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EasyVis2: a real-time multi-view 3D visualization system for laparoscopic surgery training enhanced by a deep neural
Yung-Hong Sun1, Gefei Shen2, Jayer Fernandes2
1Electrical and Computer Engineering, University of Wisconsin-Madison, Madison, WI, United States. ysun376@wisc.edu.
Updates in Surgery
|May 26, 2026
Summary
EasyVis2 enhances minimally invasive surgery with improved 3D visualization and instrument tracking. This hands-free system uses deep learning for real-time 3D perception, aiding surgical guidance.
Area of Science:
- Medical Technology
- Computer Vision
- Surgical Robotics
Background:
- Minimally invasive surgery faces challenges with limited depth perception and restricted visual fields.
- Existing systems require improvement for enhanced 3D visualization and instrument tracking.
- Hands-free visualization is crucial for improved surgical workflow.
Purpose of the Study:
- To introduce EasyVis2, an advanced hands-free, real-time 3D visualization system.
- To enhance instrument tracking and visualization quality using deep learning.
- To improve computational efficiency for real-time surgical applications.
Main Methods:
- Integration of YOLOv8-Pose deep neural network for multi-view 2D pose estimation.
- Development of a customized surgical domain training dataset for the YOLOv8-Pose model.
- Fusion of multi-view 2D poses to compute 3D poses for real-time instrument surface rendering.
Main Results:
- The EasyVis2 system demonstrated higher 3D reconstruction accuracy and faster processing speeds than its predecessor.
- The adapted YOLOv8-Pose model achieved 96.6% precision and 95.9% sensitivity in 2D pose estimation.
- The system achieved a back-projection error of 3.809 pixels with a processing speed of 12.6 ms per frame.
Conclusions:
- EasyVis2 significantly improves 3D visualization and instrument tracking in minimally invasive surgery.
- The system shows potential for intra-operative guidance, surgical training, and computer-assisted interventions.
- Deep learning integration enhances real-time performance and accuracy for surgical visualization.
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