Computer-aided diagnosis of the airways: beyond nodule detection.

Although to date, the major impetus for the development of computer-assisted diagnosis (CAD) has been the detection of pulmonary nodules, CAD should properly be viewed as a potential tool for assisting radiologic interpretation of the entire gamut of chest diseases, including not just enhanced detection of disease but also characterization and quantification, ideally leading to improved patient management. The use of CAD to improve visualization of the airways using advanced computer techniques, including sophisticated methods for obtaining 3-dimensional segmentation of the central airways and, in particular, the development of virtual bronchoscopy has been recently studied. In this paper, the authors review the development of a specific series of CAD applications enabling automated identification and characterization of chronically inflamed airways. The advantages to the use of computer methodologies to quantify peripheral airway disease include reproducible visualization methods to display the location, severity, and extent of airway dilatation, bronchial wall thickening, and the presence of mucoid impacted airways. Currently, a number of semiquantitative global scoring systems have been proposed to assess disease extent and severity in patients with bronchiectasis. Unfortunately, with the exception of patients with cystic fibrosis, these are rarely if ever employed, largely owing to the considerable inconvenience of measuring individual airway dimensions and computing a global score. It is apparent that for this specific purpose, CAD may be ideally suited. Automated staging allows for more complete assessment of the entire bronchial tree while providing improved standardization and eliminating an otherwise tedious and time-consuming task.

[1]  D A Lynch,et al.  Correlation of CT findings with clinical evaluations in 261 patients with symptomatic bronchiectasis. , 1999, AJR. American journal of roentgenology.

[2]  Koichi Yamazaki,et al.  CT-guided transbronchial biopsy using an ultrathin bronchoscope with virtual bronchoscopic navigation. , 2004, Chest.

[3]  J. Im,et al.  Normal Bronchial and Pulmonary Arterial Diameters Measured by Thin Section CT , 1995, Journal of computer assisted tomography.

[4]  Eric A. Hoffman,et al.  Accurate measurement of intrathoracic airways , 1997, IEEE Transactions on Medical Imaging.

[5]  Michael L Goris,et al.  An automated approach to quantitative air trapping measurements in mild cystic fibrosis. , 2003, Chest.

[6]  S. Armato,et al.  Automated detection of lung nodules in CT scans: preliminary results. , 2001, Medical physics.

[7]  P. Paré,et al.  Quantitative assessment of airway remodeling using high-resolution CT. , 2002, Chest.

[8]  K. Bae,et al.  Pulmonary nodules: automated detection on CT images with morphologic matching algorithm--preliminary results. , 2005, Radiology.

[9]  J. F. Lerallut,et al.  Automated detection of mucus plugs within bronchial tree in MSCT images , 2007, SPIE Medical Imaging.

[10]  Geoffrey McLennan,et al.  Virtual bronchoscopy for quantitative airway analysis , 2005, SPIE Medical Imaging.

[11]  Ronald M Summers,et al.  Road maps for advancement of radiologic computer-aided detection in the 21st century. , 2003, Radiology.

[12]  P. Paré,et al.  Computed tomographic measurements of airway dimensions and emphysema in smokers. Correlation with lung function. , 2000, American journal of respiratory and critical care medicine.

[13]  Kensaku Mori,et al.  Automated anatomical labeling of the bronchial branch and its application to the virtual bronchoscopy system , 2000, IEEE Transactions on Medical Imaging.

[14]  Shin Matsuoka,et al.  Serial change in airway lumen and wall thickness at thin-section CT in asymptomatic subjects. , 2005, Radiology.

[15]  D M Hansell,et al.  Small airways diseases: detection and insights with computed tomography. , 2001, The European respiratory journal.

[16]  E. Hoffman,et al.  High-resolution Computed Tomography Imaging of Airway Disease in Infants with Cystic , 2002 .

[17]  Sumit K. Shah,et al.  Computer-aided lung nodule detection in CT: results of large-scale observer test. , 2005, Academic radiology.

[18]  D. Hansell,et al.  Airflow obstruction in bronchiectasis: correlation between computed tomography features and pulmonary function tests , 2000, Thorax.

[19]  Milan Sonka,et al.  Intrathoracic airway trees: segmentation and airway morphology analysis from low-dose CT scans , 2005, IEEE Transactions on Medical Imaging.

[20]  Cristian Lorenz,et al.  Automated assessment of bronchial lumen, wall thickness and bronchoarterial diameter ratio of the tracheobronchial tree using high-resolution CT , 2004, CARS.

[21]  Li Fan,et al.  Identification of missed pulmonary nodules on low-dose CT lung cancer screening studies using an automatic detection system , 2003, SPIE Medical Imaging.

[22]  K. Awai,et al.  Pulmonary nodules at chest CT: effect of computer-aided diagnosis on radiologists' detection performance. , 2004, Radiology.

[23]  D M Hansell,et al.  A comparison of serial computed tomography and functional change in bronchiectasis , 2002, European Respiratory Journal.

[24]  D. McCauley,et al.  Cystic fibrosis: scoring system with thin-section CT. , 1991, Radiology.

[25]  W. Heindel,et al.  Diagnostic performance of a commercially available computer-aided diagnosis system for automatic detection of pulmonary nodules: comparison with single and double reading. , 2004, RoFo : Fortschritte auf dem Gebiete der Rontgenstrahlen und der Nuklearmedizin.

[26]  Sumit K. Shah,et al.  Computer-aided diagnosis of the solitary pulmonary nodule. , 2005, Academic radiology.

[27]  David P. Naidich,et al.  Boundary-Specific Cost Functions for Quantitative Airway Analysis , 2007, MICCAI.

[28]  Moira Chan-Yeung,et al.  High-resolution CT quantification of bronchiectasis: clinical and functional correlation. , 2002, Radiology.

[29]  J. F. Lerallut,et al.  Automated airway evaluation system for multi-slice computed tomography using airway lumen diameter, airway wall thickness and broncho-arterial ratio , 2006, SPIE Medical Imaging.

[30]  K. Marten,et al.  Computer-assisted detection of pulmonary nodules: evaluation of diagnostic performance using an expert knowledge-based detection system with variable reconstruction slice thickness settings , 2005, European Radiology.

[31]  Geoffrey McLennan,et al.  Three-dimensional path planning for virtual bronchoscopy , 2004, IEEE Transactions on Medical Imaging.

[32]  Sumit K. Shah,et al.  Computer-aided Diagnosis of the Solitary Pulmonary Nodule1 , 2005 .

[33]  S. Diederich,et al.  Interobserver variation in the diagnosis of bronchiectasis on high-resolution computed tomography , 2004, European Radiology.

[34]  S. Achenbach,et al.  Interobserver Variability of 64-Slice Computed Tomography for the Quantification of Non-Calcified Coronary Atherosclerotic Plaque , 2007, RoFo : Fortschritte auf dem Gebiete der Rontgenstrahlen und der Nuklearmedizin.

[35]  Françoise J. Prêteux,et al.  Pulmonary airways: 3-D reconstruction from multislice CT and clinical investigation , 2004, IEEE Transactions on Medical Imaging.

[36]  Carl-Fredrik Westin,et al.  Accurate Airway Wall Estimation Using Phase Congruency , 2006, MICCAI.

[37]  Françoise J. Prêteux,et al.  Modeling, segmentation, and caliber estimation of bronchi in high resolution computerized tomography , 1999, J. Electronic Imaging.

[38]  E. Hoffman,et al.  Characterization of the interstitial lung diseases via density-based and texture-based analysis of computed tomography images of lung structure and function. , 2003, Academic radiology.

[39]  J. Bogaert,et al.  Virtual bronchoscopy: accuracy and usefulness--an overview. , 2005, Seminars in ultrasound, CT, and MR.

[40]  Geoffrey McLennan,et al.  Three-dimensional human airway segmentation methods for clinical virtual bronchoscopy. , 2002, Academic radiology.