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Abstract #1986

Automated Detection of Central Vessel Sign in Multiple Sclerosis using a 3D Deep Convolutional Neural Network

Richard Watts1

1Radiology, Larner College of Medicine, University of Vermont, Burlington, VT, United States

A 3D deep convolutional neural network (dCNN) was trained to differentiate MS from non-MS lesions based on the orientation and location of a central vein ('central vein sign') relative to the lesion. Excellent performance was achieved using simulated FLAIR and T2*-weighted imaging, with realistic noise levels. The dCNN may be capable of identifying other discriminatory features from multimodal human imaging data.

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