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Instrument-Tissue-Guided Surgical Action Triplet Detection via Textual-Temporal Trail Exploration.

Jialun Pei, Jiaan Zhang, Guanyi Qin

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    Summary

    This study introduces ITG-Trip, a novel method for surgical action triplet detection. It improves instrument localization and action recognition in laparoscopic surgery videos by integrating visual and textual data.

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    Area of Science:

    • Computer Vision
    • Medical Imaging
    • Surgical Robotics

    Background:

    • Surgical action triplet detection is crucial for understanding laparoscopic surgery.
    • Accurate instrument localization and action recognition remain challenging.
    • Existing methods struggle to fully leverage spatial and semantic information.

    Purpose of the Study:

    • To develop a method for simultaneous surgical instrument localization and action triplet recognition.
    • To enhance the comprehensive understanding of intraoperative surgical scenes.
    • To improve the accuracy of surgical workflow analysis.

    Main Methods:

    • Proposed Instrument-Tissue-Guided Triplet detector (ITG-Trip) using pseudo-localization labeling.
    • Implemented Visual-Linguistic Association (VLA) module with a pre-trained text encoder for textual cues.
    • Introduced Mamba-enhanced Spatial-temporal Perception (MSP) decoder integrating Mamba and Transformer blocks.

    Main Results:

    • ITG-Trip significantly outperforms state-of-the-art methods on the CholecT50 benchmark.
    • Achieved superior performance in both instrument localization and action triplet detection.
    • Demonstrated effective integration of visual, textual, and temporal information.

    Conclusions:

    • The proposed ITG-Trip method enhances surgical scene understanding through improved action triplet detection and instrument localization.
    • The integration of visual-linguistic association and Mamba-enhanced spatial-temporal perception is effective.
    • This work advances the field of automated surgical workflow analysis.