Development and validation of morphological segmentation of age-related cerebral white matter hyperintensities

Abstract

Accurate automated segmentation of age-related white matter hyperintensity (WMH) is desirable for topological studies and those involving large samples. We assessed the accuracy of a novel automated method for segmentation of WMH on magnetic resonance imaging (MRI) in a randomly selected population-based sample of older people aged >60 years. The method combined morphological segmentation and statistical classifiers. Validation of this method was performed against expert manual segmentation in a sample of 30 scans, and against semi-automated segmentation in 202 scans. Its performance was also compared with those of other known methods derived from simple thresholding or Gaussian mixture modelling.Automated morphological segmentation combined with an adaptive boosting statistical classifier showed substantial agreement with manual segmentation, with an intraclass correlation coefficient (ICC) of 0.90 (95% confidence interval [CI], 0.80–0.95) for WMH volume and median similarity index (SI) of 0.58 (interquartile range [IQR] 0.50–0.65). The method also showed similarly high levels of agreement with semi-automated segmentation, with ICC 0.92 (95% CI 0.89–0.93) and median SI 0.56 (IQR 0.49–0.66). Its best performance was observed for the highest tertile of WMH volume. Threshold-based and Gaussian mixture model-driven automated segmentation generally did not perform well in this study.