Department of Ophthalmology, the Second Affiliated Hospital of Zhejiang University, College of Medicine, Zhejiang University, Hangzhou, 310009, China.
School of Electronic Engineering and Computer Science, Queen Mary University of London, London, E1 4NS, United Kingdom.
Sci Data. 2022 Aug 4;9(1):475. doi: 10.1038/s41597-022-01564-3.
Retinal vasculature provides an opportunity for direct observation of vessel morphology, which is linked to multiple clinical conditions. However, objective and quantitative interpretation of the retinal vasculature relies on precise vessel segmentation, which is time consuming and labor intensive. Artificial intelligence (AI) has demonstrated great promise in retinal vessel segmentation. The development and evaluation of AI-based models require large numbers of annotated retinal images. However, the public datasets that are usable for this task are scarce. In this paper, we collected a color fundus image vessel segmentation (FIVES) dataset. The FIVES dataset consists of 800 high-resolution multi-disease color fundus photographs with pixelwise manual annotation. The annotation process was standardized through crowdsourcing among medical experts. The quality of each image was also evaluated. To the best of our knowledge, this is the largest retinal vessel segmentation dataset for which we believe this work will be beneficial to the further development of retinal vessel segmentation.
视网膜血管为观察血管形态提供了直接的机会，而血管形态与多种临床情况相关。然而，对视网膜血管进行客观和定量的解释依赖于精确的血管分割，这既耗时又费力。人工智能（AI）在视网膜血管分割方面显示出巨大的潜力。基于 AI 的模型的开发和评估需要大量的标注视网膜图像。然而，可用于此任务的公共数据集却很稀缺。在本文中，我们收集了一个彩色眼底图像血管分割（FIVES）数据集。FIVES 数据集包含 800 张高分辨率多疾病彩色眼底照片，具有像素级别的人工标注。标注过程通过医疗专家众包进行标准化。还评估了每张图像的质量。据我们所知，这是最大的视网膜血管分割数据集，我们相信这项工作将有助于进一步发展视网膜血管分割。
