Purpose: To provide a diverse, high-quality dataset of color fundus images (CFIs) with detailed artery-vein (A/V) segmentation annotations, supporting the development and evaluation of machine learning algorithms for vascular analysis in ophthalmology. Methods: CFIs were sampled from the longitudinal Rotterdam Study (RS), encompassing a wide range of ages, devices, and capture conditions. Images were annotated using a custom interface that allowed graders to label arteries, veins, and unknown vessels on separate layers, starting from an initial vessel segmentation mask. Connectivity was explicitly verified and corrected using connected component visualization tools. Results: The dataset includes 1024x1024-pixel PNG images in three modalities: original RGB fundus images, contrast-enhanced versions, and RGB-encoded A/V masks. Image quality varied widely, including challenging samples typically excluded by automated quality assessment systems, but judged to contain valuable vascular information. Conclusion: This dataset offers a rich and heterogeneous source of CFIs with high-quality segmentations. It supports robust benchmarking and training of machine learning models under real-world variability in image quality and acquisition settings. Translational Relevance: By including connectivity-validated A/V masks and diverse image conditions, this dataset enables the development of clinically applicable, generalizable machine learning tools for retinal vascular analysis, potentially improving automated screening and diagnosis of systemic and ocular diseases.

翻译：目的：提供一个多样化、高质量的彩色眼底图像数据集，包含详细的动静脉分割标注，以支持眼科血管分析机器学习算法的开发与评估。方法：彩色眼底图像采样自纵向鹿特丹研究，涵盖广泛的年龄、设备和采集条件。使用定制标注界面，标注人员在初始血管分割掩模的基础上，分别在独立图层上标注动脉、静脉和未知血管。通过连通分量可视化工具对连通性进行了显式验证和校正。结果：数据集包含1024×1024像素的PNG图像，提供三种模态：原始RGB眼底图像、对比度增强版本和RGB编码的动静脉分割掩模。图像质量差异显著，包含通常被自动质量评估系统排除但经判断具有重要血管信息的挑战性样本。结论：本数据集提供了丰富且异质性的彩色眼底图像资源，并配有高质量分割标注。它支持在真实世界图像质量和采集条件变异下，对机器学习模型进行稳健的基准测试和训练。转化相关性：通过纳入经过连通性验证的动静脉分割掩模及多样化的图像条件，本数据集能够促进开发具有临床适用性和泛化能力的视网膜血管分析机器学习工具，有望提升全身性疾病与眼部疾病的自动化筛查与诊断水平。