Automated Cardiac Adipose Tissue Segmentation in Computed Tomography: A Literature Review

Andreas W. Aspe; Jonas Jalili Pedersen; Andreas Ohrt Johansen; Klaus Fuglsang Kofoed; Kristine Aavild Sørensen; Rasmus Reinhold Paulsen; Josefine Vilsbøll Sundgaard

cs.CVJul 18, 2026

Automated Cardiac Adipose Tissue Segmentation in Computed Tomography: A Literature Review

Authors: Andreas W. Aspe, Jonas Jalili Pedersen, Andreas Ohrt Johansen, Klaus Fuglsang Kofoed, Kristine Aavild Sørensen, Rasmus Reinhold Paulsen, Josefine Vilsbøll Sundgaard

Organizations: DTU Compute, Technical University of Denmark, Kongens Lyngby, Denmark · Heart Centre, Rigshospitalet, Copenhagen, Denmark, Denmark · Novo Nordisk A/S, Søborg, Denmark

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.

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May 19, 2026 · cs.CVJ/K move · Enter open · S save

Cardiac fat segmentation using computed tomography and an image-to-image conditional generative adversarial neural network

Guilherme Santos da Silva, Dalcimar Casanova, Jefferson Tales Oliva, Erick Oliveira Rodrigues

Academic Department of Informatics, Universidade Tecnoldgica Federal do Parand (UTFPR), Pato Branco, 85503-390, Brasil · Graduate Program of Production and Systems Engineering, Universidade Tecnológica Federal do Parand (UTFPR), Pato Branco, 85503-390, Brazil

In recent years, research has highlighted the association between increased adipose tissue surrounding the human heart and elevated susceptibility to cardiovascular diseases such as atrial fibrillation and coronary heart disease. However, the manual segmentation of these fat deposits has not been widely implemented in clinical practice due to the substantial workload it entails for medical professionals and the associated costs. Consequently, the demand for more precise and time-efficient quantitative analysis has driven the emergence of novel computational methods for fat segmentation. This study presents a novel deep learning-based methodology that offers autonomous segmentation and quantification of two distinct types of cardiac fat deposits. The proposed approach leverages the pix2pix network, a generative conditional adversarial network primarily designed for image-to-image translation tasks. By applying this network architecture, we aim to investigate its efficacy in tackling the specific challenge of cardiac fat segmentation, despite not being originally tailored for this purpose. The two types of fat deposits of interest in this study are referred to as epicardial and mediastinal fats, which are spatially separated by the pericardium. The experimental results demonstrated an average accuracy of 99.08% and f1-score 98.73 for the segmentation of the epicardial fat and 97.90% of accuracy and f1-score of 98.40 for the mediastinal fat. These findings represent the high precision and overlap agreement achieved by the proposed methodology. In comparison to existing studies, our approach exhibited superior performance in terms of f1-score and run time, enabling the images to be segmented in real time.

Whole-Heart Segmentation Generative Adversarial Network Long-Tailed Distribution

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Abstract

Explore similar work

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