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Updated: Jul 31, 2025

Robotized Testing of Camera Positions to Determine Ideal Configuration for Stereo 3D Visualization of Open-Heart Surgery

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Published on: August 12, 2021

Investigating keypoint descriptors for camera relocalization in endoscopy surgery.

Isabela Hernández¹, Roger Soberanis-Mukul², Jan Emily Mangulabnan²

¹Johns Hopkins University, Baltimore, 21211, MD, USA. iherna12@jhu.edu.

International Journal of Computer Assisted Radiology and Surgery

|May 9, 2023

Summary

This study introduces a new method for tracking endoscopic cameras during surgery using patient-specific learning-based descriptors. The approach improves camera localization accuracy even with anatomical changes, crucial for surgical navigation.

Keywords:

Anatomical landmark recognition Camera relocalization Learning-based descriptors Sinus surgery navigation

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Area of Science:

Computer Vision
Machine Learning
Surgical Navigation

Background:

Endoscopic video analysis enables dense anatomical reconstruction.
Reliable camera tracking is essential for surgical navigation systems.
Frequent endoscope removal and re-insertion challenge camera localization.

Purpose of the Study:

Investigate learning-based keypoint descriptors for six degree-of-freedom camera pose estimation.
Assess camera tracking performance in intraoperative endoscopic sequences with anatomical changes.
Evaluate robustness to anatomical modifications from surgical resection.

Main Methods:

Utilize dense structure from motion (SfM) reconstruction with patient-specific learning-based descriptors.
Establish 2D-3D correspondences for Perspective-n-Point (PnP) camera pose estimation.
Evaluate the method on six intraoperative sequences from cadaveric subjects with anatomical modifications.

Main Results:

Achieved average translation and rotation errors of 3.9 mm and 0.2 radians, respectively.
Localized 21.86% of cameras across six sequences.
Demonstrated comparable pose estimation performance to HardNet++ with improved camera localization.

Conclusions:

Patient-specific learning-based descriptors effectively relocalize images across modified anatomy.
Camera relocalization in endoscopic sequences remains challenging.
Future research is needed to enhance robustness and accuracy.