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Abstract #2732

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

Nina Jacobsen1, Andreas Deistung1,2,3, Dagmar Timmann2,3, 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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