Automated segmentation of multiple sclerosis lesion subtypes with multichannel MRI.
OBJECTIVE
To automatically segment multiple sclerosis (MS) lesions into three subtypes (i.e., enhancing lesions, T1 "black holes", T2 hyperintense lesions).


METHODS
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.


RESULTS
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%).


CONCLUSIONS
3ch-TDS+ is a promising method for automated segmentation of MS lesion subtypes.
Wu
Ying
Y
Center for Neurological Imaging, Departments of Radiology and Neurology, Brigham and Women's Hospital, Harvard Medical School, 221 Longwood Avenue RF394A, Boston, MA 02115, USA.
Warfield
Simon K
SK
Tan
I Leng
IL
Wells
William M
WM
Meier
Dominik S
DS
van Schijndel
Ronald A
RA
Barkhof
Frederik
F
Guttmann
Charles R G
CR
D000328:Adult; D000465:Algorithms; D016477:Artifacts; D001921:Brain / Q000473:pathology; D002138:Calibration; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008279:Magnetic Resonance Imaging; D008297:Male; D008875:Middle Aged; D009103:Multiple Sclerosis / Q000473:pathology*; D015203:Reproducibility of Results