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Holistic OR domain modeling: a semantic scene graph approach.

Ege Özsoy1, Tobias Czempiel2, Evin Pınar Örnek2

  • 1Computer Aided Medical Procedures, Technische Universität München, Garching, Germany. ege.oezsoy@tum.de.

International Journal of Computer Assisted Radiology and Surgery
|October 12, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces semantic scene graphs (SSG) for automated operating room (OR) understanding. The developed pipeline successfully models complex surgical environments, enabling accurate clinical role and surgical phase recognition.

Keywords:
3D4D-ORSemantic scene graphSurgical scene understanding

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Area of Science:

  • Computer Vision
  • Medical Informatics
  • Artificial Intelligence

Background:

  • Operating rooms (ORs) are complex environments with numerous interacting elements.
  • Current understanding of OR dynamics relies heavily on human expertise.
  • Automated analysis of surgical procedures is crucial for improving efficiency and patient safety.

Purpose of the Study:

  • To introduce semantic scene graphs (SSG) for automated, comprehensive, and semantic understanding of the OR domain.
  • To develop a novel approach for describing and summarizing surgical environments in a structured and semantically rich manner.
  • To advance the field toward automated modeling of surgical procedures.

Main Methods:

  • Creation of the first open-source 4D SSG dataset (4D-OR) using simulated total knee replacement surgeries.
  • Annotation of the dataset with SSGs, human/object pose, clinical roles, and surgical phases.
  • Development of a neural network-based SSG generation pipeline for semantic reasoning within the OR.

Main Results:

  • Demonstration of the pipeline's capability for successful reasoning within the OR domain.
  • Validation of the SSG approach through successful application to clinical role prediction.
  • Validation of the SSG approach through successful application to surgical phase recognition.

Conclusions:

  • The developed SSG approach enables multimodal holistic operating room modeling.
  • This work has the potential to significantly enhance surgical data analysis, decision-making, and patient safety.
  • The code and dataset are publicly available to foster further research.