Application of Improved U-Net Convolutional Neural Network for Automatic Quantification of the Foveal Avascular Zone in Diabetic Macular Ischemia

Abstract

Objectives.The foveal avascular zone (FAZ) is a biomarker for quantifying diabetic macular ischemia (DMI), to automate the identification and quantification of the FAZ in DMI, using an improved U-Net convolutional neural network (CNN) and to establish a CNN model based on optical coherence tomography angiography (OCTA) images for the same purpose. Methods. The FAZ boundaries on the full-thickness retina of 6 × 6 mm en face OCTA images of DMI and normal eyes were manually marked. Seventy percent of OCTA images were used as the training set, and ten percent of these images were used as the validation set to train the improved U-Net CNN with two attention modules. Finally, twenty percent of the OCTA images were used as the test set to evaluate the accuracy of this model relative to that of the baseline U-Net model. This model was then applied to the public data set sFAZ to compare its effectiveness with existing models at identifying and quantifying the FAZ area. Results. This study included 110 OCTA images. The Dice score of the FAZ area predicted by the proposed method was 0.949, the Jaccard index was 0.912, and the area correlation coefficient was 0.996. The corresponding values for the baseline U-Net were 0.940, 0.898, and 0.995, respectively, and those based on the description data set sFAZ were 0.983, 0.968, and 0.950, respectively, which were better than those previously reported based on this data set. Conclusions. The improved U-Net CNN was more accurate at automatically measuring the FAZ area on the OCTA images than the traditional CNN. The present model may measure the DMI index more accurately, thereby assisting in the diagnosis and prognosis of retinal vascular diseases such as diabetic retinopathy.

Progress in clinical research and applications of retinal vessel quantification technology based on fundus imaging

“…Diabetic retinopathy (DR) is a prevalent microvascular complication of diabetes and a frequent cause of blindness; the disease triggers vascular variations by damaging capillary cells like the vascular endothelial cells and pericytes, leading to local ischemia. DR progression leads to an increase in the number of microaneurysms ( Wu et al, 2014 ; Jiang et al, 2020 ), vascular curvature ( Lee et al, 2018 ), RNP area ( Baxter et al, 2019 ; Alibhai et al, 2020 ; Kim et al, 2021 ), FAZ area ( Mihailovic et al, 2019 ; Ratra et al, 2021 ; Meng et al, 2022 ), and non-circular index and a decrease in VD ( Durbin et al, 2017 ). The RNP area shows a positive correlation with the emergence of neovascularization ( Yu et al, 2020 ).…”

Optical Coherence Tomography Angiography as a Diagnostic Tool for Diabetic Retinopathy

“…The biomarkers include eyes with larger FAZ area, lower VD, and lower fractional dimensions (FD) on DCP, which are associated with a higher risk of DR progression. Meng et al found that the higher the severity of DR classification, the larger the FAZ area and the greater the reduction in visual acuity [90]. Understanding these biomarkers will allow targeted treatment to prevent visual loss.…”