White matter hyperintensities (WMH) are a hallmark of small vessel diseases (SVD). Yet, no automated segmentation method is readily and widely used, especially in patients with extensive WMH where lesions are close to the cerebral cortex. BIANCA (Brain Intensity AbNormality Classification Algorithm) is a new fully automated, supervised method for WMH segmentation. In this study, we optimized and compared BIANCA against a reference method with manual editing in a cohort of patients with extensive WMH. This was achieved in two datasets: a clinical protocol with 90 patients having 2-dimensional FLAIR and an advanced protocol with 66 patients having 3-dimensional FLAIR. We first determined simultaneously which input modalities (FLAIR alone or FLAIR + T1) and which training sets were better compared to the reference. Three strategies for the selection of the threshold that is applied to the probabilistic output of BIANCA were then evaluated: chosen at the group level, based on Fazekas score or determined individually. Accuracy of the segmentation was assessed through measures of spatial agreement and volumetric correspondence with respect to reference segmentation. Based on all our tests, we identified multimodal inputs (FLAIR + T1), mixed WMH load training set and individual threshold selection as the best conditions to automatically segment WMH in our cohort. A median Dice similarity index of 0.80 (0.80) and an intraclass correlation coefficient of 0.97 (0.98) were obtained for the clinical (advanced) protocol. However, Bland-Altman plots identified a difference with the reference method that was linearly related to the total burden of WMH. Our results suggest that BIANCA is a reliable and fast segmentation method to extract masks of WMH in patients with extensive lesions.

Key Points Question Can electroencephalography (EEG), combined with clinical, biological, and magnetic resonance imaging (MRI) analyses, help to better characterize patients with neurologic coronavirus disease 2019 (COVID-19) and diagnose specific COVID-19–related encephalopathy? Findings Neurologic manifestations, biological findings, EEG findings, and brain MRI images were analyzed in a cohort study of 78 adult patients with COVID-19. Nine patients had no identified cause of brain injury, as revealed by biological and MRI findings; their injury was defined as COVID-19–related encephalopathy. Meaning This study suggests that, although neurologic manifestations, EEG findings, and MRI findings may appear heterogeneous and nonspecific, multimodal monitoring may better identify patients with COVID-19–related encephalopathy and guide treatment strategy.