OCTA image dataset with label annotation for quality assessment
- 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
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:
OCTA image dataset with label annotation for quality assessment. sOCTA-3x3-10k: 10,480 3 × 3 mm2 superficial vascular layer OCTA (sOCTA) images divided into three classes; sOCTA-6x6-14k: 14,042 6 × 6 mm2 sOCTA images divided into three classes.
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
Files
(4.3 GB)
| Name | Size | Download all |
|---|---|---|
|
md5:a838c6bb855ac97c98ab39a33506e5d5
|
4.3 GB | Download |