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.

翻译：光学相干断层扫描血管成像（OCTA）是一种能揭示高分辨率视网膜血管的非侵入性成像技术。本文提出一种用于OCTA图像视网膜血管分割的精准高效神经网络。该网络在保持与其他先进方法相当精度的同时，具有更少的参数量和更快的推理速度（例如，比U-Net轻110倍且快1.3倍），这一特性对工业应用极为友好。该成果通过将改进的循环ConvNeXt模块应用于全分辨率卷积网络实现。此外，我们构建了一个包含918张OCTA图像及其对应血管标注的新数据集。该数据集借助Segment Anything Model（SAM）进行半自动化标注，大幅提升了标注效率。为促进社区发展，我们的代码与数据集可从https://github.com/nhjydywd/OCTA-FRNet获取。