  The area of the foveal avascular zone (FAZ) from en face images of optical
coherence tomography angiography (OCTA) is one of the most common measurement
based on this technology. However, its use in clinic is limited by the high
variation of the FAZ area across normal subjects, while the calculation of the
volumetric measurement of the FAZ is limited by the high noise that
characterizes OCTA scans. We designed an algorithm that exploits the higher
signal-to-noise ratio of en face images to efficiently identify the capillary
network of the inner retina in 3-dimensions (3D), under the assumption that the
capillaries in separate plexuses do not overlap. The network is then processed
with morphological operations to identify the 3D FAZ within the bounding
segmentations of the inner retina. The FAZ volume and area in different
plexuses were calculated for a dataset of 430 eyes. Then, the measurements were
analyzed using linear mixed effect models to identify differences between three
groups of eyes: healthy, diabetic without diabetic retinopathy (DR) and
diabetic with DR. Results showed significant differences in the FAZ volume
between the different groups but not in the area measurements. These results
suggest that volumetric FAZ could be a better diagnostic detector than the
planar FAZ. The efficient methodology that we introduced could allow the fast
calculation of the FAZ volume in clinics, as well as providing the 3D
segmentation of the capillary network of the inner retina.
