Automated segmentation and morphometric analysis of the human airway tree from multidetector CT images

Abstract

Remarkable advances in computed tomography (CT) technology geared our research toward investigating the integrative function of the lung and the development of a database of the airway tree that incorporates anatomic and functional data with computational models. As part of this project, we are developing the algorithm to construct an anatomically realistic geometric model of airways from CT images. The basic concept of the algorithm is to segment as many airway trees as possible from CT images and later correct quantified parameters based on CT values. CT images are acquired with a 64-channel multidetector CT, and the airway is then extracted from them by the region-growing method while maintaining connectivity. Using this method, we extracted 428 airways up to the 14th branching generation. Although the airway diameters up to the 4th generation showed good agreement with those reported in an autopsy study, those in later generations were all greater than the reported values because of the limited resolution of the CT images. We corrected the errors in diameters by assessing the relationship between the diameter and median value of Hounsfield unit (HU) intensity of each airway; consequently, the diameters up to generation 8 agreed well with the reported values. Based on these results, we concluded that the use of HU-based correction algorithm combined with rough segmentation can be another way to improve data accuracy in the morphometric analysis of airways from CTs.