title: A hierarchical scheme for geodesic anatomical labeling of airway trees.
abstract: 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.
authors.affiliation: Department of Computer Science, University of Copenhagen, Denmark. aasa@diku.dk
authors.forename: Aasa
authors.initials: A
authors.lastname: Feragen
authors.forename: Jens
authors.initials: J
authors.lastname: Petersen
authors.forename: Megan
authors.initials: M
authors.lastname: Owen
authors.forename: Pechin
authors.initials: P
authors.lastname: Lo
authors.forename: Laura H
authors.initials: LH
authors.lastname: Thomsen
authors.forename: Mathilde M W
authors.initials: MM
authors.lastname: Wille
authors.forename: Asger
authors.initials: A
authors.lastname: Dirksen
authors.forename: Marleen
authors.initials: M
authors.lastname: de Bruijne
mesh_terms: D000465:Algorithms; D001995:Bronchography / Q000379:methods*; D006801:Humans; 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*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique; D014057:Tomography, X-Ray Computed / Q000379:methods*