A hierarchical scheme for geodesic anatomical labeling of airway trees

Aasa Feragen, Jens Petersen, Megan Owen, Pechin Lo, Laura H. Thomsen, Mathilde M.W. Wille, Asger Dirksen, Marleen de Bruijne

Research output: Chapter/Conference proceedingConference proceedingAcademicpeer-review

23 Citations (Scopus)


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
Title of host publicationMedical Image Computing and Computer-Assisted Intervention, MICCAI2012 - 15th International Conference, Proceedings
EditorsNicholas Ayache, Herve Delingette, Polina Golland, Kensaku Mori
Number of pages9
ISBN (Print)9783642334535
Publication statusPublished - 2012
Event15th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2012 - Nice, France
Duration: 1 Oct 20125 Oct 2012

Publication series

SeriesLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7512 LNCS


Conference15th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2012

Bibliographical note

Funding Information:
Acknowledgement. This research was supported by the Lundbeck Foundation; AstraZeneca; The Danish Council for Strategic Research; Netherlands Organisation for Scientific Research; Centre for Stochastic Geometry and Advanced Bioimaging, funded by the Villum Foundation. M.O. was funded by a Fields-Ontario Postdoctoral Fellowship.

Publisher Copyright:
© Springer-Verlag Berlin Heidelberg 2012.


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