Dynamic tree-based large-deformation image registration for multi-atlas segmentation

Pei Zhang; Guorong Wu; Yaozong Gao; Pew Thian Yap; Dinggang Shen

doi:10.1007/978-3-319-42016-5_13

Dynamic tree-based large-deformation image registration for multi-atlas segmentation

Pei Zhang, Guorong Wu, Yaozong Gao, Pew Thian Yap, Dinggang Shen

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Citation (Scopus)

Abstract

Multi-atlas segmentation is a powerful approach to automated anatomy delineation via fusing label information from a set of spatially normalized atlases. For simplicity, many existing methods perform pairwise image registration, leading to inaccurate segmentation especially when shape variation is large. In this paper, we propose a dynamic tree-based strategy for effective large-deformation registration and multiatlas segmentation. To deal with local minima caused by large shape variation, coarse estimates of deformations are first obtained via alignment of automatically localized landmark points. A dynamic tree capturing the structural relationships between images is then used to further reduce misalignment errors. Validation on two real human brain datasets, ADNI and LPBA40, shows that our method significantly improves registration and segmentation accuracy.

Original language	English
Title of host publication	Medical Computer Vision
Subtitle of host publication	Algorithms for Big Data - International Workshop, MCV 2015 and Held in Conjunction with MICCAI 2015, Revised Selected Papers
Editors	Michael Kelm, Henning Müller, Bjoern Menze, Shaoting Zhang, Dimitris Metaxas, Georg Langs, Albert Montillo, Weidong Cai
Publisher	Springer Verlag
Pages	137-145
Number of pages	9
ISBN (Print)	9783319420158
DOIs	https://doi.org/10.1007/978-3-319-42016-5_13
Publication status	Published - 2016
Externally published	Yes
Event	International Workshop on Medical Image Computing for Computer Assisted Intervention, 2015 MICCAI - Germany, Germany Duration: 2015 Oct 9 → 2015 Oct 9

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	9601 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Other

Other	International Workshop on Medical Image Computing for Computer Assisted Intervention, 2015 MICCAI
Country/Territory	Germany
City	Germany
Period	15/10/9 → 15/10/9

Bibliographical note

Funding Information:
This work was supported in part by a UNC BRIC-Radiology start-up fund, and NIH grants (EB006733, EB008374, EB009634, MH088520 and NIHM 5R01MH091645-02)

Publisher Copyright:
© Springer International Publishing Switzerland 2016.

Copyright:
Copyright 2017 Elsevier B.V., All rights reserved.

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-319-42016-5_13

Cite this

Zhang, P., Wu, G., Gao, Y., Yap, P. T., & Shen, D. (2016). Dynamic tree-based large-deformation image registration for multi-atlas segmentation. In M. Kelm, H. Müller, B. Menze, S. Zhang, D. Metaxas, G. Langs, A. Montillo, & W. Cai (Eds.), Medical Computer Vision: Algorithms for Big Data - International Workshop, MCV 2015 and Held in Conjunction with MICCAI 2015, Revised Selected Papers (pp. 137-145). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9601 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-42016-5_13

Dynamic tree-based large-deformation image registration for multi-atlas segmentation. / Zhang, Pei; Wu, Guorong; Gao, Yaozong et al.
Medical Computer Vision: Algorithms for Big Data - International Workshop, MCV 2015 and Held in Conjunction with MICCAI 2015, Revised Selected Papers. ed. / Michael Kelm; Henning Müller; Bjoern Menze; Shaoting Zhang; Dimitris Metaxas; Georg Langs; Albert Montillo; Weidong Cai. Springer Verlag, 2016. p. 137-145 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9601 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Zhang, P, Wu, G, Gao, Y, Yap, PT & Shen, D 2016, Dynamic tree-based large-deformation image registration for multi-atlas segmentation. in M Kelm, H Müller, B Menze, S Zhang, D Metaxas, G Langs, A Montillo & W Cai (eds), Medical Computer Vision: Algorithms for Big Data - International Workshop, MCV 2015 and Held in Conjunction with MICCAI 2015, Revised Selected Papers. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9601 LNCS, Springer Verlag, pp. 137-145, International Workshop on Medical Image Computing for Computer Assisted Intervention, 2015 MICCAI, Germany, Germany, 15/10/9. https://doi.org/10.1007/978-3-319-42016-5_13

Zhang P, Wu G, Gao Y, Yap PT, Shen D. Dynamic tree-based large-deformation image registration for multi-atlas segmentation. In Kelm M, Müller H, Menze B, Zhang S, Metaxas D, Langs G, Montillo A, Cai W, editors, Medical Computer Vision: Algorithms for Big Data - International Workshop, MCV 2015 and Held in Conjunction with MICCAI 2015, Revised Selected Papers. Springer Verlag. 2016. p. 137-145. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-42016-5_13

Zhang, Pei ; Wu, Guorong ; Gao, Yaozong et al. / Dynamic tree-based large-deformation image registration for multi-atlas segmentation. Medical Computer Vision: Algorithms for Big Data - International Workshop, MCV 2015 and Held in Conjunction with MICCAI 2015, Revised Selected Papers. editor / Michael Kelm ; Henning Müller ; Bjoern Menze ; Shaoting Zhang ; Dimitris Metaxas ; Georg Langs ; Albert Montillo ; Weidong Cai. Springer Verlag, 2016. pp. 137-145 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "Multi-atlas segmentation is a powerful approach to automated anatomy delineation via fusing label information from a set of spatially normalized atlases. For simplicity, many existing methods perform pairwise image registration, leading to inaccurate segmentation especially when shape variation is large. In this paper, we propose a dynamic tree-based strategy for effective large-deformation registration and multiatlas segmentation. To deal with local minima caused by large shape variation, coarse estimates of deformations are first obtained via alignment of automatically localized landmark points. A dynamic tree capturing the structural relationships between images is then used to further reduce misalignment errors. Validation on two real human brain datasets, ADNI and LPBA40, shows that our method significantly improves registration and segmentation accuracy.",

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Dynamic tree-based large-deformation image registration for multi-atlas segmentation

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