A supervised framework with intensity subtraction and deformation field features for the detection of new T2-w lesions in multiple sclerosis
dc.contributor.author
dc.date.accessioned
2019-09-10T10:48:34Z
dc.date.available
2019-09-10T10:48:34Z
dc.date.issued
2018
dc.identifier.issn
2213-1582
dc.identifier.uri
dc.description.abstract
Longitudinal magnetic resonance imaging (MRI) analysis has an important role in multiple sclerosis diagnosis and follow-up. The presence of new T2-w lesions on brain MRI scans is considered a prognostic and predictive biomarker for the disease. In this study, we propose a supervised approach for detecting new T2-w lesions using features from image intensities, subtraction values, and deformation fields (DF).
Methods
One year apart multi-channel brain MRI scans were obtained for 60 patients, 36 of them with new T2-w lesions. Images from both temporal points were preprocessed and co-registered. Afterwards, they were registered using multi-resolution affine registration, allowing their subtraction. In particular, the DFs between both images were computed with the Demons non-rigid registration algorithm. Afterwards, a logistic regression model was trained with features from image intensities, subtraction values, and DF operators. We evaluated the performance of the model following a leave-one-out cross-validation scheme.
Results
In terms of detection, we obtained a mean Dice similarity coefficient of 0.77 with a true-positive rate of 74.30% and a false-positive detection rate of 11.86%. In terms of segmentation, we obtained a mean Dice similarity coefficient of 0.56. The performance of our model was significantly higher than state-of-the-art methods.
Conclusions
The performance of the proposed method shows the benefits of using DF operators as features to train a supervised learning model. Compared to other methods, the proposed model decreases the number of false-positives while increasing the number of true-positives, which is relevant for clinical settings
dc.description.sponsorship
This work has been supported by “La Fundació la Marató de TV3”, by Retos de Investigación TIN2014-55710-R and TIN2015-73563-JIN, and by MPC UdG 2016/022 grant
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/
dc.relation.isformatof
Reproducció digital del document publicat a: https://doi.org/10.1016/j.nicl.2017.11.015
dc.relation.ispartof
NeuroImage: Clinical, 2018, vol. 17, p. 607-615
dc.relation.ispartofseries
Articles publicats (D-ATC)
dc.rights
Attribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.uri
dc.title
A supervised framework with intensity subtraction and deformation field features for the detection of new T2-w lesions in multiple sclerosis
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
027738
dc.contributor.funder
dc.type.peerreviewed
peer-reviewed
dc.relation.ProjectAcronym