Fully automatic airway segmentation in CT images via dual-stage neural network with topology-constrained centerline completion

Background

Precise segmentation of airways from computed tomography (CT) images is critical for pre-procedural planning in navigational bronchoscopy; however, existing methods often suffer from distal branch fractures and morphological losses due to slender diameters, low contrast, and noise. To address these limitations and achieve accurate, topologically complete airway segmentation, this study proposed a novel dual-stage neural network with topology-constrained centerline completion.