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Details

Autor(en) / Beteiligte
Titel
Automated segmentation of multiple sclerosis lesion subtypes with multichannel MRI
Ist Teil von
  • NeuroImage (Orlando, Fla.), 2006-09, Vol.32 (3), p.1205-1215
Ort / Verlag
United States: Elsevier Inc
Erscheinungsjahr
2006
Quelle
MEDLINE
Beschreibungen/Notizen
  • 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.
Sprache
Englisch
Identifikatoren
ISSN: 1053-8119
eISSN: 1095-9572
DOI: 10.1016/j.neuroimage.2006.04.211
Titel-ID: cdi_proquest_miscellaneous_68785016

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