Automated localization of periventricular and subcortical white matter lesions

It is still unclear whether periventricular and subcortical white matter lesions (WMLs) differ in etiology or clinical consequences. Studies addressing this issue would benefit from automated segmentation and localization of WMLs. Several papers have been published on WML segmentation in MR images. Automated localization however, has not been investigated as much. This work presents and evaluates a novel method to label segmented WMLs as periventricular and subcortical. The proposed technique combines tissue classification and registration-based segmentation to outline the ventricles in MRI brain data. The segmented lesions can then be labeled into periventricular W7MLs and subcortical WMLs by applying region growing and morphological operations. The technique was tested on scans of 20 elderly subjects in which neuro-anatomy experts manually segmented WMLs. Localization accuracy was evaluated by comparing the results of the automated method with a manual localization. Similarity indices and volumetric intraclass correlations between the automated and the manual localization were 0.89 and 0.95 for periventricular WMLs and 0.64 and 0.89 for subcortical WMLs, respectively. We conclude that this automated method for WML localization performs well to excellent in comparison to the gold standard.