Vaanathi Sundaresan1, Mark Jenkinson1, Giovanna Zamboni2, and Ludovica Griffanti1
1FMRIB, Wellcome Centre for Integrative Neuroimaging (WIN), Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom, 2Centre for prevention of Stroke and Dementia, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
We propose a triplanar U-Net ensemble network (TrUE-Net) model for detecting white matter hyperintensities (WMHs) on structural brain MR images. The network uses a combination of loss functions based on the anatomical distribution of WMHs. The model takes T1-weighted and FLAIR images as input channels. The U-Nets in three planes (axial, coronal, sagittal) provides three 2D probability maps, which are combined by averaging across planes to obtain the final probability map. When evaluated on three different cohorts from the MICCAI WMH segmentation challenge dataset, TrUE-Net provided better average performance (Dice=0.78, voxel-wise TPR=0.75), when compared to FSL-BIANCA (Dice=0.69, voxel-wise TPR=0.74).
