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Abstract #2825

Automatic Dentate Nuclei Segmentation based on Quantitative Susceptibility Maps using a Convolutional Neural Network: Application to Healthy Controls and Cerebellar Ataxia Patients

Nina Jacobsen1, Dominik Jäschke2, Sophia Luise Goericke3, Jürgen R. Reichenbach1,4, Dagmar Timmann2,5, Daniel Güllmar1, and Andreas Deistung1,2,5

1Medical Physics Group, Institute for Diagnostic and Interventional Radiology, Jena University Hospital, Jena, Germany, 2Department of Neurology, Essen University Hospital, University of Duisburg-Essen, Essen, Germany, 3Department of Diagnostic and Interventional Radiology and Neuroradiology, Essen University Hospital, University of Duisburg-Essen, Essen, Germany, 4Michael-Stifel-Center-Jena for Data-Driven and Simulation Science, Friedrich-Schiller University Jena, Jena, Germany, 5Erwin L. Hahn Institute for Magnetic Resonance Imaging, University of Duisburg-Essen, Essen, Germany

Non-invasive visualization and segmentation of the dentate nucleus is helpful for characterizing neurological diseases. Therefore, we set up an automatic segmentation strategy relying on a convolutional neural network (CNN) for the delineation of the dentate nucleus based on quantitative susceptibility maps. We trained the network on 101 healthy controls and 118 patients suffering from various types of cerebellar ataxia. We were able to demonstrate that the CNN accurately segments the dentate nuclei in 26 healthy controls and 21 SCA6 patients with volume estimates being in agreement with literature.

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