{ "dc.contributor.author": "Roura Perez, Eloy" , "dc.contributor.author": "Sarbu, Nicolae" , "dc.contributor.author": "Oliver i Malagelada, Arnau" , "dc.contributor.author": "Valverde Valverde, Sergi" , "dc.contributor.author": "González Villà, Sandra" , "dc.contributor.author": "Cervera, Ricard" , "dc.contributor.author": "Bargalló, Núria" , "dc.contributor.author": "Lladó Bardera, Xavier" , "dc.date.accessioned": "2016-11-21T13:36:05Z" , "dc.date.available": "2016-11-21T13:36:05Z" , "dc.date.issued": "2016-08-12" , "dc.identifier.uri": "http://hdl.handle.net/10256/13182" , "dc.description.abstract": "Brain magnetic resonance imaging provides detailed information which can be used to detectand segment white matter lesions (WML). In this work we propose an approach to automatically segment WML in Lupus patients by using T1 wandfluid-attenuated inversion recovery (FLAIR) images. Lupus WML appear as small fo calabnormal tissue observed as hyperintensities in the FLAIR images. The quantification of these WML is a key factor for the stratification of lupus patients and therefore both lesion detection and segmentation play an important role. In our approach, the T1 wimage is first used to classify the three maint issues of the brain , white matter (WM), graymatter (GM) ,and cerebro spinal fluid (CSF), while the FLAIR image is then used to detect focal WM La soutliers of its GMintensity distribution. Aset of post-processing steps based on lesionsize, tissue neighborhood, and location are used to refine the lesion candidates. The propos alise valuated on 20 patients, presenting qualitative, and quantitative results in terms of precision and sensitivity of lesion detection [True Positive Rate (62%) and Positive Prediction Value (80%), respectively] as well as segmentation accuracy [Dice Similarity Coefficient (72%)]. Obtained results illustrate the validity of the aproach to automatically detectand segment lupus lesions. Besides,our approach is publicly available as a SPM8/12 tool box extension with a simple parameter configuration" , "dc.description.sponsorship": "ER holds a BR-UdG2013 Ph.D. grant. SV holds a FI-DGR2013 Ph.D.grant. This work has been supported by“L aFundació la Marató de TV3”,by Retos de Investigación TIN2014-55710-R, and by MP CUdG2016/022grant" , "dc.format.mimetype": "application/pdf" , "dc.language.iso": "eng" , "dc.publisher": "Frontiers Media" , "dc.relation": "info:eu-repo/grantAgreement/MINECO//TIN2014-55710-R/ES/HERRAMIENTAS DE NEUROIMAGEN PARA MEJORAR EL DIAGNOSIS Y EL SEGUIMIENTO CLINICO DE LOS PACIENTES CON ESCLEROSIS MULTIPLE/" , "dc.relation.isformatof": "Reproducció digital del document publicat a: http://dx.doi.org/10.3389/fninf.2016.00033" , "dc.relation.ispartof": "Frontiers in Neuroinformatics, 2016, vol. 10, art.33" , "dc.relation.ispartofseries": "Articles publicats (D-ATC)" , "dc.rights": "Attribution 3.0 Spain" , "dc.rights.uri": "http://creativecommons.org/licenses/by/3.0/es/" , "dc.subject": "Imatgeria per ressonància magnètica" , "dc.subject": "Magnetic resonance imaging" , "dc.subject": "Malalties cerebrovasculars -- Imatges per ressonància magnètica" , "dc.subject": "Cerebrovascular disease -- Magnetic resonance imaging" , "dc.subject": "Imatges -- Segmentació" , "dc.subject": "Imaging segmentation" , "dc.subject": "Imatgeria mèdica" , "dc.subject": "Imaging systems in medicine" , "dc.title": "Automated Detection of Lupus White Matter Lesions in MRI" , "dc.type": "info:eu-repo/semantics/article" , "dc.rights.accessRights": "info:eu-repo/semantics/openAccess" , "dc.embargo.terms": "Cap" , "dc.type.version": "info:eu-repo/semantics/publishedVersion" , "dc.identifier.doi": "http://dx.doi.org/10.3389/fninf.2016.00033" , "dc.identifier.idgrec": "025433" , "dc.contributor.funder": "Ministerio de Economía y Competitividad (Espanya)" , "dc.relation.ProjectAcronym": "HERRAMIENTAS DE NEUROIMAGEN PARA MEJORAR EL DIAGNOSIS Y EL SEGUIMIENTO CLINICO DE LOS PACIENTES CON ESCLEROSIS MULTIPLE" , "dc.identifier.eissn": "1662-5196" }