Multiple sclerosis cortical lesion detection with deep learning at ultra-high-field MRI.

ABSTRACT: Manually segmenting multiple sclerosis (MS) cortical lesions (CLs) is extremely time consuming, and past studies have shown only moderate inter-rater reliability. To accelerate this task, we developed a deep-learning-based framework (CLAIMS: Cortical Lesion AI-Based Assessment in Multiple Sclerosis) for the automated detection and classification of MS CLs with 7 T MRI. Two 7 T datasets, acquired at different sites, were considered. The first consisted of 60 scans that include 0.5 mm isotropic MP2RAGE acquired four times (MP2RAGE×4), 0.7 mm MP2RAGE, 0.5 mm T₂ *-weighted GRE, and 0.5 mm T₂ *-weighted EPI. The second dataset consisted of 20 scans including only 0.75 × 0.75 × 0.9 mm³ MP2RAGE. CLAIMS was first evaluated using sixfold cross-validation with single

SUBMITTER: La Rosa F 

PROVIDER: S-EPMC9539569 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature