PT  - JOURNAL ARTICLE
AU  - Storelli, L.
AU  - Pagani, E.
AU  - Rocca, M.A.
AU  - Horsfield, M.A.
AU  - Gallo, A.
AU  - Bisecco, A.
AU  - Battaglini, M.
AU  - De Stefano, N.
AU  - Vrenken, H.
AU  - Thomas, D.L.
AU  - Mancini, L.
AU  - Ropele, S.
AU  - Enzinger, C.
AU  - Preziosa, P.
AU  - Filippi, M.
TI  - A Semiautomatic Method for Multiple Sclerosis Lesion Segmentation on Dual-Echo MR Imaging: Application in a Multicenter Context
AID  - 10.3174/ajnr.A4874
DP  - 2016 Nov 01
TA  - American Journal of Neuroradiology
PG  - 2043--2049
VI  - 37
IP  - 11
4099  - http://www.ajnr.org/content/37/11/2043.short
4100  - http://www.ajnr.org/content/37/11/2043.full
SO  - Am. J. Neuroradiol.2016 Nov 01; 37
AB  - BACKGROUND AND PURPOSE: The automatic segmentation of MS lesions could reduce time required for image processing together with inter- and intraoperator variability for research and clinical trials. A multicenter validation of a proposed semiautomatic method for hyperintense MS lesion segmentation on dual-echo MR imaging is presented.MATERIALS AND METHODS: The classification technique used is based on a region-growing approach starting from manual lesion identification by an expert observer with a final segmentation-refinement step. The method was validated in a cohort of 52 patients with relapsing-remitting MS, with dual-echo images acquired in 6 different European centers.RESULTS: We found a mathematic expression that made the optimization of the method independent of the need for a training dataset. The automatic segmentation was in good agreement with the manual segmentation (dice similarity coefficient = 0.62 and root mean square error = 2 mL). Assessment of the segmentation errors showed no significant differences in algorithm performance between the different MR scanner manufacturers (P > .05).CONCLUSIONS: The method proved to be robust, and no center-specific training of the algorithm was required, offering the possibility for application in a clinical setting. Adoption of the method should lead to improved reliability and less operator time required for image analysis in research and clinical trials in MS.DEdual-echoPDproton density