Abstract #4104

A SEMI-AUTOMATIC METHOD TO SEGMENT MULTIPLE SCLEROSIS LESIONS ON DUAL-ECHO MAGNETIC RESONANCE IMAGES

Loredana Storelli¹, Elisabetta Pagani¹, Maria Assunta Rocca^1,2, Paolo Preziosa^1,2, Antonio Gallo^3,4, Gioacchino Tedeschi^3,4, Maria Laura Stromillo⁵, Nicola De Stefano⁵, Hugo Vrenken⁶, David Thomas⁷, Laura Mancini⁷, Christian Enzinger⁸, Franz Fazekas⁸, and Massimo Filippi^1,2

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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