Quantifying brain tissue volume in multiple sclerosis with automated lesion segmentation and filling
dc.contributor.author
dc.date.accessioned
2016-03-04T12:23:46Z
dc.date.available
2016-03-04T12:23:46Z
dc.date.issued
2015
dc.identifier.issn
2213-1582
dc.identifier.uri
dc.description.abstract
Lesion filling has been successfully applied to reduce the effect of hypo-intense T1-wMultiple Sclerosis (MS) lesions on automatic brain tissue segmentation. However, a study of fully automated pipelines incorporating lesion segmentation and lesion filling on tissue volume analysis has not yet been performed. Here,we analyzed the % of error introduced by automating the lesion segmentation and filling processes in the tissue segmentation of 70 clinically isolated syndrome patient images. First of all, images were processed using the LST and SLS toolkits with different pipeline combinations that differed in either automated ormanual lesion segmentation, and lesion filling or masking out lesions. Then, images processed following each of the pipelines were segmented into gray matter (GM) andwhite matter (WM) using SPM8, and compared with the same images where expert lesion annotationswere filled before segmentation. Our results showed that fully automated lesion segmentation and filling pipelines reduced significantly the % of error inGMandWMvolumeon images ofMS patients, and performed similarly to the images where expert lesion annotations were masked before segmentation. In all the pipelines, the amount of misclassified lesion voxels was the main cause in the observed error inGMandWMvolume. However, the % of error was significantly lower when automatically estimated lesionswere filled and not masked before segmentation. These results are relevant and suggest that LST and SLS toolboxes allow the performance of accurate brain tissue volume measurements without any kind of manual intervention, which can be convenient not only in terms of time and economic costs, but also to avoid the inherent intra/inter variability between manual annotations
dc.description.sponsorship
S. Valverde holds a FI-DGR2013 grant from the Generalitat de Catalunya. E. Roura holds a BR-UdG2013 grant. This work has been partially supported by “La Fundació la Marató de TV3” and by Retos de Investigación TIN2014-55710-R
dc.format.mimetype
application/pdf
dc.language.iso
eng
dc.publisher
Elsevier
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/10.1016/j.nicl.2015.10.012
dc.relation.ispartof
NeuroImage: Clinical, 2015, vol. 9, p. 640-647
dc.relation.ispartofseries
Articles publicats (D-ATC)
dc.rights
Attribution-NonCommercial-NoDerivs 3.0 Spain
dc.rights.uri
dc.subject
dc.title
Quantifying brain tissue volume in multiple sclerosis with automated lesion segmentation and filling
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
dc.identifier.idgrec
023942
dc.contributor.funder
dc.relation.ProjectAcronym