  Optical Coherence Tomography Angiography (OCTA) is a non-invasive and
non-contacting imaging technique providing visualization of microvasculature of
retina and optic nerve head in human eyes in vivo. The adequate image quality
of OCTA is the prerequisite for the subsequent quantification of retinal
microvasculature. Traditionally, the image quality score based on signal
strength is used for discriminating low quality. However, it is insufficient
for identifying artefacts such as motion and off-centration, which rely
specialized knowledge and need tedious and time-consuming manual
identification. One of the most primary issues in OCTA analysis is to sort out
the foveal avascular zone (FAZ) region in the retina, which highly correlates
with any visual acuity disease. However, the variations in OCTA visual quality
affect the performance of deep learning in any downstream marginally. Moreover,
filtering the low-quality OCTA images out is both labor-intensive and
time-consuming. To address these issues, we develop an automated computer-aided
OCTA image processing system using deep neural networks as the classifier and
segmentor to help ophthalmologists in clinical diagnosis and research. This
system can be an assistive tool as it can process OCTA images of different
formats to assess the quality and segment the FAZ area. The source code is
freely available at https://github.com/shanzha09/COIPS.git.
  Another major contribution is the large-scale OCTA dataset, namely
OCTA-25K-IQA-SEG we publicize for performance evaluation. It is comprised of
four subsets, namely sOCTA-3$\times$3-10k, sOCTA-6$\times$6-14k,
sOCTA-3$\times$3-1.1k-seg, and dOCTA-6$\times$6-1.1k-seg, which contains a
total number of 25,665 images. The large-scale OCTA dataset is available at
https://doi.org/10.5281/zenodo.5111975, https://doi.org/10.5281/zenodo.5111972.
