SimGen: A Diffusion-Based Framework for Simultaneous Surgical Image and Segmentation Mask Generation

Understand

Acquiring and annotating surgical data is often resource-intensive, ethical constraining, and requiring significant expert involvement.

• While generative AI models like text-to-image can alleviate data scarcity, incorporating spatial annotations, such as segmentation masks, is crucial for precision-driven surgical applications, simulation, and education.
• This study introduces both a novel task and method, SimGen, for Simultaneous Image and Mask Generation.
• SimGen is a diffusion model based on the DDPM framework and Residual U-Net, designed to jointly generate high-fidelity surgical images and their corresponding segmentation masks.