自动化分割心脏脂肪组织，提升心血管疾病研究效率
Automated Cardiac Adipose Tissue Segmentation in Computed Tomography: A Literature Review
- 结合AI与非AI方法，自动分割心外膜和心包脂肪组织
- 分割精度接近人工标注，减少人为误差与耗时
- 适合心血管影像分析、医学图像算法研究者参考
本文综述了近年来在计算机断层扫描（CT）中针对心外膜脂肪组织（EAT）和心包脂肪组织（PAT）的自动化分割方法进展。这两种脂肪组织被心包分隔，与多种心血管疾病相关，其中心外膜脂肪受关注最多。由于解剖结构复杂，手动量化耗时且存在显著观察者间差异。自动化方法有效解决了这些问题，提供更高效、一致的解决方案。研究涵盖从人工智能到传统非人工智能方法的广泛技术路线，并指出当前挑战：亟需更大规模标注公开数据集，以及针对增强CT优化的衰减阈值设定。研究表明，自动化方法的分割效果可媲美人工标注，展现出作为临床工具用于发现新生物标志物和改善患者预后的潜力。
原文摘要 · Abstract (English)
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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