  Video bronchoscopy is routinely conducted for biopsies of lung tissue
suspected for cancer, monitoring of COPD patients and clarification of acute
respiratory problems at intensive care units. The navigation within complex
bronchial trees is particularly challenging and physically demanding, requiring
long-term experiences of physicians. This paper addresses the automatic
segmentation of bronchial orifices in bronchoscopy videos. Deep learning-based
approaches to this task are currently hampered due to the lack of
readily-available ground truth segmentation data. Thus, we present a
data-driven pipeline consisting of a k-means followed by a compact marker-based
watershed algorithm which enables to generate airway instance segmentation maps
from given depth images. In this way, these traditional algorithms serve as
weak supervision for training a shallow CNN directly on RGB images solely based
on a phantom dataset. We evaluate generalization capabilities of this model on
two in-vivo datasets covering 250 frames on 21 different bronchoscopies. We
demonstrate that its performance is comparable to those models being directly
trained on in-vivo data, reaching an average error of 11 vs 5 pixels for the
detected centers of the airway segmentation by an image resolution of 128x128.
Our quantitative and qualitative results indicate that in the context of video
bronchoscopy, phantom data and weak supervision using non-learning-based
approaches enable to gain a semantic understanding of airway structures.
