Abstract
Accurate segmentation of brain white matter hyperintensities (WMHs) is important for prognosis and disease monitoring. To this end,classifiers are often trained – usually,using T1 and FLAIR weighted MR images. Incorporating additional features,derived from diffusion weighted MRI,could improve classification. However,the multitude of diffusion-derived features requires selecting the most adequate. For this,automated
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