Quantitative Airway Analysis in Longitudinal Studies Using Groupwise Registration and 4D Optimal Surfaces

  • Jens Petersen
  • Marc Modat
  • Manuel Jorge Cardoso
  • Asger Dirksen
  • Sebastien Ourselin
  • Marleen de Bruijne
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8150)


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.


CT airway lung longitudinal segmentation registration 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Jens Petersen
    • 1
  • Marc Modat
    • 2
  • Manuel Jorge Cardoso
    • 2
  • Asger Dirksen
    • 3
  • Sebastien Ourselin
    • 2
  • Marleen de Bruijne
    • 1
    • 4
  1. 1.Image Group, Department of Computer ScienceUniversity of CopenhagenDenmark
  2. 2.Centre for Medical Image Computing, Department of Medical Physics and BioengineeringUniversity College LondonUnited Kingdom
  3. 3.Department of Respiratory MedicineGentofte HospitalDenmark
  4. 4.Biomedical Imaging Group Rotterdam, Departments of Radiology & Medical InformaticsErasmus MCRotterdamThe Netherlands

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