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The Foveal Avascular Zone (FAZ) is of clinical importance since the retinal vascular arrangement around the fovea changes with retinal vascular diseases and in high myopic eyes. Therefore, it is important to segment and quantify the FAZs accurately. Using a novel location-aware deep learning method the FAZ boundary was segmented in en-face optical coherence tomography angiography (OCTA) images. The FAZ dimensions were compared the parameters determined using four methods: (1) device in-built software (Cirrus 5000 Angioplex), (2) manual segmentation using Image J software by an experienced clinician, and (4) the new method (new location-aware deep-learning method). The parameters were measured from OCTA data from healthy subjects (n=34) and myopic patients (n=66). For this purpose, FAZ location was manually delineated in en-face OCTA images of dimensions 420x420 pixels corresponding to 6mm x 6mm. A modified UNet segmentation with an additional channel from a Gaussian distribution around the likely location of the FAZ was designed and trained using 100 manually segmented OCTA images. The predicted FAZ and the related parameters were then obtained using a test dataset consisting of 100 images. For analysis, two strategies were applied. The segmentation of FAZ was compared using the Dice coefficient and Structural Similarity Index (SSIM) to determine the effectiveness of the proposed deep learning method when compared to the other three methods. Furthermore, to provide deeper insight, a set of FAZ dimensions namely area, perimeter, circularity index, eccentricity, perimeter, major axis, minor axis, inner circle radius, circumcircle radius, the maximum and minimum boundary dimensions, and orientation of major axis were compared between the 3 methods. Finally, vessel-related parameters including tortuosity, vessel diameter index (VDI) and vessel avascular density (VAD) were calculated and compared. The high myopic eyes exhibited a narrowing the FAZ area and perimeter. The currently developed algorithm does not correct for axial length variations. This analysis should be extended with a larger number of images in each group of myopia as well as correcting for axial length variations.
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