Ultrasound Image-to-Video Synthesis via Latent Dynamic Diffusion Models
Journal:
arXiv
Published Date:
Mar 19, 2025
Abstract
Ultrasound video classification enables automated diagnosis and has emerged
as an important research area. However, publicly available ultrasound video
datasets remain scarce, hindering progress in developing effective video
classification models. We propose addressing this shortage by synthesizing
plausible ultrasound videos from readily available, abundant ultrasound images.
To this end, we introduce a latent dynamic diffusion model (LDDM) to
efficiently translate static images to dynamic sequences with realistic video
characteristics. We demonstrate strong quantitative results and visually
appealing synthesized videos on the BUSV benchmark. Notably, training video
classification models on combinations of real and LDDM-synthesized videos
substantially improves performance over using real data alone, indicating our
method successfully emulates dynamics critical for discrimination. Our
image-to-video approach provides an effective data augmentation solution to
advance ultrasound video analysis. Code is available at
https://github.com/MedAITech/U_I2V.
