ArticleScientific data2025
OCT5k: A dataset of multi-disease and multi-graded annotations for retinal layers.
Article in Scientific data, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Query-Driven Retinal Layer Segmentation in OCT Using Cross-Attentive Feature Learning.Diagnostics (Basel, Switzerland) · 2026Article
- RVO-ME: A Dual-Task OCT Dataset for Segmentation and Detection of Macular Lesions in Retinal Vein Occlusion.Scientific data · 2026Article
- Can YOLO Detect Retinal Pathologies? A Step Towards Automated OCT Analysis.Diagnostics (Basel, Switzerland) · 2025Article
- Publicly available imaging datasets for age-related macular degeneration: Evaluation according to the Findable, Accessible, Interoperable, Reusable (FAIR) principles.Experimental eye research · 2025Review
- OCT5k: A dataset of multi-disease and multi-graded annotations for retinal layers.Scientific data · 2025Article
- Narrative Review of Artificial Intelligence in Ophthalmic Disease Detection : Artificial Intelligence in Ophthalmic Diseases Detection.Galen medical journal · 2025Review
Corrections and comments
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Authors and funding
18 authors.
Funding
Abstract
Publicly available open-access OCT datasets for retinal layer segmentation have been limited in scope, often being small in size, specific to a single disease, or containing only one grading. This dataset improves upon this with multi-grader and multi-disease labels for training machine learning-based algorithms. The proposed dataset covers three subsets of scans (Age-related Macular Degeneration, Diabetic Macular Edema, and healthy) and annotations for two types of tasks (semantic segmentation and object detection). This dataset compiled 5016 pixel-wise manual labels for 1672 OCT scans featuring 5 layer boundaries for three different disease classes to support development of automatic techniques. A subset of data (566 scans across 9 classes of disease biomarkers) was subsequently labeled for disease features for 4698 bounding box annotations. To minimize bias, images were shuffled and distributed among graders. Retinal layers were corrected, and outliers identified using the interquartile range (IQR). This step was iterated three times, improving layer annotations' quality iteratively, ensuring a reliable dataset for automated retinal image analysis.
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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.