Vessel segmentation and quantification in ultra-wide-field OCTA images

Abstract

Early detection of retinopathy in the periphery of the macula is an important step in preventing severe vision loss. Some morphological parameters about the extensive retina can be obtained through ultra-wide-field OCTA images. Based on small-scale fundus OCTA vessel segmentation, accurate diagnosis can already be obtained by means of deep learning. However, no similar research of segmentation of peripheral blood vessels is reported. In this study, blood vessels of retina were segmented, and blood vessel centerlines were extracted in ultra-wide-field OCTA images. Quantification of the segmented images was performed to explore features of blood vessel. We used a U-shaped neural network that performs well on small samples to cope with the problem of limited data sets. Scale compression and slice segmentation were used to apply the trained network model to vessel segmentation and centerline extraction in ultra-wide-field OCTA images which is of size at 21mm×21mm. Based on the results of the segmentation of blood vessels, the diameter index of blood vessels and vascular tortuousness were calculated, which proved to be associated with some eye diseases. These results and parameters can be helpful for the early screening of some ophthalmic diseases.