A hierarchical scheme for geodesic anatomical labeling of airway trees

TY - GEN

T1 - A hierarchical scheme for geodesic anatomical labeling of airway trees

AU - Feragen, Aasa

AU - Petersen, Jens

AU - Owen, Megan

AU - Lo, Pechin Chien Pau

AU - Thomsen, Laura

AU - Wille, Mathilde M. W.

AU - Dirksen, Asger

AU - de Bruijne, Marleen

N1 - Conference code: 15

PY - 2012

Y1 - 2012

N2 - 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.

AB - 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.

U2 - 10.1007/978-3-642-33454-2_19

DO - 10.1007/978-3-642-33454-2_19

M3 - Article in proceedings

SN - 978-3-642-33453-5

T3 - Lecture notes in computer science

SP - 147

EP - 155

BT - Medical Image Computing and Computer-Assisted Intervention – MICCAI 2012

A2 - Ayache , Nicholas

A2 - Delingette , Hervé

A2 - Golland, Polina

A2 - Mori , Kensaku

PB - Springer

T2 - 15th International Conference on Medical Image Computing and Computer-Assisted Intervention

Y2 - 1 October 2012 through 5 October 2012

ER -