TY - GEN
T1 - Dynamic tree-based large-deformation image registration for multi-atlas segmentation
AU - Zhang, Pei
AU - Wu, Guorong
AU - Gao, Yaozong
AU - Yap, Pew Thian
AU - Shen, Dinggang
N1 - 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.
PY - 2016
Y1 - 2016
N2 - 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.
AB - 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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U2 - 10.1007/978-3-319-42016-5_13
DO - 10.1007/978-3-319-42016-5_13
M3 - Conference contribution
AN - SCOPUS:84981306163
SN - 9783319420158
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 137
EP - 145
BT - Medical Computer Vision
A2 - Kelm, Michael
A2 - Müller, Henning
A2 - Menze, Bjoern
A2 - Zhang, Shaoting
A2 - Metaxas, Dimitris
A2 - Langs, Georg
A2 - Montillo, Albert
A2 - Cai, Weidong
PB - Springer Verlag
T2 - International Workshop on Medical Image Computing for Computer Assisted Intervention, 2015 MICCAI
Y2 - 9 October 2015 through 9 October 2015
ER -