12 March 2008 Active contour approach for accurate quantitative airway analysis
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Chronic airway disease causes structural changes in the lungs including peribronchial thickening and airway dilatation. Multi-detector computed tomography (CT) yields detailed near-isotropic images of the lungs, and thus the potential to obtain quantitative measurements of lumen diameter and airway wall thickness. Such measurements would allow standardized assessment, and physicians to diagnose and locate airway abnormalities, adapt treatment, and monitor progress over time. However, due to the sheer number of airways per patient, systematic analysis is infeasible in routine clinical practice without automation. We have developed an automated and real-time method based on active contours to estimate both airway lumen and wall dimensions; the method does not require manual contour initialization but only a starting point on the targeted airway. While the lumen contour segmentation is purely region-based, the estimation of the outer diameter considers the inner wall segmentation as well as local intensity variation, in order anticipate the presence of nearby arteries and exclude them. These properties make the method more robust than the Full-Width Half Maximum (FWHM) approach. Results are demonstrated on a phantom dataset with known dimensions and on a human dataset where the automated measurements are compared against two human operators. The average error on the phantom measurements was 0.10mm and 0.14mm for inner and outer diameters, showing sub-voxel accuracy. Similarly, the mean variation from the average manual measurement was 0.14mm and 0.18mm for inner and outer diameters respectively.
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Benjamin L. Odry, Benjamin L. Odry, Atilla P. Kiraly, Atilla P. Kiraly, Greg G. Slabaugh, Greg G. Slabaugh, Carol L. Novak, Carol L. Novak, David P. Naidich, David P. Naidich, Jean-Francois Lerallut, Jean-Francois Lerallut, } "Active contour approach for accurate quantitative airway analysis", Proc. SPIE 6916, Medical Imaging 2008: Physiology, Function, and Structure from Medical Images, 691613 (12 March 2008); doi: 10.1117/12.772592; https://doi.org/10.1117/12.772592

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