BronchoTrack: Airway Lumen Tracking for Branch-Level Bronchoscopic
  Localization
Qingyao Tian Huai Liao Xinyan Huang Bingyu Yang Jinlin Wu Jian Chen Lujie Li Hongbin Liu
Localizing the bronchoscope in real time is essential for ensuring
intervention quality. However, most existing methods struggle to balance
between speed and generalization. To address these challenges, we present
BronchoTrack, an innovative real-time framework for accurate branch-level
localization, encompassing lumen detection, tracking, and airway association.To
achieve real-time performance, we employ a benchmark lightweight detector for
efficient lumen detection. We are the first to introduce multi-object tracking
to bronchoscopic localization, mitigating temporal confusion in lumen
identification caused by rapid bronchoscope movement and complex airway
structures. To ensure generalization across patient cases, we propose a
training-free detection-airway association method based on a semantic airway
graph that encodes the hierarchy of bronchial tree structures.Experiments on
nine patient datasets demonstrate BronchoTrack's localization accuracy of 85.64
\%, while accessing up to the 4th generation of airways.Furthermore, we tested
BronchoTrack in an in-vivo animal study using a porcine model, where it
successfully localized the bronchoscope into the 8th generation
airway.Experimental evaluation underscores BronchoTrack's real-time performance
in both satisfying accuracy and generalization, demonstrating its potential for
clinical applications.