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This literature review evaluates automated segmentation techniques for cardiac adipose tissue in CT scans, highlighting how AI methods can match human annotation quality while reducing variability. The analysis notes that although non-AI approaches are included, the primary focus remains on leveraging machine learning to address complex anatomical challenges and accelerate clinical biomarker discovery. Remaining hurdles identified include a scarcity of large annotated public datasets and the need for optimized thresholds in contrast-enhanced imaging scenarios.
Automated Cardiac Adipose Tissue Segmentation in Computed Tomography: A Literature Review
arXiv:2607.16992v1 Announce Type: cross Abstract: This review provides an overview of recent advancements in automated segmentation methods on Computed Tomography (CT) for two types of cardiac fat: Epicardial adipose Tissue (EAT) and Pericardial Adipose Tissue (PAT). These fat deposits, separated by the pericardium, have been linked to various cardiovascular diseases, with EAT receiving the most research attention. Their complex anatomical context makes manual quantification highly time-consuming and prone to considerable inter-observer variability. Automated methods effectively address these complications, offering a more efficient and consistent solution. This study encompasses a broad range of methods, spanning AI as well as non-AI approaches. Additionally, it presents the remaining challenges, including the need for larger annotated public datasets and optimized attenuation thresholds for contrast-enhanced CT. It is demonstrated that automated methods are able to achieve segmentation results comparable to the quality of human annotation, proving their potential as a clinical tool for discovering new biomarkers and enhancing patient outcomes.
Abstract:This review provides an overview of recent advancements in automated segmentation methods on Computed Tomography (CT) for two types of cardiac fat: Epicardial adipose Tissue (EAT) and Pericardial Adipose Tissue (PAT). These fat deposits, separated by the pericardium, have been linked to various cardiovascular diseases, with EAT receiving the most research attention. Their complex anatomical context makes manual quantification highly time-consuming and prone to considerable inter-observer variability. Automated methods effectively address these complications, offering a more efficient and consistent solution. This study encompasses a broad range of methods, spanning AI as well as non-AI approaches. Additionally, it presents the remaining challenges, including the need for larger annotated public datasets and optimized attenuation thresholds for contrast-enhanced CT. It is demonstrated that automated methods are able to achieve segmentation results comparable to the quality of human annotation, proving their potential as a clinical tool for discovering new biomarkers and enhancing patient outcomes.
This preprint has not undergone peer review (when applicable) or any post-submission improvements or corrections. The Version of Record of this contribution is published in Image Analysis - 23rd Scandinavian Conference, SCIA 2025, Proceedings (Lecture Notes in Computer Science, vol. 15726), and is available online at this https URL
Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
I.4.6; I.2.10; J.3
arXiv:2607.16992 [cs.CV]
(or arXiv:2607.16992v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2607.16992 arXiv-issued DOI via DataCite (pending registration)
Lecture Notes in Computer Science, vol 15726, pp. 240-253, Springer, Cham, 2025
https://doi.org/10.1007/978-3-031-95918-9_17 DOI(s) linking to related resources
From: Andreas W. Aspe [view email] [v1] Sat, 18 Jul 2026 22:52:05 UTC (4,287 KB)
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