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Dense, deep learning-based intracranial aneurysm detection on TOF MRI using two-stage regularized U-Net - 25/01/23

Doi : 10.1016/j.neurad.2022.03.005 
Frédéric Claux a, 1 , Maxime Baudouin b, 1, , Clément Bogey b, Aymeric Rouchaud a, b
a Univ. Limoges, CNRS, XLIM, UMR 7252, F-87000 Limoges, France 
b Limoges university hospital, Department of radiology, Limoges, France 

Corresponding author at: Limoges university hospital, Department of radiology, Limoges, France.Limoges university hospitalDepartment of radiologyLimogesFrance

Abstract

Background and purpose

The prevalence of unruptured intracranial aneurysms in the general population is high and aneurysms are usually asymptomatic. Their diagnosis is often fortuitous on MRI and might be difficult and time consuming for the radiologist. The purpose of this study was to develop a deep learning neural network tool for automated segmentation of intracranial arteries and automated detection of intracranial aneurysms from 3D time-of-flight magnetic resonance angiography (TOF-MRA).

Materials and methods

3D TOF-MRA with aneurysms were retrospectively extracted. All were confirmed with angiography. The data were divided into two sets: a training set of 24 examinations and a test set of 25 examinations. Manual annotations of intracranial blood vessels and aneurysms were performed by neuroradiologists. A double convolutional neuronal network based on the U-Net architecture with regularization was used to increase performance despite a small amount of training data. The performance was evaluated for the test set. Subgroup analyses according to size and location of aneurysms were performed.

Results

The average processing time was 15 min. Overall, the sensitivity and the positive predictive value of the proposed algorithm were 78% (21 of 27; 95% CI: 62–94) and 62% (21 of 34; 95%CI: 46–78) respectively, with 0.5 FP/case. Despite gradual improvement in sensitivity regarding aneurysm size, there was no significant difference of sensitivity detection between subgroups of size and location.

Conclusions

This developed tool based on a double CNN with regularization trained with small dataset, enables accurate intracranial arteries segmentation as well as effective aneurysm detection on 3D TOF MRA.

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Graphical abstract




Image, graphical abstract

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Highlights

The prevalence of unruptured intracranial aneurysms in the general population is 3.2%.
Deep convolutional neural networks have improved the development of automatic diagnostic tools.
An innovative method using a cascaded neuronal network (segmentation then detection).
Developed tool will be available for download free of charge at the following URL: aneudetect.

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Keywords : Cerebral aneurysm, Artificial intelligence, Deep learning, Magnetic resonance angiography

Abbreviations : TOF, FP, CNN, ICA, MCA, ACA, PICA, BT, PCA


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Vol 50 - N° 1

P. 9-15 - février 2023 Retour au numéro
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