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

Jiantao Pu, Suicheng Gu, Shusen Liu, Shaocheng Zhu, David Wilson, Jill M. Siegfried, David Gur

Research output: Contribution to journalArticlepeer-review

43 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)2603-2616
Number of pages14
JournalMedical Physics
Volume39
Issue number5
DOIs
StatePublished - May 2012

Bibliographical note

Funding Information:
This work was supported in part by Grant Nos. R01 HL096613, P50 CA090440, P50 HL084948, and R01 HL107883, R01 HL095397, from National Institutes of Health, to the University of Pittsburgh, Bonnie J. Addario Lung Cancer Foundation, and the SPORE in Lung Cancer Career Development Program.

Keywords

  • computed tomography
  • computer-aided diagnosis
  • human airway
  • morphological analysis

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