title: Quantitative airway analysis in longitudinal studies using groupwise registration and 4D optimal surfaces.
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.
authors.affiliation: Image Group, Department of Computer Science, University of Copenhagen, Denmark.
authors.forename: Jens
authors.initials: J
authors.lastname: Petersen
authors.affiliation: Centre for Medical Image Computing, Department of Medical Physics and Bioengineering, University College London, UK.
authors.forename: Marc
authors.initials: M
authors.lastname: Modat
authors.affiliation: Centre for Medical Image Computing, Department of Medical Physics and Bioengineering, University College London, UK.
authors.forename: Manuel Jorge
authors.initials: MJ
authors.lastname: Cardoso
authors.affiliation: Department of Respiratory Medicine, Gentofte Hospital, Denmark.
authors.forename: Asger
authors.initials: A
authors.lastname: Dirksen
authors.affiliation: Centre for Medical Image Computing, Department of Medical Physics and Bioengineering, University College London, UK.
authors.forename: Sebastien
authors.initials: S
authors.lastname: Ourselin
authors.affiliation: Image Group, Department of Computer Science, University of Copenhagen, Denmark.
authors.forename: Marleen
authors.initials: M
authors.lastname: de Bruijne
mesh_terms: D000328:Adult; D000368:Aged; D000465:Algorithms; D005260:Female; D056973:Four-Dimensional Computed Tomography / Q000379:methods*; D006801:Humans; D008137:Longitudinal Studies; D008168:Lung / Q000000981:diagnostic imaging*; D008171:Lung Diseases / Q000000981:diagnostic imaging*; D008297:Male; D008875:Middle Aged; D010363:Pattern Recognition, Automated / Q000379:methods*; D029424:Pulmonary Disease, Chronic Obstructive / Q000000981:diagnostic imaging*; D011856:Radiographic Image Enhancement / Q000379:methods; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D012680:Sensitivity and Specificity; D013382:Subtraction Technique; D055815:Young Adult