CT based computerized identification and analysis of human airways: a review

Abstract

As one of the most prevalent chronic disorders, airway disease is a major cause of morbidity and mortality worldwide. In order to understand its underlying mechanisms and to enable assessment of therapeutic efficacy of a variety of possible interventions, noninvasive investigation of the airways in a large number of subjects is of great research interest. Due to its high resolution in temporal and spatial domains, computed tomography (CT) has been widely used in clinical practices for studying the normal and abnormal manifestations of lung diseases, albeit there is a need to clearly demonstrate the benefits in light of the cost and radiation dose associated with CT examinations performed for the purpose of airway analysis. Whereas a single CT examination consists of a large number of images, manually identifying airway morphological characteristics and computing features to enable thorough investigations of airway and other lung diseases is very time-consuming and susceptible to errors. Hence, automated and semiautomated computerized analysis of human airways is becoming an important research area in medical imaging. A number of computerized techniques have been developed to date for the analysis of lung airways. In this review, we present a summary of the primary methods developed for computerized analysis of human airways, including airway segmentation, airway labeling, and airway morphometry, as well as a number of computer-aided clinical applications, such as virtual bronchoscopy. Both successes and underlying limitations of these approaches are discussed, while highlighting areas that may require additional work.

Figure 1

An illustration of leakage that frequently occurs during region-growing operations. (b) shows the image after application of a thresholding operation. When an improper threshold is selected and applied as shown in (b), there may be leakage into the parenchyma (a sudden explosion) at the leakage site. (c) shows a segmented airway tree with leakage.

Figure 2

Examples illustrating the performance of the method in Ref. in identifying airway trees.

Figure 3

An illustration of the FWHM method as used in airway wall identification. The curve denotes the intensity profile from a point located in the airway lumen outward.

Figure 4

Examples of different presentation approaches to virtual bronchoscopy: (a) volume rendering with lighting, (b) volume rendering without lighting, and (c) surface rendering with lighting.

Figure 5

Examples demonstrating the variability in morphology of airway trees.