ArticleScientific data2024
AMD-SD: An Optical Coherence Tomography Image Dataset for wet AMD Lesions Segmentation.
Article in Scientific data, 2024. 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.
- AAV-based gene therapies for neovascular AMD.Gene therapy · 2026Review
- Comprehensive 3D Optical Coherence Tomography Dataset for AMD and DME: Facilitating Deep-Learning-Based 3D Segmentation.Scientific data · 2026Article
- RVO-ME: A Dual-Task OCT Dataset for Segmentation and Detection of Macular Lesions in Retinal Vein Occlusion.Scientific data · 2026Article
- Learning feature dependencies for precise tumor region detection and segmentation in optical coherence tomography images.International ophthalmology · 2025Article
- Three-Dimensional Choroidal Vessel Analysis in Asymmetric Bilateral Age-Related Macular Degeneration: A Comparison of Active Neovascular AMD and Dry AMD Fellow Eyes.Investigative ophthalmology & visual science · 2025Article
- AMD-SD: An Optical Coherence Tomography Image Dataset for wet AMD Lesions Segmentation.Scientific data · 2024Article
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Authors and funding
13 authors.
Funding
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Abstract
Wet Age-related Macular Degeneration (wet AMD) is a common ophthalmic disease that significantly impacts patients' vision. Optical coherence tomography (OCT) examination has been widely utilized for diagnosing, treating, and monitoring wet AMD due to its cost-effectiveness, non-invasiveness, and repeatability, positioning it as the most valuable tool for diagnosis and tracking. OCT can provide clear visualization of retinal layers and precise segmentation of lesion areas, facilitating the identification and quantitative analysis of abnormalities. However, the lack of high-quality datasets for assessing wet AMD has impeded the advancement of related algorithms. To address this issue, we have curated a comprehensive wet AMD OCT Segmentation Dataset (AMD-SD), comprising 3049 B-scan images from 138 patients, each annotated with five segmentation labels: subretinal fluid, intraretinal fluid, ellipsoid zone continuity, subretinal hyperreflective material, and pigment epithelial detachment. This dataset presents a valuable opportunity to investigate the accuracy and reliability of various segmentation algorithms for wet AMD, offering essential data support for developing AI-assisted clinical applications targeting wet AMD.
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