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AMD-SD: An Optical Coherence Tomography Image Dataset for wet AMD Lesions Segmentation.
Yunwei Hu1, Yundi Gao1, Weihao Gao2
1Ophthalmic Center, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, 330000, P. R. China.
Scientific Data
|September 18, 2024
Summary
A new dataset of Optical coherence tomography (OCT) images aids in diagnosing wet Age-related Macular Degeneration (AMD). This resource supports AI development for better wet AMD detection and monitoring.
Area of Science:
- Ophthalmology
- Medical Imaging
- Artificial Intelligence
Background:
- Wet Age-related Macular Degeneration (wet AMD) significantly impairs vision.
- Optical coherence tomography (OCT) is crucial for wet AMD diagnosis and monitoring.
- High-quality OCT datasets are lacking for wet AMD algorithm development.
Purpose of the Study:
- To introduce a comprehensive dataset for wet AMD OCT image segmentation.
- To facilitate research and development of AI algorithms for wet AMD analysis.
Main Methods:
- Curated a dataset (AMD-SD) of 3049 OCT B-scan images from 138 patients.
- Annotated images with five key segmentation labels: subretinal fluid, intraretinal fluid, ellipsoid zone continuity, subretinal hyperreflective material, and pigment epithelial detachment.
Main Results:
- The AMD-SD dataset provides detailed annotations for wet AMD lesions.
- Enables quantitative analysis of abnormalities visible in OCT scans.
Conclusions:
- The AMD-SD dataset is a valuable resource for advancing wet AMD segmentation algorithms.
- Supports the development of AI-assisted clinical applications for wet AMD.
