Deng Zhuo, Gao Weihao, Gong Zheng, Gan Run, Chen Lu, Zhang Shaochong, Ma Lan
Shenzhen International Graduate School, Tsinghua University, Shenzhen, 518055, P. R. China.
The Shenzhen Eye Hospital, Shenzhen, 518040, P. R. China.
Sci Data. 2025 Jul 25;12(1):1298. doi: 10.1038/s41597-025-05381-2.
Retinal artery-vein vessels are associated with systemic chronic diseases and cardiovascular diseases. Therefore, the accurate quantitative analysis of retinal artery-vein vessels is the preliminary basis of clinical diagnosis. Most of the existing artificial intelligence(AI) methods are data-driven. Although some public retinal artery-vein vessel segmentation datasets have been released, their data quality is unsatisfactory. In this paper, we establish a new fundus image dataset for AI-based artery-vein segmentation, Fundus-AVSeg. It consists of 100 high-resolution fundus images with pixel-wise manual annotation by professional ophthalmologists. We believe our Fundus-AVSeg will benefit the further development of retinal artery-vein vessel segmentation.
视网膜动静脉血管与全身性慢性疾病和心血管疾病相关。因此，对视网膜动静脉血管进行准确的定量分析是临床诊断的初步基础。现有的大多数人工智能（AI）方法都是数据驱动的。尽管已经发布了一些公开的视网膜动静脉血管分割数据集，但其数据质量并不理想。在本文中，我们建立了一个用于基于AI的动静脉分割的新眼底图像数据集Fundus-AVSeg。它由100张高分辨率眼底图像组成，这些图像由专业眼科医生进行逐像素手动标注。我们相信我们的Fundus-AVSeg将有助于视网膜动静脉血管分割的进一步发展。
