A voxel-spacing agnostic nnU-Net, trained with sparse annotations, iterative label cleaning, and 12,740 pseudo-labelled images, reaches DSC 85.6% for perivascular space segmentation and is extended to midbrain, hippocampus, FLAIR, and T2w inputs.
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A Comprehensive Framework for Automated Segmentation of Perivascular Spaces in Brain MRI with the nnU-Net
A voxel-spacing agnostic nnU-Net, trained with sparse annotations, iterative label cleaning, and 12,740 pseudo-labelled images, reaches DSC 85.6% for perivascular space segmentation and is extended to midbrain, hippocampus, FLAIR, and T2w inputs.