TY - JOUR T1 - A Semiautomatic Method for Multiple Sclerosis Lesion Segmentation on Dual-Echo MR Imaging: Application in a Multicenter Context JF - American Journal of Neuroradiology JO - Am. J. Neuroradiol. SP - 2043 LP - 2049 DO - 10.3174/ajnr.A4874 VL - 37 IS - 11 AU - L. Storelli AU - E. Pagani AU - M.A. Rocca AU - M.A. Horsfield AU - A. Gallo AU - A. Bisecco AU - M. Battaglini AU - N. De Stefano AU - H. Vrenken AU - D.L. Thomas AU - L. Mancini AU - S. Ropele AU - C. Enzinger AU - P. Preziosa AU - M. Filippi Y1 - 2016/11/01 UR - http://www.ajnr.org/content/37/11/2043.abstract N2 - 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 ER -