2026-04-18T00:29:25Zhttps://recercat.cat/oai/requestoai:recercat.cat:11351/86732024-12-13T10:27:04Zcom_2072_378070com_2072_378040com_2072_378071col_2072_378092col_2072_378097
RECERCAT
author
Marti Juan, Gerard
author
Frías, Marcos
author
Garcia Vidal, Aran
author
Vidal Jordana, Angela
author
Alberich Jordà, Manel
author
Calderon Miranda, Willem Guillermo
author
Montalban Gairín, Xavier
author
Rovira Cañellas, Alex
author
Pareto Onghena, Deborah
author
Sastre Garriga, Jaume
2023-11-08T13:41:22Z
2023-11-08T13:41:22Z
2022-12-16T08:37:27Z2022-12-16T08:37:27Z2022
http://hdl.handle.net/11351/8673
Deep learning; Multiple sclerosis, Optic nerveAprendizaje profundo; Esclerosis múltiple; Nervio ópticoAprenentatge profund; Esclerosi múltiple; Nervi òpticBackground
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.This project was developed as a part of Gerard Martí-Juan ECTRIMS Research Fellowship Program 2021–2022. This study was partially supported by the Projects (PI18/00823, PI19/00950), from the Fondo de Investigación Sanitaria (FIS), Instituto de Salud Carlos III.
Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/ info:eu-repo/semantics/openAccess
Esclerosi múltiple
Neuritis - Imatgeria
Imatgeria per ressonància magnètica
DISEASES::Nervous System Diseases::Autoimmune Diseases of the Nervous System::Demyelinating Autoimmune Diseases, CNS::Multiple Sclerosis
DISEASES::Nervous System Diseases::Cranial Nerve Diseases::Optic Nerve Diseases::Optic Neuritis
Other subheadings::Other subheadings::Other subheadings::/diagnostic imaging
ANALYTICAL, DIAGNOSTIC AND THERAPEUTIC TECHNIQUES, AND EQUIPMENT::Diagnosis::Diagnostic Techniques and Procedures::Diagnostic Imaging::Tomography::Magnetic Resonance Imaging
ENFERMEDADES::enfermedades del sistema nervioso::enfermedades autoinmunitarias del sistema nervioso::enfermedades autoinmunes desmielinizantes del SNC::esclerosis múltiple
ENFERMEDADES::enfermedades del sistema nervioso::enfermedades de los pares craneales::enfermedades del nervio óptico::neuritis óptica
Otros calificadores::Otros calificadores::Otros calificadores::/diagnóstico por imagen
TÉCNICAS Y EQUIPOS ANALÍTICOS, DIAGNÓSTICOS Y TERAPÉUTICOS::diagnóstico::técnicas y procedimientos diagnósticos::diagnóstico por imagen::tomografía::imagen por resonancia magnética
Detection of lesions in the optic nerve with magnetic resonance imaging using a 3D convolutional neural network
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion