Nicolas Guizard

TL;DR: A new robust method dedicated to produce consistent and accurate brain extraction based on nonlocal segmentation embedded in a multi-resolution framework, which provides results comparable to a recent label fusion approach, while being 40 times faster and requiring a much smaller library of priors.

TL;DR: A deep learning image segmentation framework that is extremely robust to missing imaging modalities, which learns, for each modality, an embedding of the input image into a single latent vector space for which arithmetic operations are well defined.

TL;DR: In this article, the authors collected longitudinal magnetic resonance imaging (MRI) data throughout a 9-month mental training intervention from a large sample of adults between 20 and 55 years of age to investigate whether the targeted mental training of different cognitive and social skills can induce specific changes in brain morphology.

TL;DR: The results confirm that both steps of the longitudinal framework reduce variability and improve accuracy in comparison with the cross-sectional framework, with longitudinal classification yielding the greatest impact.