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AJNR Am J Neuroradiol. 2016 Nov;37(11):2043-2049. doi: 10.3174/ajnr.A4874. Epub 2016 Jul 21.

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

Author information

1
From the Neuroimaging Research Unit (L.S., E.P., M.A.R., P.P., M.F.).
2
Institute of Experimental Neurology, Division of Neuroscience, Department of Neurology (M.A.R., P.P., M.F.), San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan, Italy.
3
Xinapse Systems (M.A.H.), Colchester, United Kingdom.
4
MRI Center "SUN-FISM" and Institute of Diagnosis and Care "Hermitage-Capodimonte" (A.G., A.B.).
5
I Division of Neurology, Department of Medical, Surgical, Neurological, Metabolic and Aging Sciences (A.G., A.B.), Second University of Naples, Naples, Italy.
6
Department of Neurological and Behavioral Sciences (M.B., N.D.S.), University of Siena, Italy.
7
Department of Radiology and Nuclear Medicine, MS Centre Amsterdam (H.V.), VU Medical Centre, Amsterdam, the Netherlands.
8
Neuroradiological Academic Unit (D.L.T., L.M.), UCL Institute of Neurology, London, United Kingdom.
9
Department of Neurology (S.R., C.E.).
10
Clinical Division of Neuroradiology, Vascular and Interventional Radiology, Department of Radiology (C.E.), Medical University of Graz, Austria.
11
From the Neuroimaging Research Unit (L.S., E.P., M.A.R., P.P., M.F.) filippi.massimo@hsr.it.

Abstract

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.

PMID:
27444938
DOI:
10.3174/ajnr.A4874
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