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Related Experiment Video

Updated: Mar 29, 2026

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Morphology-Aware Deep Features and Frozen Filters for Surgical Instrument Segmentation with LLM-Based Scene

Adnan Haider1, Muhammad Arsalan2, Kyungeun Cho1

  • 1Department of Computer Science and Artificial Intelligence, College of Advanced Convergence Engineering, Dongguk University-Seoul, 30 Pildongro 1-gil, Jung-gu, Seoul 04620, Republic of Korea.

Journal of Clinical Medicine
|March 28, 2026
PubMed
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This summary is machine-generated.

A new AI system, FFMS-Net, accurately detects surgical instruments in challenging live surgery conditions. This vision-based system enhances surgical safety and effectiveness by overcoming issues like blood occlusion and smoke.

Area of Science:

  • Computer Vision
  • Medical AI
  • Surgical Technology

Background:

  • Artificial intelligence is increasingly integrated into healthcare, particularly surgery.
  • Vision-based systems can improve surgical productivity, safety, and effectiveness.
  • Accurate detection of surgical instruments is difficult due to imaging challenges like blood, smoke, and blur.

Purpose of the Study:

  • To develop an advanced segmentation network for accurate surgical instrument detection.
  • To address challenges in surgical instrument segmentation caused by adverse imaging conditions.
  • To improve the robustness and accuracy of AI in surgical environments.

Main Methods:

  • Developed FFMS-Net (frozen-filters-based morphology-aware segmentation network).
  • Introduced a frozen and learnable feature pipeline (FLFP) using Sobel and Laplacian filters for edge preservation.
Keywords:
LLMartificial intelligencemedical image analysisrobotic surgerysemantic segmentationsurgical instrument

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  • Employed a tri-atrous blending (TAB) block for multi-receptive field fusion and a progressively structure-preserving decoder (PSPD) to handle class imbalance and visibility issues.

    • Main Results:

    • FFMS-Net demonstrated promising performance on three challenging surgical datasets.
    • The method achieved state-of-the-art results with only 1.5 million trainable parameters.
    • Integrated an open-source large language model for surgical scene summarization.

    Conclusions:

    • FFMS-Net effectively segments surgical instruments in complex scenarios.
    • The proposed architecture offers a robust and efficient solution for surgical vision systems.
    • The integration of LLMs provides novel capabilities for surgical scene analysis.