There are growing applications of quantitative computed tomography for assessment of pulmonary diseases by
characterizing lung parenchyma as well as the bronchial tree. Many large multi-center studies incorporating lung
imaging as a study component are interested in phenotypes relating airway branching patterns, wall-thickness, and other
morphological measures. To our knowledge, there are no fully automated airway tree segmentation methods, free of the
need for user review. Even when there are failures in a small fraction of segmentation results, the airway tree masks must
be manually reviewed for all results which is laborious considering that several thousands of image data sets are
evaluated in large studies. In this paper, we present a CT-based novel airway tree segmentation algorithm using iterative
multi-scale leakage detection, freezing, and active seed detection. The method is fully automated requiring no manual
inputs or post-segmentation editing. It uses simple intensity based connectivity and a new leakage detection algorithm to
iteratively grow an airway tree starting from an initial seed inside the trachea. It begins with a conservative threshold
and then, iteratively shifts toward generous values. The method was applied on chest CT scans of ten non-smoking
subjects at total lung capacity and ten at functional residual capacity. Airway segmentation results were compared to an
expert’s manually edited segmentations. Branch level accuracy of the new segmentation method was examined along
five standardized segmental airway paths (RB1, RB4, RB10, LB1, LB10) and two generations beyond these branches.
The method successfully detected all branches up to two generations beyond these segmental bronchi with no visual
Syed Ahmed Nadeem, Dakai Jin, Eric A. Hoffman, and Punam K. Saha, "An iterative method for airway segmentation using multiscale leakage detection," Proc. SPIE 10133, Medical Imaging 2017: Image Processing, 1013308 (Presented at SPIE Medical Imaging: February 12, 2017; Published: 24 February 2017); https://doi.org/10.1117/12.2254507.
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