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Pixel-wise recognition for holistic surgical scene understanding.

Nicolás Ayobi1, Santiago Rodríguez1, Alejandra Pérez1

  • 1Center for Research and Formation in Artificial Intelligence (CinfonIA), Universidad de los Andes, Carrera 1 No. 18a-12, 111711 Bogota, Colombia.

Medical Image Analysis
|August 7, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces the GraSP dataset for holistic surgical scene understanding and the TAPIS model, achieving state-of-the-art performance in recognizing surgical phases, steps, and instruments in endoscopic videos.

Keywords:
Endoscopic visionHolistic surgical scene understandingRobot-assisted surgerySurgical instrument segmentationSurgical workflow analysisVision transformers

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Area of Science:

  • Medical Image Analysis
  • Computer Vision
  • Surgical Robotics

Background:

  • Surgical scene understanding is crucial for improving patient outcomes.
  • Existing datasets lack multi-granular analysis capabilities for complex procedures like prostatectomies.

Purpose of the Study:

  • To introduce the Holistic and Multi-Granular Surgical Scene Understanding of Prostatectomies (GraSP) dataset.
  • To develop a versatile model (TAPIS) for comprehensive surgical scene analysis.

Main Methods:

  • Created the GraSP dataset, modeling surgical understanding across multiple granularities (phases, steps, actions, instruments).
  • Developed the Transformers for Actions, Phases, Steps, and Instrument Segmentation (TAPIS) model, integrating global features and localized instrument proposals.

Main Results:

  • TAPIS demonstrated state-of-the-art performance on the GraSP dataset and other benchmarks.
  • The model effectively handles both long-term (phase/step recognition) and short-term (instrument segmentation/action detection) tasks.

Conclusions:

  • The GraSP dataset provides a foundational benchmark for holistic surgical scene understanding.
  • The TAPIS model offers a versatile framework for advancing research in endoscopic vision and surgical analytics.