Automatic airway–artery analysis on lung CT to quantify airway wall thickening and bronchiectasis. Issue 10 (29th September 2016)

• Record Type: Journal Article
• Authors: Perez‐Rovira, Adria; Kuo, Wieying; Petersen, Jens; Tiddens, Harm A. W. M.; de Bruijne, Marleen
• Is Part Of: Medical physics. Volume 43:Issue 10(2016)
• Page Start: 5736
• Page End: 5744
• Publication Date: 2016-09-29
• Subjects: blood vessels -- computerised tomography -- diseases -- feature extraction -- image segmentation -- lung -- medical image processing -- paediatrics; Computed tomography -- Degenerative diseases (Alzheimer's, ALS, etc) -- Segmentation; Computerised tomographs -- Biological material, e.g. blood, urine; Haemocytometers -- Digital computing or data processing equipment or methods, specially adapted for specific applications -- Image data processing or generation, in general; CT -- airway -- artery -- quantification -- bronchiectasis; Vascular system -- Computed tomography -- Lungs -- Pipelines -- Medical imaging -- Bifurcations -- Radiologists -- Topology -- Multiscale methods -- Eigenvalues; Medical physics -- Periodicals; Medical physics; Geneeskunde; Natuurkunde; Toepassingen; Biophysics; Periodicals; Periodicals; Electronic journals; 610.153
• DOI: 10.1118/1.4963214
• Languages: English
• ISSNs: 0094-2405

Abstract

Abstract : Purpose: Bronchiectasis and airway wall thickening are commonly assessed in computed tomography (CT) by comparing the airway size with the size of the accompanying artery. Thus, in order to automate the quantification of bronchiectasis and wall thickening following a similar principle, there is a need for methods that automatically segment the airway and vascular trees, measure their size, and pair each airway branch with its accompanying artery. Methods: This paper combines and extends existing techniques to present a fully automated pipeline that, given a thoracic chest CT, segments, measures, and pairs airway branches with the accompanying artery, then quantifies airway wall thickening and bronchiectasis by measuring the wall–artery ratio (WAR) and lumen and outer wall airway–artery ratio (AAR). Measurements that do not use the artery size for normalization are also extracted, including wall area percentage (WAP), wall thickness ratio (WTR), and airway diameters. Results: The method was thoroughly evaluated using 8000 manual annotations of airway–artery pairs from 24 full‐inspiration pediatric CT scans (12 diseased and 12 controls). Limits of agreement between the automatically and manually measured diameters were comparable to interobserver limits of agreement. Differences in automatically obtained WAR, AAR, WAP, and WTR between bronchiectatic subjects and controls were similar as when manual annotations were used: WAR and outer AAR were significantly higher in the bronchiectatic subjects ( p < 0.05), but lumen AAR, WAP, and WTR were not. Only measurements that use artery size for normalization led to significant differences between groups, highlighting the importance of airway–artery pairing. Conclusions: The fully automatic method presented in this paper could replace time‐consuming manual annotations and visual scoring methods to quantify abnormal widening and thickening of airways.