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Related Experiment Video

Updated: May 22, 2025

Author Spotlight: Segmentation and VR for Advanced Neurovascular Interventions
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SurGrID: controllable surgical simulation via Scene Graph to Image Diffusion.

Yannik Frisch1,2, Ssharvien Kumar Sivakumar3, Çağhan Köksal4

  • 1TU Darmstadt, Fraunhoferstr. 5, 64297, Darmstadt, Germany. yannik_phil.frisch@tu-darmstadt.de.

International Journal of Computer Assisted Radiology and Surgery
|May 21, 2025
PubMed
Summary
This summary is machine-generated.

We developed SurGrID, a novel method for realistic surgical simulation using Scene Graphs and diffusion models. This approach enhances image fidelity and controllability for improved surgical training.

Keywords:
Controllable surgical simulationDenoising Diffusion ModelsScene Graph

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Area of Science:

  • Computer vision
  • Medical simulation
  • Artificial intelligence

Background:

  • Conventional surgical training can be enhanced with simulation.
  • Current simulation tools lack photorealism and interactivity.
  • Denoising Diffusion Models offer high-fidelity image synthesis but lack precise control.

Purpose of the Study:

  • To introduce SurGrID, a Scene Graph to Image Diffusion Model for controllable surgical scene synthesis.
  • To improve the photorealism and interactivity of surgical simulations.

Main Methods:

  • Leveraging Scene Graphs to encode spatial and semantic scene information.
  • Developing a novel pre-training step to capture local and global information for intermediate representation.
  • Utilizing a diffusion model for image synthesis conditioned on Scene Graphs.

Main Results:

  • SurGrID improves the fidelity and coherence of generated surgical images compared to state-of-the-art methods.
  • User assessment studies with clinical experts confirm the realism and controllability of the simulations.
  • Demonstrated effective use of Scene Graphs for precise and interactive conditioning of diffusion models.

Conclusions:

  • Scene Graphs enable precise and interactive control of Denoising Diffusion Models for surgical simulation.
  • The SurGrID model facilitates high-fidelity and interactive surgical scene generation.
  • This approach advances the potential of AI in surgical training and simulation.