A hierarchical scheme for geodesic anatomical labeling of airway trees
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- feragen_miccai_2012
Accepted author manuscript, 693 KB, PDF document
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
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 statis-
tically similar to the inter- and intra-expert agreement, and we found no
significant correlation between COPD stage and labeling accuracy.
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
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 statis-
tically similar to the inter- and intra-expert agreement, and we found no
significant correlation between COPD stage and labeling accuracy.
Original language | English |
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Title of host publication | Medical Image Computing and Computer-Assisted Intervention – MICCAI 2012 : 15th International Conference, Nice, France, October 1-5, 2012, Proceedings, Part III |
Editors | Nicholas Ayache , Hervé Delingette , Polina Golland, Kensaku Mori |
Number of pages | 9 |
Publisher | Springer |
Publication date | 2012 |
Pages | 147-155 |
ISBN (Print) | 978-3-642-33453-5 |
ISBN (Electronic) | 978-3-642-33454-2 |
DOIs | |
Publication status | Published - 2012 |
Event | 15th International Conference on Medical Image Computing and Computer-Assisted Intervention - Nice, France Duration: 1 Oct 2012 → 5 Oct 2012 Conference number: 15 |
Conference
Conference | 15th International Conference on Medical Image Computing and Computer-Assisted Intervention |
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Nummer | 15 |
Land | France |
By | Nice |
Periode | 01/10/2012 → 05/10/2012 |
Series | Lecture notes in computer science |
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Volume | 7512 |
ISSN | 0302-9743 |
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