AirwaySeekNet: Fine-Grained Segmentation and Completion of Peripheral Pulmonary Airways with Dynamic Reliability-Aware Supervision

Accurate segmentation of the airway tree is crucial for the diagnosis and intervention of pulmonary disease; however, delineating small peripheral airways remains challenging. The small size and complex branching of distal airways, combined with the limitations of CT imaging (partial volume effects, noise), often lead to missed bronchial segments. To address these challenges, we propose AirwaySeekNet, a dual-decoder neural network. The model introduces a Voxel-Selective Supervision (VSS) mechanism, a dynamic reliability-aware strategy that focuses training on uncertain voxels, mitigating annotation bias, and enhancing fine-branch detection. We further incorporate a Signed Distance Field (SDF) loss to enforce tubular shape constraints, improving the boundary delineation and connectivity of the airway tree. In experiments on a pig CT dataset, AirwaySeekNet outperformed state-of-the-art models, achieving higher topological completeness and finer branch detection, and the TD metric increased by 5.55% and the BD metric increased by 8.14%. It maintained high overall segmentation accuracy (Dice), with only a minor increase in false positives from the exploration of the smallest bronchi. Overall, AirwaySeekNet markedly improves airway segmentation accuracy and topology preservation, providing a more complete and reliable mapping of the bronchial tree for clinical applications.