A Semiautomatic Method for Multiple Sclerosis Lesion Segmentation on Dual-Echo MR Imaging: Application in a Multicenter Context

作者：Storelli L; Pagani E; Rocca M A; Horsfield M A; Gallo A; Bisecco A; Battaglini M; De Stefano N; Vrenken H; Thomas D L; Mancini L; Ropele S; Enzinger C; Preziosa P; Filippi M^*

来源：American Journal of Neuroradiology, 2016, 37(11): 2043-2049.

DOI：10.3174/ajnr.A4874

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.

• 出版日期2016-11