Abstract #2732

Accurate Cerebellum segmentation using a 3D Convolutional Neural Network and fully connected CRF

Nina Jacobsen¹, Andreas Deistung^1,2,3, Dagmar Timmann^2,3, Jürgen R. Reichenbach¹, and Daniel Güllmar¹

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

Subject-specific information about the cerebellum serves as an important biomarker in the clinical setting, however segmentation of the cerebellum is a challenging task. We demonstrate the feasibility of automatic cerebellum segmentation using a 3D convolutional neural network followed by a fully connected conditional random fields algorithm. The network was trained using 12 preprocessed T1-weighted images and corresponding manually refined ground truth segmentations. The new approach revealed robustness and similar DICE coefficients with respect to the conventional FreeSurfer approach.

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