SurGrID: Controllable Surgical Simulation via Scene Graph to Image Diffusion
This work addresses the need for more realistic and interactive surgical training tools for medical professionals, representing an incremental improvement in simulation technology.
The paper tackled the problem of generating realistic and controllable surgical simulation images by introducing SurGrID, a scene graph to image diffusion model, which improved fidelity and coherence over state-of-the-art methods and demonstrated realism and controllability in a user study with clinical experts.
Surgical simulation offers a promising addition to conventional surgical training. However, available simulation tools lack photorealism and rely on hardcoded behaviour. Denoising Diffusion Models are a promising alternative for high-fidelity image synthesis, but existing state-of-the-art conditioning methods fall short in providing precise control or interactivity over the generated scenes. We introduce SurGrID, a Scene Graph to Image Diffusion Model, allowing for controllable surgical scene synthesis by leveraging Scene Graphs. These graphs encode a surgical scene's components' spatial and semantic information, which are then translated into an intermediate representation using our novel pre-training step that explicitly captures local and global information. Our proposed method improves the fidelity of generated images and their coherence with the graph input over the state-of-the-art. Further, we demonstrate the simulation's realism and controllability in a user assessment study involving clinical experts. Scene Graphs can be effectively used for precise and interactive conditioning of Denoising Diffusion Models for simulating surgical scenes, enabling high fidelity and interactive control over the generated content.