Airway Label Prediction in Video Bronchoscopy: Capturing Temporal
  Dependencies Utilizing Anatomical Knowledge
Ron Keuth Mattias Heinrich Martin Eichenlaub Marian Himstedt
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.