Label-free Motion-Conditioned Diffusion Model for Cardiac Ultrasound Synthesis

TL;DR

This paper introduces a label-free diffusion model for generating realistic echocardiography videos conditioned on self-supervised motion features, addressing data scarcity in cardiac ultrasound analysis.

Contribution

The proposed Motion Conditioned Diffusion Model (MCDM) is a novel label-free framework that synthesizes echocardiography videos using self-supervised motion features, eliminating the need for manual annotations.

Findings

Achieves competitive video generation performance on EchoNet-Dynamic dataset.

Produces temporally coherent and clinically realistic ultrasound sequences.

Demonstrates the effectiveness of self-supervised conditioning in medical video synthesis.

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 https://github.com/ZheLi2020/LabelfreeMCDM.