Stand-alone graph-based lung airway labeling with quantitative validations.

Authors

Hairong Chen, Arezoo Modiri, Amit Sawant

Published in

Medical physics. Volume 53. Issue 9. Pages e70634.

Abstract

Previous studies have shown that protecting airways connected to high-functioning lung parenchyma reduces the risk of post-treatment radiation-induced lung injury. To account for airway sparing in radiotherapy planning, a critical requirement is to characterize the geometry and radiation response of individual airway segments.
To report on automated individual airway labeling framework with branch dimensions extracted from CT-derived virtual bronchoscopy images.
We developed an automated graph-based topological labeling algorithm with bifurcation detections and context-based denoising. In-house modules for estimating airway diameters and tube length were also developed. Both labeling and meta-information estimation were validated against a commercial virtual bronchoscopy software in data from 20 lung cancer patients. Labeling metrics were segmentation surface scores: i.e., average symmetric surface distance (ASSD), normalized surface distance (NSD), and 95th percentile surface Hausdorff (HD95surf). Meta-information metrics were absolute errors of branch major diameter, minor diameter, and tube length. Mean absolute error (MAE) and root mean square error (RMSE) were calculated to assess meta-information deviations in both validations.
Mean and standard deviations were 0.56 ± 0.21 mm for ASSD, 87.66 ± 3.20% for NSD, and 2.51 ± 0.68 mm for HD95surf. Deviations of meta-information metrics in labeling validation were MAE/RMSE: 0.33 ± 0.09/0.67 ± 0.16 mm for major diameter, 0.24 ± 0.07/0.53 ± 0.15 mm for minor diameter, and 3.27 ± 0.82/5.53 ± 1.53 mm for tube length. Deviations of meta-information metrics in meta-information validation were MAE/RMSE: 1.81 ± 0.30/2.31 ± 0.41 mm for major diameter, 1.25 ± 0.27/1.14 ± 0.26 mm for minor diameter, and 2.20 ± 0.32/3.24 ± 0.49 mm for tube length.
The proposed open-source framework provides stand-alone, fully-automated airway labeling with very good agreement with a commercial virtual bronchoscopy system. Our method shows strong potential for use in functionally-guided lung sparing in radiation treatment plan optimization.

PMID:
42658074
Bibliographic data and abstract were imported from PubMed on 27 Aug 2026.

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