发表机构
DTU Compute, Technical University of Denmark; Heart Centre, Rigshospitalet; Novo Nordisk A/S（丹麦技术大学DTU计算中心; 哥本哈根大学医院心脏中心; 诺和诺德公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
综述计算机断层扫描中自动分割心脏脂肪组织（EAT和PAT）的方法进展，涵盖多种方法，指出存在需更大带注释公共数据集等挑战，证明自动方法分割结果可媲美人工标注，有成为临床工具的潜力。
AI 中文摘要
本综述概述了计算机断层扫描（CT）上两种心脏脂肪：心外膜脂肪组织（EAT）和心包脂肪组织（PAT）自动分割方法的最新进展。这些被心包分隔的脂肪沉积与各种心血管疾病有关，EAT受到最多研究关注。其复杂解剖环境使手动量化耗时且观察者间差异大。自动方法有效解决了这些问题。本研究涵盖多种方法，包括人工智能及非人工智能方法。还提出了剩余挑战，如需要更大的带注释公共数据集和优化的增强CT衰减阈值。结果表明自动方法能实现与人工标注质量相当的分割结果，证明其作为发现新生物标志物和改善患者预后临床工具的潜力。
英文摘要
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.
CommentsThis 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 https://doi.org/10.1007/978-3-031-95918-9_17
Journal refLecture Notes in Computer Science, vol 15726, pp. 240-253, Springer, Cham, 2025
DOI：10.1007/978-3-031-95918-9_17
