ArticleSimplifying medical ultrasound : 4th International Workshop, ASMUS 2023, held in conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8, 2023, Proceedings. ASMUS (Workshop) (4th : 2023 : Vancouver, B.C. ; Online)2023
Echo from noise: synthetic ultrasound image generation using diffusion models for real image segmentation.
Article in Simplifying medical ultrasound : 4th International Workshop, ASMUS 2023, held in conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8, 2023, Proceedings. ASMUS (Workshop) (4th : 2023 : Vancouver, B.C. ; Online), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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9 citing papers in PubMed.
- Generative deep learning for foundational video translation in ultrasound.Scientific reports · 2026Article
- On the public dissemination and open sourcing of ultrasound resources, datasets and deep learning models.NPJ digital medicine · 2025Article
- Generative augmentations for improved cardiac ultrasound segmentation using diffusion models.Scientific reports · 2025Article
- Deep Learning-Based Medical Ultrasound Image and Video Segmentation Methods: Overview, Frontiers, and Challenges.Sensors (Basel, Switzerland) · 2025Review
- Privacy enhancing and generalizable deep learning with synthetic data for mediastinal neoplasm diagnosis.NPJ digital medicine · 2024Article
- Generating multi-pathological and multi-modal images and labels for brain MRI.Medical image analysis · 2024Article
- Synthetic data in radiological imaging: current state and future outlook.BJR artificial intelligence · 2024Review
- Synthetic Breast Ultrasound Images: A Study to Overcome Medical Data Sharing Barriers.Research (Washington, D.C.) · 2024Article
- Cardiac ultrasound simulation for autonomous ultrasound navigation.Frontiers in cardiovascular medicine · 2024Article
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Abstract
We propose a novel pipeline for the generation of synthetic ultrasound images via Denoising Diffusion Probabilistic Models (DDPMs) guided by cardiac semantic label maps. We show that these synthetic images can serve as a viable substitute for real data in the training of deep-learning models for ultrasound image analysis tasks such as cardiac segmentation. To demonstrate the effectiveness of this approach, we generated synthetic 2D echocardiograms and trained a neural network for segmenting the left ventricle and left atrium. The performance of the network trained on exclusively synthetic images was evaluated on an unseen dataset of real images and yielded mean Dice scores of 88.6
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