Deep Learning for Localization in the Lung
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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 preoperative CT map. We developed two deep learning approaches to localize the bronchoscope in the preoperative CT map in real time and tested the algorithms across 13 trajectories in a lung phantom and 68 trajectories in 11 human cadaver lungs. In the lung phantom, we observe performance reaching 95 precision and recall of visible airways and 3 mm average position error. On a successful cadaver lung sequence, the algorithms trained on simulation alone achieved 77 position error. We also compare the effect of GAN-stylizing images and we look at aggregate statistics over the entire set of trajectories.
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