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It is distributed mainly around the atrioventricular groove, atrial septum, ventricular septum and coronary arteries. Studies have shown that the density, thickness, volume and other characteristics of epicardial adipose tissue (EAT) are independently correlated with a variety of cardiovascular diseases. Given this association, the accurate determination of EAT volume is an essential aim of future research. Therefore, the purpose of this study was to establish a framework for fully automatic EAT segmentation and quantification in coronary computed tomography angiography (CCTA) scans.<\/jats:p><\/jats:sec>Methods<\/jats:title>A set of 103 scans are randomly selected from our medical center. An automatic pipeline has been developed to segment and quantify the volume of EAT. First, a multi\u2010slice deep neural network is used to simultaneously segment the pericardium in multiple adjacent slices. Then a deformable model is employed to reduce false positive and negative regions in the segmented binary pericardial images. Finally, the pericardium mask is used to define the region of interest (ROI) and the threshold method is utilized to extract the pixels ranging from \u2212175 Hounsfield units (HU) to \u221215\u00a0HU for the segmentation of EAT.<\/jats:p><\/jats:sec>Results<\/jats:title>The Dice indices of the pericardial segmentation using the proposed method with respect to the manual delineation results of two radiology experts were 97.1%\u00a0\u00a00.7% and 96.9%\u00a0\u00a00.6%, respectively. The inter\u2010observer variability was also assessed, resulting in a Dice index of 97.0%\u00a0\u00a00.7%. For the EAT segmentation results, the Dice indices between the proposed method and the two radiology experts were 93.4%\u00a0\u00a01.5% and 93.3%\u00a0\u00a01.3%, respectively, and the same measurement between the experts themselves was 93.6%\u00a0\u00a01.9%. The Pearson\u2019s correlation coefficients between the EAT volumes computed from the results of the proposed method and the manual delineation by the two experts were 1.00 and 0.99 and the same coefficients between the experts was 0.99.<\/jats:p><\/jats:sec>Conclusions<\/jats:title>This work describes the development of a fully automatic EAT segmentation and quantification method from CCTA scans and the results compare favorably with the assessments of two independent experts. 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