Abstract
Retinal vein occlusion (RVO) is one of the most common vision-threatening retinal diseases, with macular edema (ME) as its primary complication. Optical coherence tomography (OCT), a non-invasive imaging modality, enables detailed visualization of retinal structures and fluid distribution, thus supporting accurate diagnosis, treatment monitoring, and clinical assessment of RVO-related conditions. However, the development of automated algorithms for RVO-ME analysis has been hindered by the lack of high-quality, manually segmented datasets. To address this limitation, we constructed a manually annotated RVO-ME dataset comprising 3,012 OCT B-scans from 146 eyes of 130 patients. For each image, we provide segmentation labels for four key retinal features (subretinal fluid, intraretinal fluid, the ellipsoid zone, and the external limiting membrane), along with point annotations to facilitate the detection of highly reflective foci. This dataset provides a valuable benchmark for assessing the performance of segmentation algorithms and facilitates the advancement of artificial intelligence models for RVO-related disease analysis.
Data availability
The complete dataset is available for download via the following link: https://doi.org/10.6084/m9.figshare.29804435.v1.
Code availability
The code mentioned in this study can be found at https://github.com/AI-thpremed/Dual-task-baseline-RVO-ME.
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Acknowledgements
This work was supported by grants from the National Natural Science Foundation of China (82360204), National Natural Science Foundation incubation project of The Second Affiliated Hospital of Nanchang University (2022YNFY12004), and Nanchang University Education Development Foundation “Clinical Leading Research” Project (YK019).
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F.X., G.L., and W.G. were responsible for the conceptualization and design of the study. Y.H., W.L., G.L., and F.X. were responsible for data annotation. Y.Z., Y.G.and X.X. contributed to data collection and assisted in manuscript writing. Y.H., W.G., and L.M. participated in the technical validation. W.L.and G.L. collectively supervised this research’s progress. All authors contributed to the article and approved the submitted version.
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Xiong, F., Li, G., Gao, W. et al. RVO-ME: A Dual-Task OCT Dataset for Segmentation and Detection of Macular Lesions in Retinal Vein Occlusion. Sci Data (2026). https://doi.org/10.1038/s41597-026-06695-5
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DOI: https://doi.org/10.1038/s41597-026-06695-5
