Synthesizing CTA Image Data for Type-B Aortic Dissection using Stable Diffusion Models
This work addresses data scarcity for medical imaging researchers, but it is incremental as it applies an existing generative AI method to a new domain.
The study tackled the problem of data scarcity in cardiovascular image processing by fine-tuning a Stable Diffusion model to generate synthetic cardiac CTA images for type-B aortic dissection, successfully producing images with features unique to this medical condition.
Stable Diffusion (SD) has gained a lot of attention in recent years in the field of Generative AI thus helping in synthesizing medical imaging data with distinct features. The aim is to contribute to the ongoing effort focused on overcoming the limitations of data scarcity and improving the capabilities of ML algorithms for cardiovascular image processing. Therefore, in this study, the possibility of generating synthetic cardiac CTA images was explored by fine-tuning stable diffusion models based on user defined text prompts, using only limited number of CTA images as input. A comprehensive evaluation of the synthetic data was conducted by incorporating both quantitative analysis and qualitative assessment, where a clinician assessed the quality of the generated data. It has been shown that Cardiac CTA images can be successfully generated using using Text to Image (T2I) stable diffusion model. The results demonstrate that the tuned T2I CTA diffusion model was able to generate images with features that are typically unique to acute type B aortic dissection (TBAD) medical conditions.