RVO-Lesion: A Dual-Task OCT Dataset for Joint Segmentation and Detection of Macular Lesions in Retinal Vein Occlusion

Retinal vein occlusion (RVO) is one of the most common vision-threatening retinal diseases, primarily due to complications such as macular edema (ME). Optical coherence tomography (OCT), a non-invasive imaging modality, has become an essential tool for the diagnosis, treatment monitoring, and clinical evaluation of RVO, owing to its ability to clearly visualize fine retinal structures and macular fluid distribution. OCT facilitates precise segmentation, abnormality detection, and quantitative analysis, playing a critical role in clinical decision-making. However, the development of automated algorithms for RVO-ME analysis has been hindered by the lack of high-quality, manually segmented datasets (e.g., RVO-SD).To address this limitation, we have constructed a manually annotated RVO-ME segmentation dataset. The dataset comprises 3,012 OCT B-scan images collected from 130 patients, encompassing a total of 146 eyes.Each image is annotated with segmentation labels for five critical retinal structures—subretinal fluid (SRF), intraretinal fluid (IRF), the ellipsoid zone (EZ), the external limiting membrane (ELM) and highly reflective foci (HF) . This dataset serves as a valuable resource for evaluating the accuracy and robustness of various segmentation algorithms related to RVO, and significantly facilitates the development of artificial intelligence models for RVO-related disease analysis.