Review
- Increased epicardial fat is independently associated with the presence and chronicity of atrial fibrillation and radiofrequency ablation outcome.European radiology · 2015Article
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesThis study evaluated the performance of a novel automated software tool for epicardial fat volume (EFV) quantification compared to a standard manual technique at coronary CT angiography (cCTA).
methodscCTA data sets of 70 patients (58.6 ± 12.9 years, 33 men) were retrospectively analysed using two different post-processing software applications. Observer 1 performed a manual single-plane pericardial border definition and EFVM segmentation (manual approach). Two observers used a software program with fully automated 3D pericardial border definition and EFVA calculation (automated approach). EFV and time required for measuring EFV (including software processing time and manual optimization time) for each method were recorded. Intraobserver and interobserver reliability was assessed on the prototype software measurements. T test, Spearman's rho, and Bland-Altman plots were used for statistical analysis.
resultsThe final EFVA (with manual border optimization) was strongly correlated with the manual axial segmentation measurement (60.9 ± 33.2 mL vs. 65.8 ± 37.0 mL, rho = 0.970, P < 0.001). A mean of 3.9 ± 1.9 manual border edits were performed to optimize the automated process. The software prototype required significantly less time to perform the measurements (135.6 ± 24.6 s vs. 314.3 ± 76.3 s, P < 0.001) and showed high reliability (ICC > 0.9).
conclusionsAutomated EFVA quantification is an accurate and time-saving method for quantification of EFV compared to established manual axial segmentation methods. KEY POINTS: • Manual epicardial fat volume quantification correlates with risk factors but is time-consuming. • The novel software prototype automates measurement of epicardial fat volume with good accuracy. • This novel approach is less time-consuming and could be incorporated into clinical workflow.
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