ArticleScientific data2026
RVO-ME: A Dual-Task OCT Dataset for Segmentation and Detection of Macular Lesions in Retinal Vein Occlusion.
Article in Scientific data, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Query-Driven Retinal Layer Segmentation in OCT Using Cross-Attentive Feature Learning.Diagnostics (Basel, Switzerland) · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
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.
Indexed as
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.