White matter segmentation based on a skeletonized atlas: effects on diffusion tensor imaging studies of regions of interest.

PURPOSE: To compare the influence of conventional and skeletonized atlas-based white matter (WM) segmentation on diffusion tensor imaging (DTI) region-of-interest (ROI) investigations.
MATERIALS AND METHODS: A conventional WM atlas was skeletonized by thinning the corresponding fractional anisotropy (FA) map and labels. The conventional and skeletonized versions of the atlas were used for WM segmentation. The percentage of non-WM voxels assigned to WM labels, as well as statistical summaries of tensor-derived quantities, were compared between segmentation approaches. The ability to detect small differences in diffusion properties across groups of subjects was also compared between segmentation approaches.
RESULTS: Skeletonized segmentation resulted in significantly lower non-WM percentage (P < 0.05), higher mean FA and lower trace (P < 0.05) in most WM labels, and mainly lower standard deviation of FA and trace in labels neighboring the ventricles. In terms of maximizing the ability to detect intergroup DTI differences, skeletonized segmentation was superior in the corpus callosum, but the optimal approach varied for other WM labels.
CONCLUSION: Conventional and skeletonized atlas-based segmentation probe different portions of brain tissue and lead to different statistical summaries of diffusion characteristics in WM labels. Careful selection of segmentation approach is required for DTI investigations of WM ROIs.