Abstract
Diabetic retinopathy (DR) is a frequent vascular complication of diabetes mellitus and remains a leading cause of vision loss worldwide. Microaneurysm (MA) is usually the first symptom of DR that leads to blood leakage in the retina. Periodic detection of MAs will facilitate early detection of DR and reduction of vision injury. In this study, we proposed a novel model for the detection of MAs in fluorescein fundus angiography (FFA) images based on the improved FC-DenseNet, MAs-FC-DenseNet. FFA images were pre-processed by the Histogram Stretching and Gaussian Filtering algorithm to improve the quality of FFA images. Then, MA regions were detected by the improved FC-DenseNet. MAs-FC-DenseNet was compared against other FC-DenseNet models (FC-DenseNet56 and FC-DenseNet67) or the end-to-end models (DeeplabV3+ and PSPNet) to evaluate the detection performance of MAs. The result suggested that MAs-FC-DenseNet had higher values of evaluation metrics than other models, including pixel accuracy (PA), mean pixel accuracy (MPA), precision (Pre), recall (Re), F1-score (F1), and mean intersection over union (MIoU). Moreover, MA detection performance for MAs-FC-DenseNet was very close to the ground truth. Taken together, MAs-FC-DenseNet is a reliable model for rapid and accurate detection of MAs, which would be used for mass screening of DR patients.
Highlights
Diabetic retinopathy (DR) is a frequent vascular complication of diabetes mellitus and remains a leading cause of vision loss worldwide
We proposed a novel model for detecting MAs in fluorescein fundus angiography (FFA) images based on the improved FCDenseNet, MAs-FC-DenseNet
In experiment 2, MAs-FC-DenseNet was compared against other FC-DenseNet models including FC-DenseNet[56] and FC-DenseNet[67] to compare MA detection performance
Summary
Diabetic retinopathy (DR) is a frequent vascular complication of diabetes mellitus and remains a leading cause of vision loss worldwide. Periodic detection of MAs is required for the early diagnosis of retinopathy. FFA is highly sensitive and demonstrates MAs as the hyperfluorescent dots in the early phase. It is an important imaging modality, which can capture images after the intravenous injection of fluorescein dye[3]. An automated detection method is urgently required for the accurate detection of MAs in FFA images. Et al proposed an unsupervised method for DR detection based on C NN13. González-Gonzalo et al proposed a deep visualization method based on the unsupervised selective inpainting[14]. It still required to develop novel methods to further improve the detection accuracy of MAs
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