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MASSD: Multi-scale attention single shot detector for surgical instruments.

Lingtao Yu1, Pengcheng Wang1, Yusheng Yan1

  • 1College of Mechanical and Electrical Engineering, Harbin Engineering University, Harbin, Heilongjiang Province, China.

Computers in Biology and Medicine
|July 14, 2020
PubMed
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This study introduces a novel multi-scale attention single detector for improved surgical instrument detection. The method enhances small object recognition, crucial for computer-aided surgery and surgeon training.

Area of Science:

  • Computer Vision
  • Medical Imaging
  • Surgical Technology

Background:

  • Surgical instrument detection is vital for computer-aided minimal invasive surgery.
  • Accurate detection of small surgical instruments remains a challenge in object detection.

Purpose of the Study:

  • To design a multi-scale attention single detector for enhanced surgical instrument detection.
  • To address the challenge of accurately detecting small surgical instruments.

Main Methods:

  • An innovative feature fusion technique was developed.
  • Attention maps derived from high-level features enriched low-level features.
  • Multi-scale feature maps were generated for prediction using fused features.

Main Results:

Keywords:
Convolutional neural networkDeep learningRobot-assisted surgerySurgical instrument detectionVisual attention

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  • The proposed method achieved a detection time of 0.066 seconds per frame.
  • A mean average precision of 90.08% was obtained on the ATLAS Dione dataset.
  • The feature fusion module significantly improved small surgical instrument detection performance.

Conclusions:

  • The developed feature fusion module effectively enhances semantic information in low-level features.
  • The multi-scale attention single detector shows significant potential for improving surgical instrument detection in minimally invasive surgery.