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.
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.
Authors: Aaron Carass; Snehashis Roy; Amod Jog; Jennifer L Cuzzocreo; Elizabeth Magrath; Adrian Gherman; Julia Button; James Nguyen; Ferran Prados; Carole H Sudre; Manuel Jorge Cardoso; Niamh Cawley; Olga Ciccarelli; Claudia A M Wheeler-Kingshott; Sébastien Ourselin; Laurence Catanese; Hrishikesh Deshpande; Pierre Maurel; Olivier Commowick; Christian Barillot; Xavier Tomas-Fernandez; Simon K Warfield; Suthirth Vaidya; Abhijith Chunduru; Ramanathan Muthuganapathy; Ganapathy Krishnamurthi; Andrew Jesson; Tal Arbel; Oskar Maier; Heinz Handels; Leonardo O Iheme; Devrim Unay; Saurabh Jain; Diana M Sima; Dirk Smeets; Mohsen Ghafoorian; Bram Platel; Ariel Birenbaum; Hayit Greenspan; Pierre-Louis Bazin; Peter A Calabresi; Ciprian M Crainiceanu; Lotta M Ellingsen; Daniel S Reich; Jerry L Prince; Dzung L Pham Journal: Neuroimage Date: 2017-01-11 Impact factor: 6.556
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Authors: Navid Shiee; Pierre-Louis Bazin; Arzu Ozturk; Daniel S Reich; Peter A Calabresi; Dzung L Pham Journal: Neuroimage Date: 2009-09-17 Impact factor: 6.556