Published July 6, 2021 | Version 1.2
Dataset Restricted

OCTA image dataset with pixel-level mask annotation for FAZ segmentation

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.

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

Files

Restricted

The record is publicly accessible, but files are restricted to users with access.