Pathological Airway Segmentation with Cascaded Neural Networks for Bronchoscopic Navigation

摘要

Robotic bronchoscopic intervention requires detailed 3D airway maps for both localisation and enhanced visualisation, especially at peripheral airways. Patient-specific airway maps can be generated from preoperative chest CT scans. Due to pathological abnormalities and anatomical variations, automatically delineating the airway tree with distal branches is a challenging task. In the paper, we propose a cascaded 2D+3D model that has been tailored for airway segmentation from pathological CT scans. A novel 2D neural network is developed to generate the initial predictions where the peripheral airways are refined by a 3D adversarial training model. A sampling strategy based on a sequence of morphological operations is employed for the concatenation of the 2D and 3D models. The method has been validated on 20 pathological CT scans with results demonstrating improved segmentation accuracy and consistency, especially in peripheral airways.