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The Capital Region of Denmark - a part of Copenhagen University Hospital
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A hierarchical scheme for geodesic anatomical labeling of airway trees

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We present a fast and robust supervised algorithm for labeling anatomical airway trees, based on geodesic distances in a geometric tree-space. Possible branch label configurations for a given tree are evaluated based on distances to a training set of labeled trees. In tree-space, the tree topology and geometry change continuously, giving a natural way to automatically handle anatomical differences and noise. The algorithm is made efficient using a hierarchical approach, in which labels are assigned from the top down. We only use features of the airway centerline tree, which are relatively unaffected by pathology. A thorough leave-one-patient-out evaluation of the algorithm is made on 40 segmented airway trees from 20 subjects labeled by 2 medical experts. We evaluate accuracy, reproducibility and robustness in patients with chronic obstructive pulmonary disease (COPD). Performance is statistically similar to the inter- and intra-expert agreement, and we found no significant correlation between COPD stage and labeling accuracy.
Original languageEnglish
JournalMedical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
Volume15
Issue numberPt 3
Pages (from-to)147-55
Number of pages9
Publication statusPublished - 2012

    Research areas

  • Algorithms, Bronchography, Humans, Pattern Recognition, Automated, Pulmonary Disease, Chronic Obstructive, Radiographic Image Enhancement, Radiographic Image Interpretation, Computer-Assisted, Reproducibility of Results, Sensitivity and Specificity, Subtraction Technique, Tomography, X-Ray Computed

ID: 42730834