This study aims to develop and test a computer vision-based method for tracking and navigating a bronchoscope during lung procedures, such as biopsy or ablation. A vision-based algorithm was developed to track bronchoscope rotation and identify airway branches for navigation. The algorithm was tested on a phantom and a preclinical swine subject. The 3D airway tree was segmented from pre-procedural cone-beam CT. The airway tree was subdivided into segments and the 3D coordinates were stored using centerline extraction. Feature-based rotational tracking was calculated using SURF and brute-force matcher. Bifurcation detection was accomplished by image processing and blob detection. The localization of the bronchoscope within the airway tree was performed based on the projection of the child branches relative to the parent and related to the 3D image. A sufficient number of features to identify rotational positioning of the bronchoscope were found in 720 out of 811 (89%) video frames with an error of 3.2±2.2 degrees. Airway bifurcations were correctly identified in 29 out of 31 (90%) cases and the bronchoscope was correctly localized within a segment in seven out of seven (100%) cases. In conclusion, a computer vision-based method for tracking in the airways accurately identified the rotation of a bronchoscope and classified bifurcations to assist in navigation without the use of electromagnetic, position detection, or fiber optic shape-sensing technologies. Implementation of this technology could enable cost-controlled adoption of bronchoscopic technologies for trainees and might be utilized in low-resource settings unequipped with expensive robotic and tracking systems for diagnosis and management of suspected lung cancer.
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Computer vision-based bronchoscope tracking and navigation tool
Semantic Scholar · Medicine · 2023
Abstract
This study aims to develop and test a computer vision-based method for tracking and navigating a bronchoscope during lung procedures, such as biopsy or ablation. A vision-based algorithm was developed to track bronchoscope rotation and identify airway branches for navigation. The algorithm was tested on a phantom and a preclinical swine subject. The 3D airway tree was segmented from pre-procedural cone-beam CT. The airway tree was subdivided into segments and the 3D coordinates were stored using centerline extraction. Feature-based rotational tracking was calculated using SURF and brute-force matcher. Bifurcation detection was accomplished by image processing and blob detection. The localization of the bronchoscope within the airway tree was performed based on the projection of the child branches relative to the parent and related to the 3D image. A sufficient number of features to identify rotational positioning of the bronchoscope were found in 720 out of 811 (89%) video frames with an error of 3.2±2.2 degrees. Airway bifurcations were correctly identified in 29 out of 31 (90%) cases and the bronchoscope was correctly localized within a segment in seven out of seven (100%) cases. In conclusion, a computer vision-based method for tracking in the airways accurately identified the rotation of a bronchoscope and classified bifurcations to assist in navigation without the use of electromagnetic, position detection, or fiber optic shape-sensing technologies. Implementation of this technology could enable cost-controlled adoption of bronchoscopic technologies for trainees and might be utilized in low-resource settings unequipped with expensive robotic and tracking systems for diagnosis and management of suspected lung cancer.