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Research ArticleADULT BRAIN

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

L. Storelli, E. Pagani, M.A. Rocca, M.A. Horsfield, A. Gallo, A. Bisecco, M. Battaglini, N. De Stefano, H. Vrenken, D.L. Thomas, L. Mancini, S. Ropele, C. Enzinger, P. Preziosa and M. Filippi
American Journal of Neuroradiology November 2016, 37 (11) 2043-2049; DOI: https://doi.org/10.3174/ajnr.A4874
L. Storelli
aFrom the Neuroimaging Research Unit (L.S., E.P., M.A.R., P.P., M.F.)
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E. Pagani
aFrom the Neuroimaging Research Unit (L.S., E.P., M.A.R., P.P., M.F.)
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M.A. Rocca
aFrom the Neuroimaging Research Unit (L.S., E.P., M.A.R., P.P., M.F.)
bInstitute 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
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M.A. Horsfield
cXinapse Systems (M.A.H.), Colchester, United Kingdom
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A. Gallo
dMRI Center “SUN-FISM” and Institute of Diagnosis and Care “Hermitage-Capodimonte” (A.G., A.B.)
eI Division of Neurology, Department of Medical, Surgical, Neurological, Metabolic and Aging Sciences (A.G., A.B.), Second University of Naples, Naples, Italy
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A. Bisecco
dMRI Center “SUN-FISM” and Institute of Diagnosis and Care “Hermitage-Capodimonte” (A.G., A.B.)
eI Division of Neurology, Department of Medical, Surgical, Neurological, Metabolic and Aging Sciences (A.G., A.B.), Second University of Naples, Naples, Italy
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M. Battaglini
fDepartment of Neurological and Behavioral Sciences (M.B., N.D.S.), University of Siena, Italy
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N. De Stefano
fDepartment of Neurological and Behavioral Sciences (M.B., N.D.S.), University of Siena, Italy
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H. Vrenken
gDepartment of Radiology and Nuclear Medicine, MS Centre Amsterdam (H.V.), VU Medical Centre, Amsterdam, the Netherlands
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D.L. Thomas
hNeuroradiological Academic Unit (D.L.T., L.M.), UCL Institute of Neurology, London, United Kingdom
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L. Mancini
hNeuroradiological Academic Unit (D.L.T., L.M.), UCL Institute of Neurology, London, United Kingdom
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S. Ropele
iDepartment of Neurology (S.R., C.E.)
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C. Enzinger
iDepartment of Neurology (S.R., C.E.)
jClinical Division of Neuroradiology, Vascular and Interventional Radiology, Department of Radiology (C.E.), Medical University of Graz, Austria.
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P. Preziosa
aFrom the Neuroimaging Research Unit (L.S., E.P., M.A.R., P.P., M.F.)
bInstitute 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
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M. Filippi
aFrom the Neuroimaging Research Unit (L.S., E.P., M.A.R., P.P., M.F.)
bInstitute 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
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    Fig 1.

    Threshold functions obtained after the training step for each different scanner manufacturer at the decreasing of the training set sample sizes (ie, number of patients included) as indicated. It is possible to observe that with decreasing sample size, the linear regression functions did not modify their trends.

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    Fig 2.

    Dice similarity coefficient values (top left), mean true-positive fraction/false-positive fraction values (top right), and mean false-negative fraction values (bottom left) are shown for each patient. In the bottom right, a scatterplot to compare manual lesion load with automatic lesion load is shown. The dashed line is the line of identity.

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    Fig 3.

    Example lesion segmentations for 2 patients (rows) from 2 different scanners by the proposed method (red) compared with the expert operator segmentation (blue). The corresponding T2-weighted images are shown in the right column.

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American Journal of Neuroradiology: 37 (11)
American Journal of Neuroradiology
Vol. 37, Issue 11
1 Nov 2016
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L. Storelli, E. Pagani, M.A. Rocca, M.A. Horsfield, A. Gallo, A. Bisecco, M. Battaglini, N. De Stefano, H. Vrenken, D.L. Thomas, L. Mancini, S. Ropele, C. Enzinger, P. Preziosa, M. Filippi
A Semiautomatic Method for Multiple Sclerosis Lesion Segmentation on Dual-Echo MR Imaging: Application in a Multicenter Context
American Journal of Neuroradiology Nov 2016, 37 (11) 2043-2049; DOI: 10.3174/ajnr.A4874

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A Semiautomatic Method for Multiple Sclerosis Lesion Segmentation on Dual-Echo MR Imaging: Application in a Multicenter Context
L. Storelli, E. Pagani, M.A. Rocca, M.A. Horsfield, A. Gallo, A. Bisecco, M. Battaglini, N. De Stefano, H. Vrenken, D.L. Thomas, L. Mancini, S. Ropele, C. Enzinger, P. Preziosa, M. Filippi
American Journal of Neuroradiology Nov 2016, 37 (11) 2043-2049; DOI: 10.3174/ajnr.A4874
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