WHITE-Net : White matter HyperIntensities Tissue Extraction using deep learning Network

Given the high prevalence of aging-associated cerebral small vessel disease in the general population, accurate detection of the related white matter hyperintensities (WMH) in large-scale magnetic resonance imaging (MRI) studies is of critical importance. The performance of currently available semi-automated and automated methods for WMH classification is hampered by their inherent dependence on MRI contrast parameters and long computational processing time. We sought to improve the accuracy and computational cost of automated WMH detection by creating a whole-brain deep learning-based framework: WHITE-Net. We use a 3D ResUNet architecture trained on manually segmented WMHs from fluid-attenuated inversion recovery MRI (n=141) and test its accuracy in a large-scale dataset (n=192). We demonstrate a good generalizability across WMH lesion loads, different MRI scanner vendors, field strengths, imaging protocols, and MR contrasts. The comparison to existing WMH segmentation tools shows a similar to superior accuracy performance at significantly lower computational cost. WHITE-Net tool performance makes it well-suited for application to large-scale MRI datasets, enabling the study of the aging brain while offering the advantage of detecting early or subtle WMH changes often missed by other methods.