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To automatically segment multiple sclerosis (MS) lesions into three subtypes (i.e., enhancing lesions, T1 “black holes”, T2 hyperintense lesions).
Proton density-, T2- and contrast-enhanced T1-weighted brain images of 12 MR scans were pre-processed through intracranial cavity (IC) extraction, inhomogeneity correction and intensity normalization. Intensity-based statistical
k-nearest neighbor (
k-NN) classification was combined with template-driven segmentation and partial volume artifact correction (TDS
+) for segmentation of MS lesions subtypes and brain tissue compartments. Operator-supervised tissue sampling and parameter calibration were performed on 2 randomly selected scans and were applied automatically to the remaining 10 scans. Results from this three-channel TDS
+ (3ch-TDS
+) were compared to those from a previously validated two-channel TDS
+ (2ch-TDS
+) method. The results of both the 3ch-TDS
+ and 2ch-TDS
+ were also compared to manual segmentation performed by experts.
Intra-class correlation coefficients (ICC) of 3ch-TDS
+ for all three subtypes of lesions were higher (ICC between 0.95 and 0.96) than that of 2ch-TDS
+ for T2 lesions (ICC = 0.82). The 3ch-TDS
+ also identified the three lesion subtypes with high specificity (98.7–99.9%) and accuracy (98.5–99.9%). Sensitivity of 3ch-TDS
+ for T2 lesions was 16% higher than with 2ch-TDS
+. Enhancing lesions were segmented with the best sensitivity (81.9%). “Black holes” were segmented with the least sensitivity (62.3%).
3ch-TDS
+ is a promising method for automated segmentation of MS lesion subtypes.