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

doi:10.1007/978-3-642-33454-2_19

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

University of Copenhagen
University of Waterloo
University of California at Los Angeles
Copenhagen University Hospital (Nordvest)

Research output: Chapter/Conference proceeding › Conference proceeding › Academic › peer-review

25 Citations (Scopus)

Abstract

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 language	English
Title of host publication	Medical Image Computing and Computer-Assisted Intervention, MICCAI2012 - 15th International Conference, Proceedings
Editors	Nicholas Ayache, Herve Delingette, Polina Golland, Kensaku Mori
Publisher	Springer-Verlag
Pages	147-155
Number of pages	9
ISBN (Print)	9783642334535
DOIs	https://doi.org/10.1007/978-3-642-33454-2_19
Publication status	Published - 2012
Event	15th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2012 - Nice, France Duration: 1 Oct 2012 → 5 Oct 2012

Publication series

Series	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	7512 LNCS
ISSN	0302-9743

Conference

Conference	15th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2012
Country/Territory	France
City	Nice
Period	1/10/12 → 5/10/12

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.

Access to Document

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

Cite this

Feragen, A., Petersen, J., Owen, M., Lo, P., Thomsen, L. H., Wille, M. M. W., Dirksen, A., & de Bruijne, M. (2012). A hierarchical scheme for geodesic anatomical labeling of airway trees. In N. Ayache, H. Delingette, P. Golland, & K. Mori (Eds.), Medical Image Computing and Computer-Assisted Intervention, MICCAI2012 - 15th International Conference, Proceedings (pp. 147-155). Springer-Verlag. https://doi.org/10.1007/978-3-642-33454-2_19

Feragen, Aasa ; Petersen, Jens ; Owen, Megan et al. / A hierarchical scheme for geodesic anatomical labeling of airway trees. Medical Image Computing and Computer-Assisted Intervention, MICCAI2012 - 15th International Conference, Proceedings. editor / Nicholas Ayache ; Herve Delingette ; Polina Golland ; Kensaku Mori. Springer-Verlag, 2012. pp. 147-155 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 7512 LNCS).

@inproceedings{b5a8bedfeed74e0eb4d10e722b96709b,

title = "A hierarchical scheme for geodesic anatomical labeling of airway trees",

abstract = "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.",

author = "Aasa Feragen and Jens Petersen and Megan Owen and Pechin Lo and Thomsen, \{Laura H.\} and Wille, \{Mathilde M.W.\} and Asger Dirksen and \{de Bruijne\}, Marleen",

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: {\textcopyright} Springer-Verlag Berlin Heidelberg 2012.; 15th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2012 ; Conference date: 01-10-2012 Through 05-10-2012",

year = "2012",

doi = "10.1007/978-3-642-33454-2\_19",

language = "English",

isbn = "9783642334535",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

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Feragen, A, Petersen, J, Owen, M, Lo, P, Thomsen, LH, Wille, MMW, Dirksen, A & de Bruijne, M 2012, A hierarchical scheme for geodesic anatomical labeling of airway trees. in N Ayache, H Delingette, P Golland & K Mori (eds), Medical Image Computing and Computer-Assisted Intervention, MICCAI2012 - 15th International Conference, Proceedings. Springer-Verlag, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 7512 LNCS, pp. 147-155, 15th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2012, Nice, France, 1/10/12. https://doi.org/10.1007/978-3-642-33454-2_19

A hierarchical scheme for geodesic anatomical labeling of airway trees. / Feragen, Aasa; Petersen, Jens; Owen, Megan et al.
Medical Image Computing and Computer-Assisted Intervention, MICCAI2012 - 15th International Conference, Proceedings. ed. / Nicholas Ayache; Herve Delingette; Polina Golland; Kensaku Mori. Springer-Verlag, 2012. p. 147-155 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 7512 LNCS).

Research output: Chapter/Conference proceeding › Conference proceeding › Academic › peer-review

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AU - Feragen, Aasa

AU - Petersen, Jens

AU - Owen, Megan

AU - Lo, Pechin

AU - Thomsen, Laura H.

AU - Wille, Mathilde M.W.

AU - Dirksen, Asger

AU - de Bruijne, Marleen

N1 - 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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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.

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Feragen A, Petersen J, Owen M, Lo P, Thomsen LH, Wille MMW et al. A hierarchical scheme for geodesic anatomical labeling of airway trees. In Ayache N, Delingette H, Golland P, Mori K, editors, Medical Image Computing and Computer-Assisted Intervention, MICCAI2012 - 15th International Conference, Proceedings. Springer-Verlag. 2012. p. 147-155. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 7512 LNCS). doi: 10.1007/978-3-642-33454-2_19

A hierarchical scheme for geodesic anatomical labeling of airway trees

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