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A Weakly Supervised Learning Approach for Surgical Instrument Segmentation from Laparoscopic Video Sequences.

Zixin Yang1, Richard Simon2, Cristian Linte1,2

  • 1Center for Imaging Science, Rochester Institute of Technology Rochester, NY 14623, USA.

Proceedings of Spie--The International Society for Optical Engineering
|June 6, 2022
PubMed
Summary

This study introduces a faster method for surgical instrument segmentation using rough scribbles instead of precise masks. This weakly supervised approach achieves high accuracy, rivaling fully supervised methods without the need for extensive manual annotation.

Keywords:
Weakly supervised segmentationlearning with noisy labelssurgical instrument segmentation

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Area of Science:

  • Computer Vision
  • Medical Imaging
  • Machine Learning

Background:

  • Fully supervised learning for surgical instrument segmentation demands laborious ground truth mask creation.
  • Existing weakly supervised methods often struggle to match the performance of fully supervised approaches.

Purpose of the Study:

  • To develop an efficient and accurate weakly supervised method for surgical instrument segmentation.
  • To reduce the annotation time and effort required for training segmentation models.

Main Methods:

  • A novel framework combining graph-model-based segmentation with deep learning.
  • Utilizing rough, scribble-like annotations as initial input for segmentation.
  • Training a deep learning model on noisy, automatically generated segmentation labels.

Main Results:

  • Achieved 76.82% IoU and 85.70% Dice score on the 2017 MICCAI EndoVis Robotic Instrument Segmentation Challenge.
  • Outperformed other weakly supervised techniques in binary instrument segmentation.
  • Demonstrated performance comparable to fully supervised methods.

Conclusions:

  • The proposed framework significantly reduces annotation effort for surgical instrument segmentation.
  • Weakly supervised learning with scribble annotations is a viable alternative to fully supervised methods.
  • The method offers a practical solution for improving surgical video analysis.