An automated airway segmentation algorithm for CT images using topological leakage detection and volume freezing

Numerous multi-center studies related to chronic obstructive pulmonary disease use computed tomography (CT) based characterization of the lung parenchyma and bronchial tree to understand the disease's status and progression. To our knowledge, there are no fully automated methods for airway tree segmentation that don't require post-segmentation manual revision and intervention. In this paper, we present a novel CT-based airway tree segmentation algorithm using topological leakage detection and volume freezing. The method is fully automated requiring no manual inputs or post-segmentation editing. It uses intensity-based connectivity and novel approaches of leakage detection and volume freezing 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 threshold values. The method was applied on chest CT scans of ten non-smoking healthy subjects at total lung capacity, and the results were highly promising with no visual segmentation leakages.