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. Reichenbach^1,4, Dagmar Timmann^2,5, Daniel Güllmar¹, and Andreas Deistung^1,2,5

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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