OCTA image dataset with pixel-level mask annotation for FAZ segmentation
Authors/Creators
- 1. Key Laboratory of Bio-Resource and Eco-Environment of ministry of Education, College of Life Sciences, State Key Laboratory of Hydraulics and Mountain River Engineering, Sichuan University, Chengdu, Sichuan, 610064, China
- 2. School of Mathematical Sciences, Shanghai Jiao Tong University, Shanghai, 200240, China
- 3. State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou 510060, China
- 4. Intelligent Computing Laboratory, International Graduate School, Tsinghua University, Shenzhen, Guangdong, 518055, China
- 5. Department of Ophthalmology, The Third People's Hospital of Zigong City, Zigong 643020, China
- 6. School of Computer Science, University Technology of Sydney, Ultimo NSW 2007, Australia
Description
This dataset is publish by the research "A Deep Learning-based Quality Assessment and Segmentation System with a Large-scale Benchmark Dataset for Optical Coherence Tomographic Angiography Image"
Detail:
This dataset is the pixel-level mask annotation for FAZ segmentation. 1,101 3 × 3 mm2 sOCTA images chosen from gradable and best OCTA images randomly in subset sOCTA-3x3-10k, and 1,143 6 × 6 mm2dOCTA images were annotated by an experienced ophthalmologist.
GitHub: https://github.com/shanzha09/COIPS
These datasets are public available, if you use the dataset or our system in your research, please cite our paper: A Deep Learning-based Quality Assessment and Segmentation System with a Large-scale Benchmark Dataset for Optical Coherence Tomographic Angiography Image.
Files
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