ArticleScientific data2026
Pixel-Level Segmented Longitudinal Optical Coherence Tomography Data of Macular Hole Surgery Outcomes.
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
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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.
- Pixel-Level Segmented Longitudinal Optical Coherence Tomography Data of Macular Hole Surgery Outcomes.Scientific data · 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
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Research involving idiopathic full-thickness macular hole (iFTMH) has been limited by a shortage of longitudinal, annotated public datasets. In this article, we introduce a longitudinal annotated dataset containing OCT scans from patients who previously had iFTMH surgery with multiple time points. The OCT dataset based on the publicly accessible cohort contains a total of 2,591 fovea-centered horizontal and vertical B-scans that were collected at one preoperative and six postoperative time points (from 2 weeks to 48 months) that allow researchers to examine retinal recovery. We provide high-quality, expert-validated, pixel-level segmentation masks for 12 retinal features, representing both pathological (e.g., macular hole, cysts, epiretinal membrane) and anatomical (e.g., external limiting membrane, ellipsoid zone, retinal pigment epithelium) structures. Technical validation revealed high inter- and intra-rater reliability for each annotation (e.g., >0.80 average Dice coefficient for selected features). We also provide a reference nnUNet baseline to facilitate future benchmarking and method comparison. Overall, this resource is intended to facilitate AI modeling, support quantitative studies of retinal healing, and verify new ophthalmic image analysis methods.
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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 OpenQuestion graph.