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Quantitative Airway Analysis in Longitudinal Studies Using Groupwise Registration and 4D Optimal Surfaces
Jens Petersen1, Marc Modat2, Manuel Jorge Cardoso2, Asger Dirksen3, Sebastien Ourselin2, and Marleen de Bruijne1, 4
1Image Group, Department of Computer Science, University of Copenhagen, Denmark
2Centre for Medical Image Computing, Department of Medical Physics and Bioengineering, University College London, United Kingdom
3Department of Respiratory Medicine, Gentofte Hospital, Denmark
4Biomedical Imaging Group Rotterdam, Departments of Radiology & Medical Informatics, Erasmus MC, Rotterdam, The Netherlands
Abstract. Quantifying local changes to the airway wall surfaces from computed tomography images is important in the study of diseases such as chronic obstructive pulmonary disease. Current approaches segment the airways in the individual time point images and subsequently aggregate per airway generation or perform branch matching to assess regional changes. In contrast, we propose an integrated approach analysing the time points simultaneously using a subject-specific groupwise space and 4D optimal surface segmentation. The method combines information from all time points and measurements are matched locally at any position on the resulting surfaces.
Visual inspection of the scans of 10 subjects showed increased tree length compared to the state of the art with little change in the amount of false positives. A large scale analysis of the airways of 374 subjects including a total of 1870 images showed significant correlation with lung function and high reproducibility of the measurements.
Keywords: CT, airway, lung, longitudinal, segmentation, registration
LNCS 8150, p. 287 ff.
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© Springer-Verlag Berlin Heidelberg 2013