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Abstract #3277

Prediction of iron rim lesions in multiple sclerosis using convolutional neural networks and multi-contrast 7T MRI data

René Schranzer1,2, Steffen Bollmann3, Simon Hametner2, Christian Menard1, Siegfried Trattnig4, Fritz Leutmezer2, Paulus Stefan Rommer2, Thomas Berger2, Assunta Dal-Bianco2, and Günther Grabner1,2,4
1Department of Medical Engineering, Carinthia University of Applied Sciences, Klagenfurt, Austria, 2Department of Neurology, Medical University of Vienna, Vienna, Austria, 3School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, Australia, 4Department of Biomedical Imaging and Image-guided Therapy, High Field Magnetic Resonance Centre, Vienna, Austria

In multiple sclerosis (MS) the presence of paramagnetic iron rim lesions has been shown to be indicative for progression with a more severe disease course. Our goal was to develop a pipeline based on neural networks to automatically detect, segment and classify lesions as either non-iron or iron loaded using multi-contrast 7T MRI data. A patch-based approach with two modified u-net architectures was used for segmentation and classification. Automatic, high quality lesion segmentation and their classification based on the presence or absence of iron-rims is enabled using convolutional neural networks.

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