Brain structure segmentation in the presence of multiple sclerosis lesions
dc.contributor.author
dc.date.accessioned
2019-11-21T12:09:39Z
dc.date.available
2019-11-21T12:09:39Z
dc.date.issued
2019-02-14
dc.identifier.issn
2213-1582
dc.identifier.uri
dc.description.abstract
Intensity-based multi-atlas segmentation strategies have shown to be particularly successful in segmenting brain images of healthy subjects. However, in the same way as most of the methods in the state of the art, their performance tends to be affected by the presence of MRI visible lesions, such as those found in multiple sclerosis (MS) patients. Here, we present an approach to minimize the effect of the abnormal lesion intensities on multi-atlas segmentation. We propose a new voxel/patch correspondence model for intensity-based multi-atlas label fusion strategies that leads to more accurate similarity measures, having a key role in the final brain segmentation. We present the theory of this model and integrate it into two well-known fusion strategies: Non-local Spatial STAPLE (NLSS) and Joint Label Fusion (JLF). The experiments performed show that our proposal improves the segmentation performance of the lesion areas. The results indicate a mean Dice Similarity Coefficient (DSC) improvement of 1.96% for NLSS (3.29% inside and 0.79% around the lesion masks) and, an improvement of 2.06% for JLF (2.31% inside and 1.42% around lesions). Furthermore, we show that, with the proposed strategy, the well-established preprocessing step of lesion filling can be disregarded, obtaining similar or even more accurate segmentation results
dc.description.sponsorship
S. González-Villà holds a UdG-BRGR2015 grant. This work has been
supported by “La Fundació la Marató de TV3”, by Retos de
Investigación TIN2014-55710-R and TIN2015-73563-JIN, by DPI2017-
86696-R and by UdG mobility grant MOB17. This research was supported
by NSF CAREER 1452485, NIH R01-EB017230, and the National
Center for Advancing Translational Sciences, Grant 2 UL1 TR000445-
06. We appreciate the NIH S10 Shared Instrumentation Grant
1S10OD020154-01 (Smith), Vanderbilt IDEAS grant (Holly-
Bockelmann, Walker, Meliler, Palmeri, Weller), and ACCRE's Big Data
TIPs grant from Vanderbilt University
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/
info:eu-repo/grantAgreement/MINECO//TIN2015-73563-JIN/ES/SEGMENTACION AUTOMATICA DE LAS ESTRUCTURAS CEREBRALES PARA SU USO COMO BIOMARCADORES DE IMAGEN/
info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/DPI2017-86696-R/ES/MODELOS PREDICTIVOS PARA LA ESCLEROSIS MULTIPE USANDO BIOMARCADORES DE RESONANCIA MAGNETICA DEL CEREBRO/
dc.relation.isformatof
Reproducció digital del document publicat a: https://doi.org/10.1016/j.nicl.2019.101709
dc.relation.ispartof
NeuroImage: Clinical, 2019, vol. 22, p.101709
dc.relation.ispartofseries
Articles publicats (D-ATC)
dc.rights
Attribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.uri
dc.subject
dc.title
Brain structure segmentation in the presence of multiple sclerosis lesions
dc.type
info:eu-repo/semantics/article
dc.rights.accessRights
info:eu-repo/semantics/openAccess
dc.type.version
info:eu-repo/semantics/publishedVersion
dc.identifier.doi
dc.identifier.idgrec
029726
dc.contributor.funder
dc.type.peerreviewed
peer-reviewed
dc.relation.FundingProgramme
dc.relation.ProjectAcronym