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Related Experiment Video

Updated: Oct 15, 2025

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MIcro-surgical anastomose workflow recognition challenge report.

Arnaud Huaulmé1, Duygu Sarikaya2, Kévin Le Mut1

  • 1Univ Rennes,INSERM, LTSI - UMR 1099, Rennes, F35000, France.

Computer Methods and Programs in Biomedicine
|October 23, 2021
PubMed
Summary

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This summary is machine-generated.

The MIcro-Surgical Anastomose Workflow recognition (MISAW) challenge developed deep learning models for surgical workflow recognition using video and kinematic data. Best models achieved high accuracy for phase and step recognition, but activity recognition requires further improvement for clinical applications.

Area of Science:

  • Computer-assisted surgery
  • Surgical workflow recognition
  • Deep learning in medicine

Background:

  • Surgical workflow recognition is crucial for developing context-aware computer-assisted surgical systems.
  • Video data from various surgical settings (laparoscopic, open, robot-assisted) is increasingly available for analysis.
  • Integrating kinematic data can potentially enhance workflow recognition accuracy.

Purpose of the Study:

  • To design and evaluate the MIcro-Surgical Anastomose Workflow recognition on training sessions (MISAW) challenge.
  • To develop and compare workflow recognition models using kinematic data and/or videos.
  • To assess model performance across different levels of workflow granularity: phase, step, and activity.

Main Methods:

  • The MISAW challenge utilized a dataset of 27 micro-surgical anastomosis sequences with video, kinematics, and detailed workflow annotations.
Keywords:
Multi-modalityOR of the futureSurgical process modelWorkflow recognition

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  • Participants developed deep learning models, including Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs).
  • Performance was evaluated using average application-dependent balanced accuracy (AD-Accuracy), considering class imbalance and clinical relevance.

    • Main Results:

    • Six teams participated, employing various deep learning architectures.
    • Top models achieved >95% accuracy for phase recognition, >80% for step recognition, >60% for activity recognition, and >75% for multi-granularity recognition.
    • RNN-based models generally outperformed CNN-based models, particularly in multi-granularity tasks, except for activity recognition.

    Conclusions:

    • High-granularity recognition (phases, steps) shows potential for applications like predicting surgical time.
    • Activity recognition accuracy remains a challenge for direct clinical application.
    • The MISAW dataset is publicly available to foster further research in surgical workflow recognition.