title: A dynamic tree-based registration could handle possible large deformations among MR brain images.
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 multi-atlas 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. The dynamic tree capturing the structural relationships between images is then employed to further reduce misalignment errors. Evaluation based on two real human brain datasets, ADNI and LPBA40, shows that our method significantly improves registration and segmentation accuracy.
authors.affiliation: Department of Radiology, The University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA; Biomedical Research Imaging Center, The University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.
authors.forename: Pei
authors.initials: P
authors.lastname: Zhang
authors.affiliation: Department of Radiology, The University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA; Biomedical Research Imaging Center, The University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.
authors.forename: Guorong
authors.initials: G
authors.lastname: Wu
authors.affiliation: Department of Radiology, The University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA; Biomedical Research Imaging Center, The University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.
authors.forename: Yaozong
authors.initials: Y
authors.lastname: Gao
authors.affiliation: Department of Radiology, The University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA; Biomedical Research Imaging Center, The University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.
authors.forename: Pew-Thian
authors.initials: PT
authors.lastname: Yap
authors.affiliation: Department of Radiology, The University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA; Biomedical Research Imaging Center, The University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA; Department of Brain and Cognitive Engineering, Korea University, Seoul 02841, Republic of Korea. Electronic address: dgshen@med.unc.edu.
authors.forename: Dinggang
authors.initials: D
authors.lastname: Shen
mesh_terms: D001921:Brain / Q000000981:diagnostic imaging*; D006801:Humans; D059906:Neuroimaging / Q000379:methods*
keywords: Corresponding points; Dynamic tree; Large-deformation image registration; Multi-atlas segmentation