  Navigating surgical tools in the dynamic and tortuous anatomy of the lung's
airways requires accurate, real-time localization of the tools with respect to
the preoperative scan of the anatomy. Such localization can inform human
operators or enable closed-loop control by autonomous agents, which would
require accuracy not yet reported in the literature. In this paper, we
introduce a deep learning architecture, called OffsetNet, to accurately
localize a bronchoscope in the lung in real-time. After training on only 30
minutes of recorded camera images in conserved regions of a lung phantom,
OffsetNet tracks the bronchoscope's motion on a held-out recording through
these same regions at an update rate of 47 Hz and an average position error of
1.4 mm. Because this model performs poorly in less conserved regions, we
augment the training dataset with simulated images from these regions. To
bridge the gap between camera and simulated domains, we implement domain
randomization and a generative adversarial network (GAN). After training on
simulated images, OffsetNet tracks the bronchoscope's motion in less conserved
regions at an average position error of 2.4 mm, which meets conservative
thresholds required for successful tracking.
