Purpose: Navigation guidance is a key requirement for a multitude of lung
interventions using video bronchoscopy. State-of-the-art solutions focus on
lung biopsies using electromagnetic tracking and intraoperative image
registration w.r.t. preoperative CT scans for guidance. The requirement of
patient-specific CT scans hampers the utilisation of navigation guidance for
other applications such as intensive care units.
Methods: This paper addresses navigation guidance solely incorporating
bronchosopy video data. In contrast to state-of-the-art approaches we entirely
omit the use of electromagnetic tracking and patient-specific CT scans.
Guidance is enabled by means of topological bronchoscope localization w.r.t. an
interpatient airway model. Particularly, we take maximally advantage of
anatomical constraints of airway trees being sequentially traversed. This is
realized by incorporating sequences of CNN-based airway likelihoods into a
Hidden Markov Model.
Results: Our approach is evaluated based on multiple experiments inside a
lung phantom model. With the consideration of temporal context and use of
anatomical knowledge for regularization, we are able to improve the accuracy up
to to 0.98 compared to 0.81 (weighted F1: 0.98 compared to 0.81) for a
classification based on individual frames.
Conclusion: We combine CNN-based single image classification of airway
segments with anatomical constraints and temporal HMM-based inference for the
first time. Our approach renders vision-only guidance for bronchoscopy
interventions in the absence of electromagnetic tracking and patient-specific
CT scans possible.

本文提出了一种仅利用支气管镜视屏数据来实现导航引导的方法，通过 CNN 对气道分割进行单帧分类并采用基于状态转移的 HMM 模型应用解剖学约束和时间上文本信息对支气管树进行定位，能够在无需电磁跟踪和 CT 扫描的情况下，实现对支气管镜操作的视觉引导。