Published July 6, 2021 | Version 1.0
Dataset Open

OCTA image dataset with label annotation for quality assessment

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:

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.

arXiv:https://arxiv.org/abs/2107.10476v1

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