Abstract #3277

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

René Schranzer^1,2, Steffen Bollmann³, Simon Hametner², Christian Menard¹, Siegfried Trattnig⁴, Fritz Leutmezer², Paulus Stefan Rommer², Thomas Berger², Assunta Dal-Bianco², and Günther Grabner^1,2,4

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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