Medizinische Universität Graz Austria/Österreich - Forschungsportal - Medical University of Graz

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SHR Neuro Krebs Kardio Lipid

Storelli, L; Pagani, E; Rocca, MA; Horsfield, MA; Gallo, A; Bisecco, A; Battaglini, M; De Stefano, N; Vrenken, H; Thomas, DL; Mancini, L; Ropele, S; Enzinger, C; Preziosa, P; Filippi, M.
A Semiautomatic Method for Multiple Sclerosis Lesion Segmentation on Dual-Echo MR Imaging: Application in a Multicenter Context.
AJNR Am J Neuroradiol. 2016; 37(11):2043-2049 [OPEN ACCESS]
Web of Science PubMed FullText FullText_MUG

 

Autor/innen der Med Uni Graz:
Enzinger Christian
Ropele Stefan
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Abstract:
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. 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. 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). 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. © 2016 by American Journal of Neuroradiology.

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