  Optical coherence tomography angiography (OCTA) is a noninvasive imaging
technique that can reveal high-resolution retinal vessels. In this work, we
propose an accurate and efficient neural network for retinal vessel
segmentation in OCTA images. The proposed network achieves accuracy comparable
to other SOTA methods, while having fewer parameters and faster inference speed
(e.g. 110x lighter and 1.3x faster than U-Net), which is very friendly for
industrial applications. This is achieved by applying the modified Recurrent
ConvNeXt Block to a full resolution convolutional network. In addition, we
create a new dataset containing 918 OCTA images and their corresponding vessel
annotations. The data set is semi-automatically annotated with the help of
Segment Anything Model (SAM), which greatly improves the annotation speed. For
the benefit of the community, our code and dataset can be obtained from
https://github.com/nhjydywd/OCTA-FRNet.
