Year: 2026
Type: article
Abstract: Accurate bronchoscope localization is challenging because airway images contain limited texture and similar views across branches. We present BronchoLoc, a learning‐based framework that combines monocular video with a preoperative CT centerline graph to estimate the 3D bronchoscope tip position. Local visual tracking and global sequence context constrain predictions to the airway anatomy. The accompanying open‐source BronchoSim simulator generates synchronized images, depth, and trajectories (Traj) for training and evaluation. Averaged across Traj, localization RMSE was 5.65 mm in simulation with 82.8% branch selection accuracy and 8.72 mm in the physical phantom. Together, BronchoLoc and BronchoSim provide a framework for developing and evaluating image‐based airway localization.
Source: Advanced Intelligent Systems
DOI: 10.1002/aisy.70575
Institution: UNSW Sydney
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,970
OpenAlex ID: W7221475413