Abstract
Ultrasound echocardiography is essential for the non-invasive, real-time assessment of cardiac function, but the scarcity of labelled data, driven by privacy restrictions and the complexity of expert annotation, remains a major obstacle for deep learning methods. We propose the Motion Conditioned Diffusion Model (MCDM), a label-free latent diffusion framework that synthesises realistic echocardiography videos conditioned on self-supervised motion features. To extract these features, we design the Motion and Appearance Feature Extractor (MAFE), which disentangles motion and appearance representations from videos. Feature learning is further enhanced by two auxiliary objectives: a re-identification loss guided by pseudo appearance features and an optical flow loss guided by pseudo flow fields. Evaluated on the EchoNet-Dynamic dataset, MCDM achieves competitive video generation performance, producing temporally coherent and clinically realistic sequences without reliance on manual labels. These results demonstrate the potential of self-supervised conditioning for scalable echocardiography synthesis. Our code is available at this https URL.
Abstract (translated)
超声心动图是评估心脏功能的非侵入性和实时方法，但受隐私限制和专家标注复杂性影响的数据标记不足，仍然是深度学习方法面临的主要障碍。我们提出了一种无标签的潜在扩散框架——运动条件扩散模型（MCDM），该模型可以在自监督运动特征条件下合成逼真的超声心动图视频。为了提取这些特征，我们设计了运动与外观特征提取器（MAFE），它可以将运动和外观表示从视频中分离出来。通过两个辅助目标进一步增强了特性学习：一个由伪外观特征引导的重新识别损失，以及一个由伪流场引导的光流损失。 在EchoNet-Dynamic数据集上进行评估时，MCDM达到了竞争性的视频生成性能，能够产生时间一致性高且临床真实的序列，并且无需依赖手动标签。这些结果展示了自监督条件对于可扩展超声心动图合成的潜力。我们的代码可在[此处](https://this https URL)获取。
URL
https://arxiv.org/abs/2512.09418
