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Detection of lesions in the optic nerve with magnetic resonance imaging using a 3D convolutional neural network

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dc.contributor.author Martí Juan, Gerard
dc.contributor.author Frías Nestares, Marcos
dc.contributor.author Garcia Vidal, Aran
dc.contributor.author Vidal Jordana, Angela
dc.contributor.author Alberich, Manel
dc.contributor.author Calderon, Willem
dc.contributor.author Piella Fenoy, Gemma
dc.contributor.author Camara, Oscar
dc.contributor.author Montalban, Xavier
dc.contributor.author Sastre-Garriga, Jaume
dc.contributor.author Rovira, Àlex
dc.contributor.author Pareto, Deborah
dc.date.accessioned 2023-03-01T07:23:32Z
dc.date.available 2023-03-01T07:23:32Z
dc.date.issued 2022
dc.identifier.citation Martí-Juan G, Frías M, Garcia-Vidal A, Vidal-Jordana A, Alberich M, Calderon W, Piella G, Camara O, Montalban X, Sastre-Garriga J, Rovira A, Pareto D. Detection of lesions in the optic nerve with magnetic resonance imaging using a 3D convolutional neural network. Neuroimage Clin. 2022;36:103187. DOI: 10.1016/j.nicl.2022.103187
dc.identifier.issn 1053-8119
dc.identifier.uri http://hdl.handle.net/10230/55972
dc.description.abstract Background: Optic neuritis (ON) is one of the first manifestations of multiple sclerosis, a disabling disease with rising prevalence. Detecting optic nerve lesions could be a relevant diagnostic marker in patients with multiple sclerosis. Objectives: We aim to create an automated, interpretable method for optic nerve lesion detection from MRI scans. Materials and Methods: We present a 3D convolutional neural network (CNN) model that learns to detect optic nerve lesions based on T2-weighted fat-saturated MRI scans. We validated our system on two different datasets (N = 107 and 62) and interpreted the behaviour of the model using saliency maps. Results: The model showed good performance (68.11% balanced accuracy) that generalizes to unseen data (64.11%). The developed network focuses its attention to the areas that correspond to lesions in the optic nerve. Conclusions: The method shows robustness and, when using only a single imaging sequence, its performance is not far from diagnosis by trained radiologists with the same constraint. Given its speed and performance, the developed methodology could serve as a first step to develop methods that could be translated into a clinical setting.
dc.format.mimetype application/pdf
dc.language.iso eng
dc.publisher Elsevier
dc.relation.ispartof NeuroImage: Clinical. 2022;36:103187.
dc.relation.isreferencedby https://ars.els-cdn.com/content/image/1-s2.0-S2213158222002522-mmc1.pdf
dc.rights © 2022 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/bync-nd/4.0/).
dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.title Detection of lesions in the optic nerve with magnetic resonance imaging using a 3D convolutional neural network
dc.type info:eu-repo/semantics/article
dc.identifier.doi http://dx.doi.org/10.1016/j.nicl.2022.103187
dc.subject.keyword Optic nerve
dc.subject.keyword Multiple sclerosis
dc.subject.keyword Deep learning
dc.subject.keyword CNN
dc.subject.keyword MRI
dc.subject.keyword Optic neuritis
dc.rights.accessRights info:eu-repo/semantics/openAccess
dc.type.version info:eu-repo/semantics/publishedVersion


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