An Accurate and Efficient Neural Network for OCTA Vessel Segmentation and a New Dataset

TL;DR

This paper introduces a lightweight, fast neural network for retinal vessel segmentation in OCTA images, achieving high accuracy with fewer parameters, and provides a new annotated dataset to facilitate further research.

Contribution

It presents a novel neural network architecture with fewer parameters and faster inference for OCTA vessel segmentation, and introduces a semi-automatically annotated OCTA dataset.

Findings

Achieves comparable accuracy to state-of-the-art methods.

110x lighter and 1.3x faster than U-Net.

Provides a new dataset with 918 annotated OCTA images.

Abstract

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.