生成特定心脏病的超声心动图视频，解决数据稀缺问题。
Echo-Path: Pathology-Conditioned Echo Video Generation
- 通过病理条件控制生成心脏异常的超声视频。
- 合成视频与真实数据分布接近，分类器在真实数据上提升7%~8%准确率。
- 适合医学影像生成与临床辅助诊断研究者使用。
心血管疾病仍是全球主要致死原因，超声心动图对常见及先天性心脏疾病的诊断至关重要。然而，某些病理性超声数据稀缺，制约了自动化诊断模型的发展。本文提出Echo-Path，一种新型生成框架，可生成针对特定心脏病理的超声心动图视频。该方法将病理条件机制引入先进超声视频生成器，使模型学习并控制心腔结构与运动的疾病特异性模式。定量评估显示，合成视频具有低分布距离，视觉保真度高；临床验证表明其呈现合理病理标记。此外，基于合成数据训练的分类器在真实数据上表现良好，用于扩充真实训练集时，显著提升对房间隔缺损（ASD）和肺动脉高压（PAH）的诊断性能，分别提升7%和8%。代码、权重及数据集已公开于https://github.com/Marshall-mk/EchoPathv1。
原文摘要 · Abstract (English)
Cardiovascular diseases (CVDs) remain the leading cause of mortality globally, and echocardiography is critical for diagnosis of both common and congenital cardiac conditions. However, echocardiographic data for certain pathologies are scarce, hindering the development of robust automated diagnosis models. In this work, we propose Echo-Path, a novel generative framework to produce echocardiogram videos conditioned on specific cardiac pathologies. Echo-Path can synthesize realistic ultrasound video sequences that exhibit targeted abnormalities, focusing here on atrial septal defect (ASD) and pulmonary arterial hypertension (PAH). Our approach introduces a pathology-conditioning mechanism into a state-of-the-art echo video generator, allowing the model to learn and control disease-specific structural and motion patterns in the heart. Quantitative evaluation demonstrates that the synthetic videos achieve low distribution distances, indicating high visual fidelity. Clinically, the generated echoes exhibit plausible pathology markers. Furthermore, classifiers trained on our synthetic data generalize well to real data and, when used to augment real training sets, it improves downstream diagnosis of ASD and PAH by 7\% and 8\% respectively. Code, weights and dataset are available here https://github.com/Marshall-mk/EchoPathv1
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