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CholecInstanceSeg: A Tool Instance Segmentation Dataset for Laparoscopic Surgery.

Oluwatosin Alabi1, Ko Ko Zayar Toe2, Zijian Zhou3

  • 1Kings College London, Surgical & Interventional Engineering, London, SE1 7EU, United Kingdom.

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Summary
This summary is machine-generated.

This study introduces CholecInstanceSeg, a large open-access dataset for surgical tool instance segmentation in laparoscopic cholecystectomy. It addresses the need for human surgical data, improving computer-assisted interventions.

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Area of Science:

  • Medical Imaging
  • Computer-Assisted Surgery
  • Surgical Robotics

Background:

  • Precise tool instance segmentation is crucial for advanced computer-assisted interventions in laparoscopic and robotic surgery.
  • Existing surgical datasets often lack comprehensive annotations for tool instance segmentation, and many are derived from porcine surgeries.

Purpose of the Study:

  • To introduce CholecInstanceSeg, the largest open-access dataset for tool instance segmentation in human laparoscopic cholecystectomy procedures.
  • To provide a high-quality, comprehensive dataset to advance the development and evaluation of tool instance segmentation algorithms.

Main Methods:

  • Derived from CholecT50 and Cholec80 datasets, CholecInstanceSeg includes novel annotations for laparoscopic cholecystectomy.
  • Comprises 41.9k annotated frames from 85 clinical procedures with 64.4k tool instances, each labeled with semantic masks and instance IDs.
  • Ensured annotation reliability through extensive quality control, label agreement statistics, and benchmarking with instance segmentation baselines.

Main Results:

  • Established CholecInstanceSeg as the largest open-access dataset for surgical tool instance segmentation.
  • Demonstrated the dataset's utility by benchmarking segmentation results with various instance segmentation baselines.
  • Validated the quality and reliability of the annotations through rigorous quality control measures.

Conclusions:

  • CholecInstanceSeg significantly advances the field by providing a large-scale, open-access resource for human surgical data.
  • The dataset will facilitate the development of more accurate and robust tool instance segmentation algorithms for computer-assisted surgery.
  • This resource is expected to accelerate progress in enabling advanced interventions in laparoscopic and robotic surgery.