Ultrasound Image-to-Video Synthesis via Latent Dynamic Diffusion Models

• URL: http://arxiv.org/abs/2503.14966v1
• Date: Wed, 19 Mar 2025 07:58:43 GMT
• Title: Ultrasound Image-to-Video Synthesis via Latent Dynamic Diffusion Models
• Authors: Tingxiu Chen, Yilei Shi, Zixuan Zheng, Bingcong Yan, Jingliang Hu, Xiao Xiang Zhu, Lichao Mou,
• Abstract summary: We propose synthesizing plausible ultrasound videos from readily available, abundant ultrasound images.<n>We demonstrate strong quantitative results and visually appealing synthesized videos on the BUSV benchmark.<n>Our image-to-video approach provides an effective data augmentation solution to advance ultrasound video analysis.
• Score: 17.949823366019285
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• EchoFlow: A Foundation Model for Cardiac Ultrasound Image and Video Generation [6.849095682774907]
  We present EchoFlow, a novel framework designed to generate high-quality, privacy-preserving synthetic echocardiogram images and videos. We rigorously evaluate our synthetic datasets on the clinically relevant task of ejection fraction regression and demonstrate, for the first time, that downstream models trained exclusively on EchoFlow-generated synthetic datasets achieve performance parity with models trained on real datasets.
  arXiv  Detail & Related papers  (2025-03-28T11:51:59Z)
• AccVideo: Accelerating Video Diffusion Model with Synthetic Dataset [55.82208863521353]
  We propose AccVideo to reduce the inference steps for accelerating video diffusion models with synthetic dataset. Our model achieves 8.5x improvements in generation speed compared to the teacher model. Compared to previous accelerating methods, our approach is capable of generating videos with higher quality and resolution.
  arXiv  Detail & Related papers  (2025-03-25T08:52:07Z)
• SF-V: Single Forward Video Generation Model [57.292575082410785]
  We propose a novel approach to obtain single-step video generation models by leveraging adversarial training to fine-tune pre-trained models. Experiments demonstrate that our method achieves competitive generation quality of synthesized videos with significantly reduced computational overhead.
  arXiv  Detail & Related papers  (2024-06-06T17:58:27Z)
• Diffusion Priors for Dynamic View Synthesis from Monocular Videos [59.42406064983643]
  Dynamic novel view synthesis aims to capture the temporal evolution of visual content within videos. We first finetune a pretrained RGB-D diffusion model on the video frames using a customization technique. We distill the knowledge from the finetuned model to a 4D representations encompassing both dynamic and static Neural Radiance Fields.
  arXiv  Detail & Related papers  (2024-01-10T23:26:41Z)
• UAV-Sim: NeRF-based Synthetic Data Generation for UAV-based Perception [62.71374902455154]
  We leverage recent advancements in neural rendering to improve static and dynamic novelview UAV-based image rendering. We demonstrate a considerable performance boost when a state-of-the-art detection model is optimized primarily on hybrid sets of real and synthetic data.
  arXiv  Detail & Related papers  (2023-10-25T00:20:37Z)
• Echocardiography video synthesis from end diastolic semantic map via diffusion model [0.0]
  This paper aims to tackle the challenges by expanding upon existing video diffusion models for the purpose of cardiac video synthesis. Our focus lies in generating video using semantic maps of the initial frame during the cardiac cycle, commonly referred to as end diastole. Our model exhibits better performance compared to the standard diffusion technique in terms of multiple metrics, including FID, FVD, and SSMI.
  arXiv  Detail & Related papers  (2023-10-11T02:08:05Z)
• Motion-Conditioned Diffusion Model for Controllable Video Synthesis [75.367816656045]
  We introduce MCDiff, a conditional diffusion model that generates a video from a starting image frame and a set of strokes. We show that MCDiff achieves the state-the-art visual quality in stroke-guided controllable video synthesis.
  arXiv  Detail & Related papers  (2023-04-27T17:59:32Z)
• Feature-Conditioned Cascaded Video Diffusion Models for Precise Echocardiogram Synthesis [5.102090025931326]
  We extend elucidated diffusion models for video modelling to generate plausible video sequences from single images. Our image to sequence approach achieves an $R2$ score of 93%, 38 points higher than recently proposed sequence to sequence generation methods.
  arXiv  Detail & Related papers  (2023-03-22T15:26:22Z)
• Latent Video Diffusion Models for High-Fidelity Long Video Generation [58.346702410885236]
  We introduce lightweight video diffusion models using a low-dimensional 3D latent space. We also propose hierarchical diffusion in the latent space such that longer videos with more than one thousand frames can be produced. Our framework generates more realistic and longer videos than previous strong baselines.
  arXiv  Detail & Related papers  (2022-11-23T18:58:39Z)
• Weakly-supervised High-fidelity Ultrasound Video Synthesis with Feature Decoupling [13.161739586288704]
  In clinical practice, analysis and diagnosis often rely on US sequences rather than a single image to obtain dynamic anatomical information. This is challenging for novices to learn because practicing with adequate videos from patients is clinically unpractical. We propose a novel framework to synthesize high-fidelity US videos.
  arXiv  Detail & Related papers  (2022-07-01T14:53:22Z)
• DiVAE: Photorealistic Images Synthesis with Denoising Diffusion Decoder [73.1010640692609]
  We propose a VQ-VAE architecture model with a diffusion decoder (DiVAE) to work as the reconstructing component in image synthesis. Our model achieves state-of-the-art results and generates more photorealistic images specifically.
  arXiv  Detail & Related papers  (2022-06-01T10:39:12Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.