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<p>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.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年2月4日

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  • Fen Xiong,
  • Guodong Li,
  • Weihao Gao,
  • Yundi Gao,
  • Yanfang Zhu,
  • Xinjing Xia,
  • Lan Ma,
  • Weifeng Liu,
  • Yunwei Hu

摘要

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.