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 |