Automatic segmentation and quantitative analysis of white matter hyperintensities on FLAIR images using trimmed-likelihood estimator

Abstract

Rationale and objectives: Quantitative analysis of white matter hyperintensities (WMHs) on fluid-attenuated inversion recovery (FLAIR) images provides information for disease tracking, therapeutic efficacy assessment, and cognitive science research. This study developed an automatic segmentation method to detect and quantify WMHs on FLAIR images. This study aims to assess the accuracy and reproducibility of the proposed method.

Materials and methods: The FLAIR images of 82 patients (58-84 years) with different WMH burdens were acquired with the same 3T scanner (Intera-achieva SMI-2.1; Philip Medical System, Sixth Affiliated People's Hospital, Shanghai, China). The FLAIR images were preprocessed through brain extraction and intensity inhomogeneity correction. An anatomy atlas built from a set of 20 patients with different WMH burdens (mild, 11 patients; moderate, 6 patients; and severe, 3 patients) was used to estimate a control parameter in the framework of segmentation. The general flow for WMH segmentation included classification of foreground and background regions, detection of abnormally high signals, and WMH refinement. The performance of automatic segmentation was evaluated by a volumetric comparison with manual segmentation on patients with different WMH burdens.

Results: Similarity index values for the accuracy of automatic segmentation compared to manual segmentation were consistently high for patients with different WMH burdens (mild, 0.78 ± 0.08; moderate, 0.83 ± 0.06; severe, 0.84 ± 0.08; and total, 0.80 ± 0.08). Linear regression demonstrated a strong correlation between the WMH volumes measured by the two methods in all patients (r = 0.98, P = .006). Small average differences were detected between the WMH volumes obtained through manual and automatic segmentations (mild, 4.76%; moderate, 6.84%; and severe, 7.59%). The results of Bland-Altman analysis show a system bias of 0.68 cm(3) and a standard deviation of 2.10 cm(3) over the range of 2.58-53.9 cm(3).

Conclusions: The developed method is accurate and efficient in detecting and quantifying differently sized WMHs on FLAIR images. Automatic segmentation is a promising computer-aided diagnostic tool to study WMHs in aging and dementia in basic research and even in clinical trials.

Keywords: Gaussian mixture model; White matter hyperintensities; automatic segmentation; trimmed likelihood estimator.