Ultrasound Image Generation using Latent Diffusion Models
This work addresses the scarcity of open-source medical images, particularly for rare conditions, by enabling synthetic data generation to train classification and segmentation models, though it is incremental as it applies an existing method to a new domain.
The authors tackled the problem of generating realistic ultrasound images for medical imaging by fine-tuning Stable Diffusion on the BUSI dataset, successfully producing high-quality breast ultrasound images that were validated by experts.
Diffusion models for image generation have been a subject of increasing interest due to their ability to generate diverse, high-quality images. Image generation has immense potential in medical imaging because open-source medical images are difficult to obtain compared to natural images, especially for rare conditions. The generated images can be used later to train classification and segmentation models. In this paper, we propose simulating realistic ultrasound (US) images by successive fine-tuning of large diffusion models on different publicly available databases. To do so, we fine-tuned Stable Diffusion, a state-of-the-art latent diffusion model, on BUSI (Breast US Images) an ultrasound breast image dataset. We successfully generated high-quality US images of the breast using simple prompts that specify the organ and pathology, which appeared realistic to three experienced US scientists and a US radiologist. Additionally, we provided user control by conditioning the model with segmentations through ControlNet. We will release the source code at http://code.sonography.ai/ to allow fast US image generation to the scientific community.