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Abstract #4104


Loredana Storelli1, Elisabetta Pagani1, Maria Assunta Rocca1,2, Paolo Preziosa1,2, Antonio Gallo3,4, Gioacchino Tedeschi3,4, Maria Laura Stromillo5, Nicola De Stefano5, Hugo Vrenken6, David Thomas7, Laura Mancini7, Christian Enzinger8, Franz Fazekas8, and Massimo Filippi1,2

1Neuroimaging Research Unit, San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan, Italy, 2Department of Neurology, San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan, Italy, 3MRI Center “SUN-FISM”, Second University of Naples and Institute of Diagnosis and Care “Hermitage-Capodimonte, Naples, Italy, 4I Division of Neurology, Department of Medical, Surgical, Neurological, Metabolic and Aging Sciences, Second University of Naples, Naples, Italy, 5Department of Neurological and Behavioral Sciences, University of Siena, Siena, Italy, 6Department of Radiology and Nuclear Medicine, MS Centre Amsterdam, VU University Medical Centre, Amsterdam, Netherlands, 7NMR Research Unit, Queen Square MS Centre, UCL Institute of Neurology, London, United Kingdom, 8Department of Neurology, Medical University of Graz, Graz, Austria

Aim of the study was to develop a semi-automatic method for the segmentation of hyperintense multiple sclerosis (MS) lesions on dual-echo (DE) PD/T2-weighted scans. DE MRI scans were obtained from 6 different European centers from 52 MS patients with a mean lesion load of 10.3 (± 11.9) ml. The method was based on a region growing approach initialized by manual identification of lesions and a priori information. The segmentation results with the new method showed high accordance with the ground truth and a low misclassification of lesion voxels. Furthermore, operator time required for lesion segmentation was drastically reduced.

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