The added value of diffusion tensor imaging for automated white matter hyperintensity segmentation

Abstract

Automatedwhite matter hyperintensity (WMH) segmentation techniques for brain MRI often employ voxel-wise classifiers, trained on traditional features such as: multi-spectral MR image intensities, spatial location, texture, or shape. Recent studies show that diffusion tensor imaging (DTI) provides a measure for WMH, independent from the commonly used FLAIR images. Hence, we hypothesized that adding features derived from DTI to a voxel-wise classifier for WMH segmentation may have added value and improve segmentation results. A k nearest neighbour (kNN) classifier was implemented and trained on various combinations of features. Manual delineations of WMH were available for 20 subjects. Classifiers trained with diffusion features, such as fractional anisotropy and mean diffusivity, are compared to an equivalent classifier without diffusion features. Evaluation measures are sensitivity and Dice similarity coefficient (SI). Adding diffusion features to a kNN classifier significantly (Student’s t-test, p < 0:0001) improved the quality of the segmentation. Depending on the chosen kNN parameters and features, improvements in sensitivity ranged from 2.4 to 13.5% and in SI from 4.7 to 18.0%. In conclusion, adding diffusion features derived from DTI to a voxel-wise classifier for WMH segmentation significantly improves the quality of the segmentation.