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Abstract #2432

Automated assessment of longitudinal White Matter Hyperintensities changes using a novel convolutional neural network in CADASIL

Valentin Demeusy1, Florent Roche1, Fabrice Vincent1, Jean-Pierre Guichard2, Jessica Lebenberg3,4, Eric Jouvent3,5, and Hugues Chabriat3,5
1Imaging Core Lab, Medpace, Lyon, France, 2Department of Neuroradiology, Hôpital Lariboisière, APHP, Paris, France, 3FHU NeuroVasc, INSERM U1141, Paris, France, 4Université de Paris, Paris, France, 5Departement of Neurology, Hôpital Lariboisière, APHP, Paris, France

We propose a novel automatic WMH segmentation method based on a convolutional neural network to study the longitudinal WMH changes among a cohort of 101 CADASIL patients. We demonstrate that this method is able to produce consistent quantitative measures of WMH volume by the strong correlation between the computed baseline WMH volume and the clinically assessed Fazekas score. Our main results show that the progression of WMH is correlated to the baseline volume and that this progression largely vary at individual level although a rapid extension is mainly detected between 40 and 60 years in the whole population.

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