| Autor: |
Fried I; I. Fried, J. Hoelscher, S. Pizer, and R. Alterovitz are with the Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.; I. Fried is also with the Medical Scientist Training Program, University of North Carolina School of Medicine, Chapel Hill, NC, 27599, USA., Hoelscher J; I. Fried, J. Hoelscher, S. Pizer, and R. Alterovitz are with the Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA., Akulian JA; J. A. Akulian is with the Division of Pulmonary Diseases and Critical Care Medicine at the University of North Carolina at Chapel Hill, NC 27599, USA., Pizer S; I. Fried, J. Hoelscher, S. Pizer, and R. Alterovitz are with the Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA., Alterovitz R; I. Fried, J. Hoelscher, S. Pizer, and R. Alterovitz are with the Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA. |
| Abstrakt: |
Bronchoscopy is currently the least invasive method for definitively diagnosing lung cancer, which kills more people in the United States than any other form of cancer. Successfully diagnosing suspicious lung nodules requires accurate localization of the bronchoscope relative to a planned biopsy site in the airways. This task is challenging because the lung deforms intraoperatively due to respiratory motion, the airways lack photometric features, and the anatomy's appearance is repetitive. In this paper, we introduce a real-time camera-based method for accurately localizing a bronchoscope with respect to a planned needle insertion pose. Our approach uses deep learning and accounts for deformations and overcomes limitations of global pose estimation by estimating pose relative to anatomical landmarks. Specifically, our learned model considers airway bifurcations along the airway wall as landmarks because they are distinct geometric features that do not vary significantly with respiratory motion. We evaluate our method in a simulated dataset of lungs undergoing respiratory motion. The results show that our method generalizes across patients and localizes the bronchoscope with accuracy sufficient to access the smallest clinically-relevant nodules across all levels of respiratory deformation, even in challenging distal airways. Our method could enable physicians to perform more accurate biopsies and serve as a key building block toward accurate autonomous robotic bronchoscopy. |
