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.

翻译：实时定位支气管镜对确保介入治疗质量至关重要。然而，现有方法大多难以兼顾速度与泛化能力。为应对这些挑战，我们提出BronchoTrack，一个用于精确分支级别定位的创新实时框架，涵盖管腔检测、跟踪与气道关联。为实现实时性能，我们采用基准轻量级检测器进行高效管腔检测。我们首次将多目标跟踪引入支气管镜定位，缓解了因支气管镜快速移动和复杂气道结构导致的管腔识别时间混淆。为确保跨患者病例的泛化能力，我们提出一种无需训练的基于语义气道图的检测-气道关联方法，该图编码了支气管树结构的层级关系。在九个患者数据集上的实验表明，BronchoTrack的定位准确率达到85.64%，可访问至第四级气道。此外，我们在猪模型活体动物研究中测试了BronchoTrack，成功将支气管镜定位至第八级气道。实验评估凸显了BronchoTrack在满足准确性与泛化能力的同时具备实时性能，展示了其临床应用潜力。