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

arXiv: https://arxiv.org/abs/2309.09483 · HTML: https://mlsanity.com/paper/2309.09483/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 20 references that are in the map.
- First posted on arXiv 18 Sep 2023.

## Builds on (most cited first, 10 of 10)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [A ConvNet for the 2020s](https://mlsanity.com/paper/2201.03545/) (2022)
- [UNet++: A Nested U-Net Architecture for Medical Image Segmentation](https://mlsanity.com/paper/1807.10165/) (2018)
- [Road Extraction by Deep Residual U-Net](https://mlsanity.com/paper/1711.10684/) (2017)
- [Recurrent Residual Convolutional Neural Network based on U-Net (R2U-Net) for Medical Image Segmentation](https://mlsanity.com/paper/1802.06955/) (2018)
- [ROSE: A Retinal OCT-Angiography Vessel Segmentation Dataset and New Model](https://mlsanity.com/paper/2007.05201/) (2020)
- [Image Magnification Network for Vessel Segmentation in OCTA Images](https://mlsanity.com/paper/2110.13428/) (2021)

## Built on by (most cited first, 12 of 12)

- [Segment Anything Model for Medical Image Segmentation: Current Applications and Future Directions](https://mlsanity.com/paper/2401.03495/) (2024)
- [Dr-SAM: An End-to-End Framework for Vascular Segmentation, Diameter Estimation, and Anomaly Detection on Angiography Images](https://mlsanity.com/paper/2404.17029/) (2024)
- [OCTAMamba: A State-Space Model Approach for Precision OCTA Vasculature Segmentation](https://mlsanity.com/paper/2409.08000/) (2024)
- [SAM-OCTA2: Layer Sequence OCTA Segmentation with Fine-tuned Segment Anything Model 2](https://mlsanity.com/paper/2409.09286/) (2024)
- [Universal Vessel Segmentation for Multi-Modality Retinal Images](https://mlsanity.com/paper/2502.06987/) (2025)
- [UCS: A Universal Model for Curvilinear Structure Segmentation](https://mlsanity.com/paper/2504.04034/) (2025)
- [A Novel Hybrid Approach for Retinal Vessel Segmentation with Dynamic Long-Range Dependency and Multi-Scale Retinal Edge Fusion Enhancement](https://mlsanity.com/paper/2504.13553/) (2025)
- [Convolutional Prompting for Broad-Domain Retinal Vessel Segmentation](https://mlsanity.com/paper/2412.18089/) (2024)
- [Optimized Vessel Segmentation: A Structure-Agnostic Approach with Small Vessel Enhancement and Morphological Correction](https://mlsanity.com/paper/2411.15251/) (2024)
- [Cross-modal Fundus Image Registration under Large FoV Disparity](https://mlsanity.com/paper/2512.12657/) (2025)
- [Gaze into the Details: Locality-Sensitive Enhancement for OCTA Retinal Vessel Segmentation](https://mlsanity.com/paper/2605.20651/) (2026)
- [ReG-SAM: Reference Graph-Driven SAM for 2D Foundational Vessel Segmentation](https://mlsanity.com/paper/2609.31160/) (2026)

## Abstract (arXiv)

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.