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.

An Operator Independent Method for Bronchial Tree Analysis from Trachea to the Small Airways Using Volumetric Multi-Detector Computed Tomography

“…The algorithm was able to segment bronchi beyond the eleventh-twelfth generation, overcoming the problem of segmenting and analyzing bronchi beyond the fifth generation with automatic algorithms, described by Mountadon et al 15 The proposed algorithm exploits the well established thresholding and region growing techniques. 11 Finally, the required processing time (about 2 minutes on a standard PC) is reasonable and could be further reduced by the use of a high-end computer system, allowing near real-time processing. Alternative approaches were proposed in literature, as segmentation methods based on fuzzy connectivity, 16 mirror image Gaussian fit, 17 full width at half maximum, 18 19 and use of models.…”

“…To use the full potential of MDCT validated, automated, and non-subjective image analysis techniques are required. Several methods have been proposed in literature as extensively described in the review from Pu et al 11 As noted by Pu, a first recognized limitation of available airway tree segmentation schemes is the miss of a large fraction of small airways. Secondly, the unavailability of a gold standard results leaded to the use of artificial phantoms with a complexity The aim of our study is to develop and validate an operatorindependent algorithm, able to perform airways segmentation and measurement including small airways.…”

A tree-matching algorithm: Application to airways in CT images of subjects with the acute respiratory distress syndrome

“…In the bronchial research field, a review of the principal approaches can be found in (Pu et al, 2012) and (Feragen et al, 2015). A good overview of early methods is also available in (Graham and Higgins, 2006a,b) and (Metzen et al, 2007(Metzen et al, , 2009, where the authors explained, among others, why searching a graph isomorphism is not directly applicable to match anatomical trees.…”

A “loop” shape descriptor and its application to automated segmentation of airways from CT scans

“…[27][28][29][30][31][32] A relatively detailed description of these approaches can be found in a review article. 33 Despite the intensive efforts, available algorithms still miss a large fraction of small airways. Whereas abnormalities, such as obstruction, frequently occur in peripheral regions, and small airways always constitute a region of interest for investigating various lung diseases.…”