Airway label prediction in video bronchoscopy: capturing temporal dependencies utilizing anatomical knowledge.

Abstract

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 utilization of navigation guidance for other applications such as intensive care units. This paper addresses bronchoscope tracking by solely incorporating video data. In contrast to state-of-the-art approaches, we entirely omit the use of electromagnetic tracking and patient-specific CT scans to avoid changes in clinical workflows and additional hardware requirements in intensive care units. Guidance is enabled by means of topological bronchoscope localization w.r.t. a generic 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. 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. We combine CNN-based single image classification of airway segments with anatomical constraints and temporal HMM-based inference for the first time. Our approach shows first promising results in vision-based guidance for bronchoscopy interventions in the absence of electromagnetic tracking and patient-specific CT scans.