  Lung cancer is the leading cause of cancer-related death worldwide, and early
diagnosis is critical to improving patient outcomes. To diagnose cancer, a
highly trained pulmonologist must navigate a flexible bronchoscope deep into
the branched structure of the lung for biopsy. The biopsy fails to sample the
target tissue in 26-33% of cases largely because of poor registration with the
preoperative CT map. To improve intraoperative registration, we develop two
deep learning approaches to localize the bronchoscope in the preoperative CT
map based on the bronchoscopic video in real-time, called AirwayNet and
BifurcationNet. The networks are trained entirely on simulated images derived
from the patient-specific CT. When evaluated on recorded bronchoscopy videos in
a phantom lung, AirwayNet outperforms other deep learning localization
algorithms with an area under the precision-recall curve of 0.97. Using
AirwayNet, we demonstrate autonomous driving in the phantom lung based on video
feedback alone. The robot reaches four targets in the left and right lungs in
95% of the trials. On recorded videos in eight human cadaver lungs, AirwayNet
achieves areas under the precision-recall curve ranging from 0.82 to 0.997.
