AUTHORS: Huelnhagen T, Al Louzi O, Fartaria MJ, Daboul L, Maggi P, Granziera C, Bach Cuadra M, Richiardi J, Reich DS, Kober T, Sati P O

Proceedings of the 29th ISMRM & SMRT Annual Meeting, : , Online, May 2021

ABSTRACT

The fraction of white matter lesions exhibiting the central vein sign (CVS) has shown promise as a biomarker in the diagnosis of multiple sclerosis. As manual CVS assessment is not clinically feasible, automated solutions have been proposed to perform this task. A deep-learning-based method called “CVSnet” demonstrated effective and accurate discrimination of MS from its mimics but required manual pre-selection. This work extends CVSnet to allow fully automated CVS assessment without manual interaction. High-quality, expert-reviewed segmentations of almost 6300 lesions were used for training and testing. The proposed method achieved accuracies between 75% and 80% in an unseen testing set.

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