Label-Free Motion-Conditioned Diffusion Model for Cardiac Ultrasound Synthesis
Li Z, Reynaud H, Müller J, Kainz B (2026)
Publication Type: Conference contribution
Publication year: 2026
Journal
Publisher: Springer Science and Business Media Deutschland GmbH
Book Volume: 1520 LNEE
Pages Range: 47-57
Conference Proceedings Title: Lecture Notes in Electrical Engineering
Event location: London, GBR
ISBN: 9789819574247
DOI: 10.1007/978-981-95-7425-4_5
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.
Authors with CRIS profile
Zhe Li
Professur für Image Data Exploration and Analysis
Hadrien Jean-Francois Reynaud
Johanna Müller Professur für Image Data Exploration and Analysis Bernhard Kainz Professur für Image Data Exploration and Analysis
Johanna Müller Professur für Image Data Exploration and Analysis Bernhard Kainz Professur für Image Data Exploration and Analysis
Involved external institutions
United Kingdom (GB)How to cite
APA:
Li, Z., Reynaud, H., Müller, J., & Kainz, B. (2026). Label-Free Motion-Conditioned Diffusion Model for Cardiac Ultrasound Synthesis. In Ruidan Su, Yudong Zhang, Alejandro F. Frangi (Eds.), Lecture Notes in Electrical Engineering (pp. 47-57). London, GBR: Springer Science and Business Media Deutschland GmbH.
MLA:
Li, Zhe, et al. "Label-Free Motion-Conditioned Diffusion Model for Cardiac Ultrasound Synthesis." Proceedings of the 6th International Conference on Medical Imaging and Computer-Aided Diagnosis, MICAD 2025, London, GBR Ed. Ruidan Su, Yudong Zhang, Alejandro F. Frangi, Springer Science and Business Media Deutschland GmbH, 2026. 47-57.
BibTeX: Download