Label-free Motion-Conditioned Diffusion Model for Cardiac Ultrasound Synthesis
This work addresses the scarcity of labeled data in echocardiography for deep learning, offering a scalable synthesis method that could benefit medical imaging applications.
The authors tackled the problem of synthesizing realistic cardiac ultrasound videos without manual labels by proposing a label-free latent diffusion model conditioned on self-supervised motion features, achieving competitive video generation performance on the EchoNet-Dynamic dataset.
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 https://github.com/ZheLi2020/LabelfreeMCDM.