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Optical Coherence Tomography Angiography (OCTA) is a non-invasive andnon-contacting imaging technique providing visualization of microvasculature ofretina and optic nerve head in human eyes in vivo. The adequate image qualityof OCTA is the prerequisite for the subsequent quantification of retinalmicrovasculature. Traditionally, the image quality score based on signalstrength is used for discriminating low quality. However, it is insufficientfor identifying artefacts such as motion and off-centration, which relyspecialized knowledge and need tedious and time-consuming manualidentification. One of the most primary issues in OCTA analysis is to sort outthe foveal avascular zone (FAZ) region in the retina, which highly correlateswith any visual acuity disease. However, the variations in OCTA visual qualityaffect the performance of deep learning in any downstream marginally. Moreover,filtering the low-quality OCTA images out is both labor-intensive andtime-consuming. To address these issues, we develop an automated computer-aidedOCTA image processing system using deep neural networks as the classifier andsegmentor to help ophthalmologists in clinical diagnosis and research. Thissystem can be an assistive tool as it can process OCTA images of differentformats to assess the quality and segment the FAZ area. The source code isfreely available at https://github.com/shanzha09/COIPS.git.  Another major contribution is the large-scale OCTA dataset, namelyOCTA-25K-IQA-SEG we publicize for performance evaluation. It is comprised offour 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 atotal number of 25,665 images. The large-scale OCTA dataset is available athttps://doi.org/10.5281/zenodo.5111975, https://doi.org/10.5281/zenodo.5111972.

A Deep Learning-based Quality Assessment and Segmentation System with a  Large-scale Benchmark Dataset for Optical Coherence Tomographic Angiography  Image