TY - JOUR

T1 - Geodesic atlas-based labeling of anatomical trees

T2 - application and evaluation on airways extracted from CT

AU - Feragen, Aasa

AU - Petersen, Jens

AU - Owen, Megan

AU - Lo, Pechin

AU - Thomsen, Laura Hohwu

AU - Wille, Mathilde Marie Winkler

AU - Dirksen, Asger

AU - de Bruijne, Marleen

PY - 2015

Y1 - 2015

N2 - We present a fast and robust atlas-based algorithm for labeling airway trees, using geodesic distances in a geometric tree-space. Possible branch label configurations for an unlabeled airway tree are evaluated using distances to a training set of labeled airway trees. In tree-space, airway tree topology and geometry change continuously, giving a natural automatic handling of anatomical differences and noise. A hierarchical approach makes the algorithm efficient, assigning labels from the trachea and downwards. Only the airway centerline tree is used, which is relatively unaffected by pathology. The algorithm is evaluated on 80 segmented airway trees from 40 subjects at two time points, labeled by 3 medical experts each, testing accuracy, reproducibility and robustness in patients with Chronic Obstructive Pulmonary Disease (COPD). The accuracy of the algorithm is statistically similar to that of the experts and not significantly correlated with COPD severity. The reproducibility of the algorithm is significantly better than that of the experts, and negatively correlated with COPD severity. Evaluation of the algorithm on a longitudinal set of 8724 trees from a lung cancer screening trial shows that the algorithm can be used in large scale studies with high reproducibility, and that the negative correlation of reproducibility with COPD severity can be explained by missing branches, for instance due to segmentation problems in COPD patients. We conclude that the algorithm is robust to COPD severity given equally complete airway trees, and comparable in performance to that of experts in pulmonary medicine, emphasizing the suitability of the labeling algorithm for clinical use.

AB - We present a fast and robust atlas-based algorithm for labeling airway trees, using geodesic distances in a geometric tree-space. Possible branch label configurations for an unlabeled airway tree are evaluated using distances to a training set of labeled airway trees. In tree-space, airway tree topology and geometry change continuously, giving a natural automatic handling of anatomical differences and noise. A hierarchical approach makes the algorithm efficient, assigning labels from the trachea and downwards. Only the airway centerline tree is used, which is relatively unaffected by pathology. The algorithm is evaluated on 80 segmented airway trees from 40 subjects at two time points, labeled by 3 medical experts each, testing accuracy, reproducibility and robustness in patients with Chronic Obstructive Pulmonary Disease (COPD). The accuracy of the algorithm is statistically similar to that of the experts and not significantly correlated with COPD severity. The reproducibility of the algorithm is significantly better than that of the experts, and negatively correlated with COPD severity. Evaluation of the algorithm on a longitudinal set of 8724 trees from a lung cancer screening trial shows that the algorithm can be used in large scale studies with high reproducibility, and that the negative correlation of reproducibility with COPD severity can be explained by missing branches, for instance due to segmentation problems in COPD patients. We conclude that the algorithm is robust to COPD severity given equally complete airway trees, and comparable in performance to that of experts in pulmonary medicine, emphasizing the suitability of the labeling algorithm for clinical use.

U2 - 10.1109/TMI.2014.2380991

DO - 10.1109/TMI.2014.2380991

M3 - Journal article

C2 - 25532169

VL - 34

SP - 1212

EP - 1226

JO - I E E E Transactions on Medical Imaging

JF - I E E E Transactions on Medical Imaging

SN - 0278-0062

IS - 6

ER -