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[Glasses-free Three Dimensional Endoscopic Display System with Multi-face Detection].

Yilin Leng1, Zhaohui Xu2

  • 1School of Communication and Information Engineering, Shanghai University, Shanghai, 200444.

Zhongguo Yi Liao Qi Xie Za Zhi = Chinese Journal of Medical Instrumentation
|February 12, 2022
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Summary

This study enhances glasses-free 3D endoscopic displays for surgery by using deep learning to expand the optimal viewing zone. This improves surgical accuracy and visual comfort for multiple viewers during procedures and training.

Keywords:
endoscopeface detectionglasses-free three dimensional displaysurgery teaching

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Area of Science:

  • Medical Imaging
  • Computer Vision
  • Surgical Technology

Background:

  • Glasses-free 3D endoscopic displays offer depth perception in minimally invasive surgery, reducing surgeon fatigue and improving instrument accuracy.
  • Current systems suffer from narrow optimal viewing zones and crosstalk, limiting their effectiveness, particularly in surgical teaching.

Purpose of the Study:

  • To overcome the limitations of narrow viewing zones in glasses-free 3D endoscopic displays.
  • To enhance the viewing experience for multiple users in surgical applications, including teaching scenarios.

Main Methods:

  • Implemented deep learning algorithms for multi-face detection and localization.
  • Fine-tuned the 3D display grating and rearranged pixels to dynamically adjust the best view area.

Main Results:

  • Achieved a face detection accuracy of 97.88%.
  • Maintained real-time performance with a detection time of 135 frames/ms.
  • Successfully expanded the optimal viewing zone for glasses-free 3D endoscopic displays.

Conclusions:

  • Deep learning effectively addresses the narrow viewing zone limitation in 3D endoscopic displays.
  • The proposed method enhances usability for multiple viewers, improving surgical training and operative procedures.