Sensitive reconstruction of peripheral airway branches is critical for bronchoscopic navigation, yet existing deep learning (DL) methods often struggle with incomplete annotations and variability in CT acquisitions. We propose ASTRA-Net, a DL framework for segmenting both annotated and previously unlabeled airway branches from CT scans. ASTRA-Net integrates four key components: (1) auxiliary anatomical inputs (lung and vessel masks) to provide structural context, (2) an encoder-guided attention (EGA) module to refine boundary regions with high structural uncertainty, (3) a centerline- and branch-aware loss weighting scheme to emphasize clinically important peripheral branches, and (4) a resolution-robust post-optimization step to improve sensitivity under varying slice thickness. Experiments on multiple public and inhouse datasets show that ASTRA-Net achieves the highest tree and branch detection rates (TDR and BDR) while maintaining competitive overlap scores, demonstrating strong generalizability across domains. These results indicate ASTRA-Net's potential as a step toward integrating robust airway segmentation into image-guided bronchoscopic procedures. The source code is available at: https://github.com/pnu-amilab/Airway.
Lee et al. (Thu,) studied this question.