FOVEA: Preoperative and intraoperative fetinal fundus images with optic disc and retinal vessel annotations

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.应用于眼科影像的计算机视觉方法的性能与应用范围，高度依赖标注训练数据的可获取性。目前虽已有多款彩色眼底摄影数据集，但据我们所知，FOVEA是首个实现术中域与术前域高质量标注匹配的数据集。该数据集包含从英国伦敦摩尔菲尔兹眼科医院收集的40名患者的相关数据，包含由两名独立临床研究员标注的术前与术中视网膜血管、视盘标注信息，同时附带术中通过生物显微镜成像采集的视网膜眼底短视频片段。标注信息已完成验证并转换为二值分割掩码，相关代码已开源至GitHub平台。我们期望该数据集可用于辅助玻璃体视网膜手术过程中外科医生的深度学习应用，例如通过定位感兴趣区域或配准额外成像模态。