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Related Experiment Video

Updated: Jun 27, 2026

Automatic Surgery in Transcatheter Aortic Valve Replacement Using Augmented Reality
07:46

Automatic Surgery in Transcatheter Aortic Valve Replacement Using Augmented Reality

Published on: August 9, 2024

Surgical Video Understanding with Alignment-Preserving Temporal Adaptation and Action Triplet Text Alignment.

Taiyo Ikeido1, Ren Togo2, Takahiro Ogawa2

  • 1Graduate School of Information Science and Technology, Hokkaido University, Kita 14, Nishi 9, Kita-ku, Sapporo 060-0814, Hokkaido, Japan.

Bioengineering (Basel, Switzerland)
|June 26, 2026
PubMed
Summary

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This summary is machine-generated.

This study introduces an efficient framework for surgical video analysis using a pretrained vision-language model and a temporal adapter. It enhances surgical phase recognition and enables few-shot activity recognition with minimal annotations.

Area of Science:

  • Medical image analysis
  • Computer vision
  • Artificial intelligence in surgery

Background:

  • Surgical workflow understanding is crucial but hindered by the high cost and expertise required for dense video annotations.
  • Existing methods struggle with annotation efficiency for long-horizon surgical videos.

Purpose of the Study:

  • To develop a text-guided, annotation-efficient framework for surgical video understanding.
  • To leverage a frozen surgical vision-language-pretrained (VLP) encoder with a lightweight temporal adapter.
  • To improve surgical phase recognition and enable few-shot activity recognition.

Main Methods:

  • Utilized a frozen SurgVLP image encoder for frame-level visual embeddings.
  • Employed a lightweight temporal adapter to aggregate embeddings into clip-level representations.
Keywords:
CholecT50few-shot recognitionsurgical video understandingtemporal adaptationtext prototype matchingvision–language pretraining

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  • Evaluated using text-guided prototype matching for phase recognition and few-shot triplet recognition on the CholecT50 dataset.
  • Implemented a Text Contrastive method with rich phase prompts.

    • Main Results:

    • Temporal adaptation enhanced phase recognition while preserving the SurgVLP embedding space.
    • The Text Contrastive method with rich phase prompts achieved superior phase recognition performance.
    • The framework enabled classifier-free few-shot triplet recognition without a dedicated triplet classifier.

    Conclusions:

    • Effective surgical video understanding with limited annotations requires temporal adaptation and preserved alignment with the pretrained text space.
    • Semantically informative text prompts are vital for improving performance.
    • The proposed framework offers an annotation-efficient approach to surgical video analysis.