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Related Experiment Video

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Technical Approach for Infrared Tracking for Soft Tissue Navigation with a Holographic Head-Mounted Display and Preclinical Validation
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Cavir: confidence-aware vision-based registration for image-guided surgery.

Hisashi Ishida1,2, Yuechao Lu3, Pourya Shirazian3

  • 1Department of Computer Science, Johns Hopkins University, Baltimore, MD, 21218, USA. hishida3@jhu.edu.

International Journal of Computer Assisted Radiology and Surgery
|March 29, 2026
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Summary

This study introduces a tracker-free vision system for surgical navigation, improving registration accuracy and detecting errors in complex scenes. It enhances safety and reliability in image-guided surgery.

Keywords:
Image-guided surgeryLaparoscopyRegistration

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Area of Science:

  • Medical Imaging
  • Computer Vision
  • Surgical Technology

Background:

  • Surgical navigation relies on accurate image-to-patient registration.
  • Challenges include tracker line-of-sight issues, anatomy deformation, and occlusions, degrading accuracy.
  • Existing methods struggle in complex operative environments.

Purpose of the Study:

  • Develop a tracker-free, robust, and explainable vision-based framework for surgical navigation.
  • Improve accuracy and reliability in complex operative scenes.
  • Enable real-time error detection and intervention.

Main Methods:

  • Fuse accurate depth estimation with dense 2D point tracking for 3D surface motion recovery from surgical video.
  • Implement a registration error detection module evaluating tracking quality, motion consistency, and 3D motion agreement.
  • Continuously update image-to-patient registration and disable mesh overlay upon error detection.

Main Results:

  • Improved target registration accuracy and robustness in phantom scenarios (occlusion, deformation) versus ICP and PREDATOR.
  • Achieved higher accuracy and ~20x faster runtime on live-porcine sequences, indicating real-time feasibility.
  • Demonstrated early detection of failure modes, guiding user intervention.

Conclusions:

  • The proposed method reduces reliance on external trackers for surgical navigation.
  • Explainable error detection enhances trustworthiness and resilience in image-guided surgery.
  • Contributes to developing more reliable surgical navigation systems.