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Abstract #0771

cBEaST: Cerebellar Brain Extraction based on Nonlocal Segmentation Technique – A comparison with state-of-the-art methods

Daniel Güllmar1, Viktor Pfaffenrot2,3, Rossitza Draganova2, Xiang Feng1, Jürgen R Reichenbach1, Dagmar Timmann2, and Andreas Deistung1,2

1Medical Physics Group, Institute for Diagnostic and Interventional Radiology, Jena University Hospital – Friedrich Schiller-University, Jena, Germany, 2Section of Experimental Neurology, Department of Neurology, Essen University Hospital, Germany, 3Erwin L. Hahn Institute for Magnetic Resonance Imaging, University Duisburg-Essen, Essen, Germany

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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