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. 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.
D. Shen—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).
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Zhang, P., Wu, G., Gao, Y., Yap, PT., Shen, D. (2016). Dynamic Tree-Based Large-Deformation Image Registration for Multi-atlas Segmentation. In: Menze, B., et al. Medical Computer Vision: Algorithms for Big Data. MCV 2015. Lecture Notes in Computer Science(), vol 9601. Springer, Cham. https://doi.org/10.1007/978-3-319-42016-5_13
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DOI: https://doi.org/10.1007/978-3-319-42016-5_13
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