Brain atlases are commonly used in a number of applications such as MRI segmentation and surgery targetting. Our goal is to register a basal ganglia atlas to a subject using MR image registration. Existing registration methods are for the most part either too constrained (linear registration) or can deform deep brain ROIs into implausible anatomical shapes. We developed a block-matching registration method suitable for atlas registration, using a new confidence-based regularization of the vector field. The method was used to register a set of 17 manually segmented MRI onto one subject. Results show that basal ganglia structures were better registered than when using an affine registration method. © 2008 Springer Berlin Heidelberg.
CITATION STYLE
Bhattacharjee, M., Pitiot, A., Roche, A., Dormont, D., & Bardinet, E. (2008). Anatomy-preserving nonlinear registration of deep brain ROIs using confidence-based block-matching. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5242 LNCS, pp. 956–963). Springer Verlag. https://doi.org/10.1007/978-3-540-85990-1_115
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