An automatic segmentation of the cerebellum is required to determine the cerebellar volume and for improving spatial normalization in voxel-based analysis approaches. While existing segmentation approaches typically work quite robust in healthy subjects, errors in segmentation increase with cerebellar atrophy and typically require manual corrections. We introduce a novel cerebellum segmentation approach, referred to as cBEaST, that relies on a dedicated multi-resolution segmentation library with manually edited cerebellar masks of both healthy and diseased subjects in combination with multi-atlas-propagation and segmentation as implemented in BEaST. Finally segmentation of the cerebellum with BEaST is compared with the alternative techniques SUIT and FreeSurfer.
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