A fully convolutional neural network for new T2-w lesion detection in multiple sclerosis
dc.contributor.author
dc.date.accessioned
2020-03-16T15:46:57Z
dc.date.available
2020-03-16T15:46:57Z
dc.date.issued
2020-01
dc.identifier.issn
2213-1582
dc.identifier.uri
dc.description.abstract
Longitudinal magnetic resonance imaging (MRI) has an important role in multiple sclerosis (MS) diagnosis and follow-up. Specifically, the presence of new T2-w lesions on brain MR scans is considered a predictive biomarker for the disease. In this study, we propose a fully convolutional neural network (FCNN) to detect new T2-w lesions in longitudinal brain MR images.
Methods: One year apart, multichannel brain MR scans (T1-w, T2-w, PD-w, and FLAIR) were obtained for 60 patients, 36 of them with new T2-w lesions. Modalities from both temporal points were preprocessed and linearly coregistered. Afterwards, an FCNN, whose inputs were from the baseline and follow-up images, was trained to detect new MS lesions. The first part of the network consisted of U-Net blocks that learned the deformation fields (DFs) and nonlinearly registered the baseline image to the follow-up image for each input modality. The learned DFs together with the baseline and follow-up images were then fed to the second part, another U-Net that performed the final detection and segmentation of new T2-w lesions. The model was trained end-to-end, simultaneously learning both the DFs and the new T2-w lesions, using a combined loss function. We evaluated the performance of the model following a leave-one-out cross-validation scheme.
Results: In terms of the detection of new lesions, we obtained a mean Dice similarity coefficient of 0.83 with a true positive rate of 83.09% and a false positive detection rate of 9.36%. In terms of segmentation, we obtained a mean Dice similarity coefficient of 0.55. The performance of our model was significantly better compared to the state-of-the-art methods (p < 0.05).
Conclusions: Our proposal shows the benefits of combining a learning-based registration network with a segmentation network. Compared to other methods, the proposed model decreases the number of false positives. During testing, the proposed model operates faster than the other two state-of-the-art methods based on the DF obtained by Demons
dc.description.sponsorship
This work has been supported by Retos de Investigación TIN2015-73563-JIN and DPI2017-86696-R from the Ministerio de Ciencia y Tecnología
dc.format.mimetype
application/pdf
dc.language.iso
eng
dc.publisher
Elsevier
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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.2019.102149
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NeuroImage: Clinical, 2020, vol. 25, art. núm. 102149
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Articles publicats (D-ATC)
dc.rights
Attribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.uri
dc.subject
dc.title
A fully convolutional neural network for new T2-w lesion detection 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
031166
dc.contributor.funder
dc.type.peerreviewed
peer-reviewed
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dc.relation.ProjectAcronym