A hierarchical scheme for geodesic anatomical labeling of airway trees

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review


We present a fast and robust supervised algorithm for label-
ing anatomical airway trees, based on geodesic distances in a geometric
tree-space. Possible branch label configurations for a given unlabeled air-
way tree are evaluated based on the distances to a training set of labeled
airway trees. In tree-space, the airway 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 is relatively unaffected by

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 statis-
tically similar to the inter- and intra-expert agreement, and we found no
significant correlation between COPD stage and labeling accuracy.
Original languageEnglish
Title of host publicationMedical Image Computing and Computer-Assisted Intervention – MICCAI 2012 : 15th International Conference, Nice, France, October 1-5, 2012, Proceedings, Part III
EditorsNicholas Ayache , Hervé Delingette , Polina Golland, Kensaku Mori
Number of pages9
Publication date2012
ISBN (Print)978-3-642-33453-5
ISBN (Electronic)978-3-642-33454-2
Publication statusPublished - 2012
Event15th International Conference on Medical Image Computing and Computer-Assisted Intervention - Nice, France
Duration: 1 Oct 20125 Oct 2012
Conference number: 15


Conference15th International Conference on Medical Image Computing and Computer-Assisted Intervention
SeriesLecture notes in computer science

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