Ravasio Claudio S, Flores-Sánchez Blanca, Bloch Edward, Bergeles Christos, da Cruz Lyndon
School of Biomedical Engineering & Imaging Sciences, King's College London, London, SE1 7EU, UK.
Sheffield Teaching Hospitals, Sheffield, S5 7AU, UK.
Sci Data. 2025 Apr 26;12(1):703. doi: 10.1038/s41597-025-04965-2.
The performance and scope of computer vision methods applied to ophthalmic images is highly dependent on the availability of labelled training data. While there are a number of colour fundus photography datasets, FOVEA is to the best of our knowledge the first dataset that matches high-quality annotations in the intraoperative domain with those in the preoperative one. It comprises data from 40 patients collected at Moorfields Eye Hospital (London, UK) and includes preoperative and intraoperative retinal vessel and optic disc annotations performed by two independent clinical research fellows, as well as short video clips showing the retinal fundus though biomicroscopy imaging in the intraoperative setting. The annotations were validated and converted into binary segmentation masks, with the code used available on GitHub. We expect this data to be useful for deep learning applications aimed at supporting surgeons during vitreoretinal surgery procedures e.g. by localising points of interest or registering additional imaging modalities.
应用于眼科图像的计算机视觉方法的性能和范围高度依赖于带标签训练数据的可用性。虽然有许多彩色眼底摄影数据集,但据我们所知,FOVEA是第一个将术中领域的高质量注释与术前领域的注释相匹配的数据集。它包含来自英国伦敦摩尔菲尔德眼科医院收集的40名患者的数据,包括由两名独立临床研究员进行的术前和术中视网膜血管及视盘注释,以及术中通过生物显微镜成像显示眼底的短视频片段。这些注释经过验证并转换为二进制分割掩码,所使用的代码可在GitHub上获取。我们期望这些数据对旨在在玻璃体视网膜手术过程中支持外科医生的深度学习应用有用,例如通过定位感兴趣点或配准其他成像模态。