TY - JOUR
T1 - Automated segmentation of multiple sclerosis lesion subtypes with multichannel MRI
AU - Wu, Ying
AU - Warfield, Simon K.
AU - Tan, I. Leng
AU - Wells III, William M.
AU - Meier, Dominik S.
AU - van Schijndel, Ronald A.
AU - Barkhof, Frederik
AU - Guttmann, Charles R. G.
PY - 2006
Y1 - 2006
N2 - Purpose.: To automatically segment multiple sclerosis (MS) lesions into three subtypes (i.e., enhancing lesions, T1 "black holes", T2 hyperintense lesions). Materials and 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%). Conclusion.: 3ch-TDS+ is a promising method for automated segmentation of MS lesion subtypes. © 2006 Elsevier Inc. All rights reserved.
AB - Purpose.: To automatically segment multiple sclerosis (MS) lesions into three subtypes (i.e., enhancing lesions, T1 "black holes", T2 hyperintense lesions). Materials and 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%). Conclusion.: 3ch-TDS+ is a promising method for automated segmentation of MS lesion subtypes. © 2006 Elsevier Inc. All rights reserved.
UR - https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=33748062327&origin=inward
UR - https://www.ncbi.nlm.nih.gov/pubmed/16797188
U2 - https://doi.org/10.1016/j.neuroimage.2006.04.211
DO - https://doi.org/10.1016/j.neuroimage.2006.04.211
M3 - Article
C2 - 16797188
SN - 1053-8119
VL - 32
SP - 1205
EP - 1215
JO - NEUROIMAGE
JF - NEUROIMAGE
IS - 3
ER -