Automatic Segmentation and Statistical Analysis of the Foveal Avascular Zone

This study facilitates the extraction of foveal avascular zone (FAZ) metrics from optical coherence tomography angiography (OCTA) images, offering valuable clinical insights and enabling detailed statistical analysis of FAZ size and shape across three patient groups: healthy, type II diabetes mellitus and both type II diabetes mellitus (DM) and high blood pressure (HBP). Additionally, it evaluates the performance of four deep learning (DL) models—U-Net, U-Net with DenseNet121, U-Net with MobileNetV2 and U-Net with VGG16—in automating the segmentation of the FAZ. Manual segmentation of the images by ophthalmological clinicians was performed initially, and data augmentation was used to enhance the dataset for robust model training and evaluation. Consequently, the original set of 103 full retina OCTA images was extended to 672 cases, including 42 images from normal patients, 357 images from DM patients, and 273 images from patients with both DM and HBP. Among the models, U-Net with DenseNet outperformed the others, achieving the highest accuracy, Intersection over Union (IoU), and Dice coefficient across all patient groups. This research is distinct in its focus on full retina OCTA images and its inclusion of patients with both hypertension and diabetes, an area that is less studied in the existing literature.