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

Abstract

Deep learning; Multiple sclerosis, Optic nerve

Aprendizaje profundo; Esclerosis múltiple; Nervio óptico

Aprenentatge profund; Esclerosi múltiple; Nervi òptic

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.

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.