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

arXiv: https://arxiv.org/abs/2607.16992 · HTML: https://mlsanity.com/paper/2607.16992/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 53 references that are in the map.
- First posted on arXiv 18 Jul 2026.

## Builds on (most cited first, 5 of 5)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [A novel approach for the automated segmentation and volume quantification of cardiac fats on computed tomography](https://mlsanity.com/paper/2112.11381/) (2021)
- [Towards the automated segmentation of epicardial and mediastinal fats: A multi-manufacturer approach using intersubject registration and random forest](https://mlsanity.com/paper/2605.29217/) (2026)
- [Cardiac fat segmentation using computed tomography and an image-to-image conditional generative adversarial neural network](https://mlsanity.com/paper/2605.20064/) (2026)
- [Epicardial Adipose Tissue Segmentation from CT Images with A Semi-3D Neural Network](https://mlsanity.com/paper/2204.12904/) (2022)

## Built on by (most cited first, 1 of 1)

- [Anatomy-Aware 3D Mesh Refinement of Pericardium Segmentations on Computed Tomography](https://mlsanity.com/paper/2607.19210/) (2026)

## Abstract (arXiv)

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.