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Cortical thickness estimation performed in-vivo via magnetic resonance imaging is an important technique for the diagnosis and understanding of the progression of neurodegenerative diseases. Currently, two different computational paradigms exist, with methods generally classified as either surface or voxel-based. This paper provides a much needed comparison of the surface-based method FreeSurfer and two voxel-based methods using clinical data. We demonstrate that voxel-based methods can detect similar patterns of group-wise differences as well as FreeSurfer, where the lack of deformable model constraints may provide more sensitivity but with a resulting trade-off in reproducibility.