Abstract #0695

Automated Segmentation of Cerebellar Nuclei from Ultra-High-Field Quantitative Susceptibility Maps with Multi-Atlas Shape Fusion

Pierre-Louis Bazin^1,2,3, Andreas Deistung⁴, Andreas Schäfer⁵, Robert Turner^1,3, Jürgen Reichenbach⁴, and Dagmar Timmann⁶

¹Spinoza Centre for Neuroimaging, Amsterdam, Netherlands, ²Netherlands Institute for Neuroscience, Amsterdam, Netherlands, ³Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany, ⁴Medical Physics Group, Jena University Hospital, Jena, Germany, ⁵Siemens Healthcare GmbH, Erlangen, Germany, ⁶Department of Neurology, Essen University Hospital, University of Duisburg-Essen, Essen, Germany

Multi-atlas segmentation techniques fail to properly represent very small nuclei because of their low overlap in the fusion stage. We present a shape modeling approach that recovers more accurately such small structures, which we apply to the segmentation of the deep cerebellar nuclei.

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