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Open Fundus Photograph Dataset with Pathologic Myopia Recognition and Anatomical Structure Annotation
Huihui Fang1,2, Fei Li3, Junde Wu4
1South China University of Technology, Guangzhou, China.
Scientific Data
|January 20, 2024
Summary
This study introduces PALM, an open dataset of 1200 fundus images for pathological myopia (PM) recognition. This AI-ready dataset aids in early detection of retinal degeneration to prevent vision loss.
Area of Science:
- Ophthalmology
- Medical Imaging
- Artificial Intelligence
Background:
- Pathologic myopia (PM) is a leading cause of irreversible blindness.
- Early detection of PM-associated fundus lesions is crucial for preventing vision loss.
- Automated diagnostic tools using artificial intelligence can aid in screening and identification of PM.
Purpose of the Study:
- To introduce PALM, an open fundus imaging dataset for pathological myopia recognition.
- To provide detailed annotations for anatomical structures and lesions within the dataset.
- To facilitate the development of AI-driven tools for early PM detection.
Main Methods:
- Compilation of a dataset comprising 1200 color fundus photographs.
- Manual annotation of pathological myopia categories.
- Detailed labeling of anatomical structures (optic disc, fovea) and lesions (retinal atrophy, retinal detachment).
Main Results:
- The PALM dataset includes 1200 images with pathological myopia labels.
- Comprehensive annotations cover optic disc, fovea, patchy retinal atrophy, and retinal detachment.
- The dataset is characterized by its quality and specific sample characteristics.
Conclusions:
- The PALM dataset is a valuable resource for training AI models for pathological myopia detection.
- Availability of this annotated dataset can accelerate research in automated ophthalmic diagnostics.
- Facilitates early screening and management of pathological myopia to preserve vision.
