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. 2021 Dec 27;22(1):167.
doi: 10.3390/s22010167.

Early Diagnosis of Multiple Sclerosis Using Swept-Source Optical Coherence Tomography and Convolutional Neural Networks Trained with Data Augmentation

Almudena López-Dorado  1 , Miguel Ortiz  2 , María Satue  3 , María J Rodrigo  3 , Rafael Barea  1 , Eva M Sánchez-Morla  4   5   6 , Carlo Cavaliere  1 , José M Rodríguez-Ascariz  1 , Elvira Orduna-Hospital  3 , Luciano Boquete  1 , Elena Garcia-Martin  3
Affiliations

Early Diagnosis of Multiple Sclerosis Using Swept-Source Optical Coherence Tomography and Convolutional Neural Networks Trained with Data Augmentation

Almudena López-Dorado et al. Sensors (Basel). .

Abstract

Background: The aim of this paper is to implement a system to facilitate the diagnosis of multiple sclerosis (MS) in its initial stages. It does so using a convolutional neural network (CNN) to classify images captured with swept-source optical coherence tomography (SS-OCT).

Methods: SS-OCT images from 48 control subjects and 48 recently diagnosed MS patients have been used. These images show the thicknesses (45 × 60 points) of the following structures: complete retina, retinal nerve fiber layer, two ganglion cell layers (GCL+, GCL++) and choroid. The Cohen distance is used to identify the structures and the regions within them with greatest discriminant capacity. The original database of OCT images is augmented by a deep convolutional generative adversarial network to expand the CNN's training set.

Results: The retinal structures with greatest discriminant capacity are the GCL++ (44.99% of image points), complete retina (26.71%) and GCL+ (22.93%). Thresholding these images and using them as inputs to a CNN comprising two convolution modules and one classification module obtains sensitivity = specificity = 1.0.

Conclusions: Feature pre-selection and the use of a convolutional neural network may be a promising, nonharmful, low-cost, easy-to-perform and effective means of assisting the early diagnosis of MS based on SS-OCT thickness data.

Keywords: convolutional neural network; generative adversarial network; multiple sclerosis; optical coherence tomography.

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Conflict of interest statement

The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

Figures

Figure 1
Figure 1
(a) Retinal layer measurements analyzed: RNFL, GCL+, GCL++, complete retina and choroid; (b) OCT scanning source slice image of a normal eye showing, in green, the boundaries of the layers into which the software segments the neuroretina image and the representation of the complexes measured; (c) representation of delimitation of the four retinal layers determined by the segmentation software of Triton OCT (optical coherence tomography) in a patient with multiple sclerosis and in a control subject: GCL+ (ganglion cell layer +: between the boundaries of the retinal nerve fiber layer and the inner nuclear layer, therefore including the GCL and the inner plexiform layer), GCL++ (between the boundaries of the inner limiting membrane and the inner nuclear layer, therefore including the retinal nerve fiber layer and the GCL+), RNFL (retinal nerve fiber layer: between the boundaries of the inner limiting membrane and the GCL) and CHOROID (from Bruch’s membrane to the choroidal-scleral interface).
Figure 1
Figure 1
(a) Retinal layer measurements analyzed: RNFL, GCL+, GCL++, complete retina and choroid; (b) OCT scanning source slice image of a normal eye showing, in green, the boundaries of the layers into which the software segments the neuroretina image and the representation of the complexes measured; (c) representation of delimitation of the four retinal layers determined by the segmentation software of Triton OCT (optical coherence tomography) in a patient with multiple sclerosis and in a control subject: GCL+ (ganglion cell layer +: between the boundaries of the retinal nerve fiber layer and the inner nuclear layer, therefore including the GCL and the inner plexiform layer), GCL++ (between the boundaries of the inner limiting membrane and the inner nuclear layer, therefore including the retinal nerve fiber layer and the GCL+), RNFL (retinal nerve fiber layer: between the boundaries of the inner limiting membrane and the GCL) and CHOROID (from Bruch’s membrane to the choroidal-scleral interface).
Figure 2
Figure 2
3D images of the 5 structures obtained with OCT in real subjects; mean value in all control subjects (left) and mean value in MS patients (right). (a) complete retina; (b) RNFL; (c) GCL+; (d) GCL++; (e) choroid.
Figure 2
Figure 2
3D images of the 5 structures obtained with OCT in real subjects; mean value in all control subjects (left) and mean value in MS patients (right). (a) complete retina; (b) RNFL; (c) GCL+; (d) GCL++; (e) choroid.
Figure 3
Figure 3
CNN architecture implemented. C1, C2: convolutional submodules. FCL: fully connected layer. CL: classification layer.
Figure 4
Figure 4
GAN framework workflow.
Figure 5
Figure 5
Generator and discriminator architecture. NF: number of filters; Fs = filter dimensions.
Figure 6
Figure 6
Processed OCT images of real subjects. Left: Cohen’s d value for the various structures. Right: the best regions, selected with a threshold of dTH = 1.02 (identical for all layers), are shown in yellow. (a) Complete retina; (b) RNFL; (c) GCL+; (d) GCL++; (e) choroid.
Figure 6
Figure 6
Processed OCT images of real subjects. Left: Cohen’s d value for the various structures. Right: the best regions, selected with a threshold of dTH = 1.02 (identical for all layers), are shown in yellow. (a) Complete retina; (b) RNFL; (c) GCL+; (d) GCL++; (e) choroid.
Figure 7
Figure 7
Generator and discriminator learning curve loss over time.
Figure 8
Figure 8
3D images of the 3 structures synthesized with DCGAN; mean value in all control subjects (left) and mean value in MS patients (right). (a,b) Complete retina; (c,d) GCL+ layer; (e,f) GCL++ layer.
Figure 8
Figure 8
3D images of the 3 structures synthesized with DCGAN; mean value in all control subjects (left) and mean value in MS patients (right). (a,b) Complete retina; (c,d) GCL+ layer; (e,f) GCL++ layer.
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