Automated detection of a nonperfusion area caused by retinal vein occlusion in optical coherence tomography angiography images using deep learning

Abstract

We aimed to assess the ability of deep learning (DL) and support vector machine (SVM) to detect a nonperfusion area (NPA) caused by retinal vein occlusion (RVO) with optical coherence tomography angiography (OCTA) images. The study included 322 OCTA images (normal: 148; NPA owing to RVO: 174 [128 branch RVO images and 46 central RVO images]). Training to construct the DL model using deep convolutional neural network (DNN) algorithms was provided using OCTA images. The SVM used a scikit-learn library with a radial basis function kernel. The area under the curve (AUC), sensitivity and specificity for detecting an NPA were examined. We compared the diagnostic ability (sensitivity, specificity and average required time) between the DNN, SVM and seven ophthalmologists. Heat maps were generated. With regard to the DNN, the mean AUC, sensitivity, specificity and average required time for distinguishing RVO OCTA images with an NPA from normal OCTA images were 0.986, 93.7%, 97.3% and 176.9 s, respectively. With regard to SVM, the mean AUC, sensitivity, and specificity were 0.880, 79.3%, and 81.1%, respectively. With regard to the seven ophthalmologists, the mean AUC, sensitivity, specificity and average required time were 0.962, 90.8%, 89.2%, and 700.6 s, respectively. The DNN focused on the foveal avascular zone and NPA in heat maps. The performance of the DNN was significantly better than that of SVM in all parameters (p < 0.01, all) and that of the ophthalmologists in AUC and specificity (p < 0.01, all). The combination of DL and OCTA images had high accuracy for the detection of an NPA, and it might be useful in clinical practice and retinal screening.

Fig 1. Representative images of the normal macula obtained using optical coherence tomography angiography (OCTA).

The left image is a superficial capillary plexus OCTA image with a normal macula, and the right image is a deep capillary plexus OCTA image with a normal macula. The arrowheads indicate the foveal avascular zone.

Fig 2. Representative retinal vein occlusion images of the macula obtained using optical coherence tomography angiography (OCTA).

(A) The left image is the superficial capillary plexus (SCP) OCTA image with branch retinal vein occlusion (BRVO), and the right image is the deep capillary plexus (DCP) OCTA image with BRVO. The arrows indicate the foveal avascular zone and nonperfusion area with BRVO. (B) The left image is the SCP OCTA image with central retinal vein occlusion (CRVO), and the right image is the DCP OCTA image with CRVO. In the SCP and DCP OCTA images with CRVO, the foveal avascular zone and the nonperfusion area are observed throughout the cropped images.

Fig 3. Overall architecture of the Visual Geometry Group (VGG)-16 model.

A data set of resized optical coherence tomography angiography images (256 × 192 pixels) is the input. VGG-16 includes five blocks and three fully connected layers. Each block includes some convolutional layers followed by a max-pooling layer. The output of block 5 is flattened, resulting in two fully connected layers. The first layer removes spatial information from the extracted feature vectors, and the second layer is a classification layer that uses the feature vectors of the target images acquired in previous layers and the softmax function for binary classification.

Fig 4. The Receiver operating characteristic curves in the deep-learning model, support vector machine model and ophthalmologists.

The area under the curve is 0.986 in the deep-learning model, 0.880 in the support vector machine model and 0.962 in the seven ophthalmologists.

Fig 5. Representative images obtained using optical coherence tomography angiography (OCTA) and their heat maps.

(A) Normal superficial capillary plexus (SCP) OCTA image, (B) normal deep capillary plexus (DCP) OCTA image, (C) heat map of the normal SCP OCTA image, (D) heat map of the normal DCP OCTA image, (E) SCP OCTA image with a nonperfusion area (NPA) owing to branch retinal vein occlusion (BRVO), (F) DCP OCTA image with an NPA owing to BRVO, (G) heat map of the SCP OCTA image with BRVO, (H) heat map of the DCP OCTA image with BRVO, (I) SCP OCTA image with an NPA owing to central retinal vein occlusion (CRVO), (J) DCP OCTA image with an NPA owing to CRVO, (K) heat map of the SCP OCTA image with CRVO and (L) heat map of the DCP OCTA image with CRVO. Red is used to indicate the strength of deep convolutional neural network focus. The color intensity is high at the area of the foveal avascular zone and NPA in SCP and DCP OCTA images; accumulation is noted at the focal points. The deep convolutional neural network focused on the foveal avascular zone and NPA.