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CLAD-Net: cross-layer aggregation attention network for real-time endoscopic instrument detection.

Xiushun Zhao1, Jing Guo1, Zhaoshui He1

  • 1School of Automation, Guangdong University of Technology, Guangzhou, 510006 China.

Health Information Science and Systems
|November 29, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces CLAD-Net, a novel deep learning model for precise endoscopic instrument detection in minimally invasive surgery (MIS). CLAD-Net significantly improves surgical safety and efficiency by accurately identifying instruments in challenging visual conditions.

Keywords:
Composite attention mechanismCross-layer feature aggregationRefinement moduleSurgical instrument detection

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Area of Science:

  • Medical Imaging
  • Computer Vision
  • Surgical Technology

Background:

  • Minimally invasive surgery (MIS) relies on accurate identification of surgical instruments for effective guidance.
  • Endoscopic instrument detection is challenging due to confined spaces, occlusions, and variable lighting.

Purpose of the Study:

  • To develop an accurate and real-time detection network for endoscopic instruments in complex MIS scenarios.
  • To enhance surgical efficiency and patient safety through improved instrument recognition.

Main Methods:

  • Proposed a cross-layer aggregated attention detection network (CLAD-Net).
  • Introduced a cross-layer aggregation attention module for enhanced feature fusion and propagation.
  • Developed a composite attention mechanism (CAM) for multi-scale contextual information extraction and feature refinement.
  • Implemented a feature refinement module (RM) to improve edge and detail extraction.

Main Results:

  • CLAD-Net achieved high detection accuracy, with 98.9% on the Cholec80 dataset and 98.6% on a neuroendoscopic dataset.
  • The proposed network outperformed existing advanced detection networks in complex surgical environments.
  • Demonstrated real-time detection capabilities.

Conclusions:

  • CLAD-Net offers a robust solution for accurate endoscopic instrument detection in MIS.
  • The proposed attention mechanisms effectively address challenges like inconsistent target size and low contrast.
  • This technology has the potential to significantly advance image-guided surgery and improve patient outcomes.