An Accurate and Efficient Neural Network for OCTA Vessel Segmentation and a New Dataset
This work addresses efficient vessel segmentation for medical imaging applications, but it is incremental as it builds on existing methods with optimizations for speed and size.
The authors tackled retinal vessel segmentation in OCTA images by proposing a neural network that achieves accuracy comparable to state-of-the-art methods while being 110x lighter and 1.3x faster than U-Net, making it suitable for industrial applications.
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.