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 Montalbán Gairín, 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.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.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.doi http://dx.doi.org/10.1016/j.nicl.2022.103187
• dc.identifier.issn 1053-8119
• dc.identifier.uri http://hdl.handle.net/10230/55972
• 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.accessRights info:eu-repo/semantics/openAccess
• dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.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.type.version info:eu-repo/semantics/publishedVersion