ArticlePeerJ. Computer science2021
A 3D deep learning approach to epicardial fat segmentation in non-contrast and post-contrast cardiac CT images.
Article in PeerJ. Computer science, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed, 15 citations in OpenAlex.
- 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
- Association of deep learning-derived epicardial fat volume with target organ damage in subjects with nonobstructive coronary artery disease.Journal of cardiovascular imaging · 2025Article
- Automated detection of epicardial adipose tissue in cardiac CT using ensemble machine learning for improved diagnosis.MethodsX · 2025Article
- Deep learning-based workflow for automatic extraction of atria and epicardial adipose tissue on cardiac computed tomography in atrial fibrillation.Journal of the Chinese Medical Association : JCMA · 2024Article
- Development of Multiscale 3D Residual U-Net to Segment Edematous Adipose Tissue by Leveraging Annotations from Non-Edematous Adipose Tissue.Proceedings of SPIE--the International Society for Optical Engineering · 2023Article
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3 authors at 3 institutions in 2 countries.
Funding
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Abstract
Epicardial fat (ECF) is localized fat surrounding the heart muscle or myocardium and enclosed by the thin-layer pericardium membrane. Segmenting the ECF is one of the most difficult medical image segmentation tasks. Since the epicardial fat is infiltrated into the groove between cardiac chambers and is contiguous with cardiac muscle, segmentation requires location and voxel intensity. Recently, deep learning methods have been effectively used to solve medical image segmentation problems in several domains with state-of-the-art performance. This paper presents a novel approach to 3D segmentation of ECF by integrating attention gates and deep supervision into the 3D U-Net deep learning architecture. The proposed method shows significant improvement of the segmentation performance, when compared with standard 3D U-Net. The experiments show excellent performance on non-contrast CT datasets with average Dice scores of 90.06%. Transfer learning from a pre-trained model of a non-contrast CT to contrast-enhanced CT dataset was also performed. The segmentation accuracy on the contrast-enhanced CT dataset achieved a Dice score of 88.16%.
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