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Related Experiment Video

Updated: Sep 22, 2025

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Exploring Intra- and Inter-Video Relation for Surgical Semantic Scene Segmentation.

Yueming Jin, Yang Yu, Cheng Chen

    IEEE Transactions on Medical Imaging
    |May 23, 2022
    PubMed
    Summary

    This study introduces STswinCL, a novel framework for surgical scene segmentation. It enhances performance by integrating intra- and inter-video relations for improved cognitive intelligence in operating theatres.

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    Area of Science:

    • Computer Vision
    • Medical Imaging
    • Artificial Intelligence

    Background:

    • Surgical scene segmentation is crucial for operating theatre cognitive intelligence.
    • Existing methods primarily use local context, limiting performance.
    • There's a need for methods capturing both local and global contextual information.

    Purpose of the Study:

    • To propose a novel framework, STswinCL, for surgical scene segmentation.
    • To enhance segmentation performance by exploring complementary intra- and inter-video relations.
    • To progressively capture global context for improved accuracy.

    Main Methods:

    • Developed a hierarchy Transformer to capture intra-video spatial and temporal cues.
    • Implemented a joint space-time window shift scheme for efficient cue aggregation.
    • Utilized pixel-to-pixel contrastive learning for inter-video relation modeling.
    • Introduced a multi-source contrast training objective for global property learning.

    Main Results:

    • STswinCL demonstrates promising performance on surgical video segmentation.
    • The method consistently outperforms previous state-of-the-art approaches.
    • Validated on EndoVis18 Challenge and CaDIS datasets.

    Conclusions:

    • The proposed STswinCL framework effectively leverages intra- and inter-video relations.
    • This approach significantly boosts surgical scene segmentation performance.
    • The method advances cognitive intelligence in surgical environments.