Bronchoscopes locate airways accurately using only video input
BronchoTop: Bronchoscopy Navigation via RGB-Only Topological Localization
Computer Vision and Pattern Recognition
Summary
Doctors need to know exactly where a bronchoscope is inside the lungs to reach problem areas safely. Usually, they have to use detailed CT scans or extra sensors, which can be costly and slow. The authors created BronchoTop, a system that figures out the bronchoscope’s position by looking only at the live video from the scope and comparing it to a general airway map. This new method works in real-time and was tested on fake and real lung data, showing it is more accurate than existing methods. BronchoTop shares its code and data openly, helping others improve bronchoscopy navigation.
What this means in practice
- •For clinical bronchoscopy teams: Provide real-time airway navigation using only standard bronchoscopy video, reducing reliance on CT scans and extra sensors during procedures.
- •For medical robotics engineers: Integrate RGB-only bronchoscope localization to guide robotic systems navigating through lung airways without patient-specific imaging.
Authors
Clara Tomasini, Ana Cristina Murillo, Luis Riazuelo
Abstract
Accurate localization of the bronchoscope within the bronchial tree is essential for clinicians to be able to reach target lesions, perform biopsies and avoid misidentification of airway segments during diagnostic and therapeutic procedures. However, existing navigation systems typically rely on patient-specific CT scans or additional external sensors, increasing cost, setup time and patient radiation exposure. This work presents BronchoTop, a real-time, RGB-only framework for topological bronchoscopy localization that eliminates the need for patient-specific data. BronchoTop estimates scope location relative to a generic airway model through four modules: lumen detection and tracking, lumen-branch label association, probabilistic scope location estimation, and switch verification. By using only standard bronchoscopy video input, BronchoTop provides practical, real-time navigational assistance to physicians. Evaluation on phantom, simulated and real data demonstrates state-of-the-art accuracy, improving existing approaches performance by over 20% on real bronchoscopy sequences. BronchoTop is the first published framework including both the localization algorithms as well as all the real data used, together with code to generate additional simulations, encouraging and facilitating further developments and benchmarking. The results highlight BronchoTop's potential to enhance procedural safety, efficiency and accessibility in clinical and robotic bronchoscopy.