Epicardial fat is a visceral fat deposit, located between the heart and the pericardium, which shares many of the pathophysiological properties of other visceral fat deposits, it may also potentially cause local inflammation and likely has direct effects on coronary atherosclerosis. epicardial fat is also associated with other known factors, such as obesity, diabetes mellitus, age, and hypertension, which interprets its role as an independent risk marker intricate. For the investigation of the relationship between epicardial fat and various diseases, it is important to segment the epicardial fat in a fast and reproducible way. However, epicardial fat has a variable distribution, and multiple conditions may affect the volume of the EF, which can increase the complexity of the already time-consuming manual segmentation work. In this study, we propose to use a 3D deep attention U-Net method to segment the epicardial fat for cardiac CT image automatically. To test the proposed method, we applied it to 40 patients’ cardiac CT images. Five-fold cross-validation experiments were used to evaluate the proposed method. We calculated the Dice similarity coefficient (DSC), precision, and recall (MSD) indices between the ground truth and our segmentation to quantify the segmentation accuracy of the proposed method. Overall, the DSC, precision, and recall were 85% ± 5%, 86% ± 4%, and 89% ± 5%, which demonstrated the detection and segmentation accuracy of the proposed method.