NeuroImage. Clinical · 2022
Detection of lesions in the optic nerve with magnetic resonance imaging using a 3D convolutional neural network.
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Martí-Juan Gerard, Frías Marcos, Garcia-Vidal Aran, Vidal-Jordana Angela, Alberich Manel, Calderon Willem, Piella Gemma, Camara Oscar, Montalban Xavier, Sastre-Garriga Jaume, Rovira Àlex, Pareto Deborah
Аннотация
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.
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.