This regular-challenge dataset was provided by Sun Yat-sen Ophthalmic Center, Sun Yat-sen University, Guangzhou, China. The dataset contains 200 fundus color images: 100 pairs in the training set and 100 pairs in the test set.

Original Statistic Information

Dataset name	Anatomical region	Task type	Modality	Num. Classes	Train/Val/Test Images	Train/Val/Test Labeled	Release Date	License
GammaTask3	eye	segmentation	fundus photophy	3	100/-/100	yes/-/-	2021	CC-BY-NC 4.0

Class Name	Num. Train	Pct. Train	Num. Val	Pct. Val	Num. Test	Pct. Test
background	100	99.02	-	-	-	-
optic disc	100	0.67	-	-	-	-
optic cup	100	0.31	-	-	-	-

Note:

Pct means percentage of pixels in this category in all pixels.

Visualization

Dataset Citation

@article{fu2018joint,
  title={Joint optic disc and cup segmentation based on multi-label deep network and polar transformation},
  author={Fu, Huazhu and Cheng, Jun and Xu, Yanwu and Wong, Damon Wing Kee and Liu, Jiang and Cao, Xiaochun},
  journal={IEEE transactions on medical imaging},
  volume={37},
  number={7},
  pages={1597--1605},
  year={2018},
  publisher={IEEE}
}

@article{sevastopolsky2017optic,
  title={Optic disc and cup segmentation methods for glaucoma detection with modification of U-Net convolutional neural network},
  author={Sevastopolsky, Artem},
  journal={Pattern Recognition and Image Analysis},
  volume={27},
  pages={618--624},
  year={2017},
  publisher={Springer}
}

Prerequisites

Python v3.8
PyTorch v1.10.0
pillow(PIL) v9.3.0
scikit-learn(sklearn) v1.2.0
MIM v0.3.4
MMCV v2.0.0rc4
MMEngine v0.2.0 or higher
MMSegmentation v1.0.0rc5

All the commands below rely on the correct configuration of PYTHONPATH, which should point to the project's directory so that Python can locate the module files. In gammm3/ root directory, run the following line to add the current directory to PYTHONPATH:

export PYTHONPATH=`pwd`:$PYTHONPATH

Dataset preparing

download dataset from here and decompression data to path 'data/'.
run script "python tools/prepare_dataset.py" to split dataset and change folder structure as below.
run script "python ../../tools/split_seg_dataset.py" to split dataset and generate train.txt, val.txt and test.txt. If the label of official validation set and test set can't be obtained, we generate train.txt and val.txt from the training set randomly.

  mmsegmentation
  ├── mmseg
  ├── projects
  │   ├── medical
  │   │   ├── 2d_image
  │   │   │   ├── fundus_photography
  │   │   │   │   ├── gamma3
  │   │   │   │   │   ├── configs
  │   │   │   │   │   ├── datasets
  │   │   │   │   │   ├── tools
  │   │   │   │   │   ├── data
  │   │   │   │   │   │   ├── train.txt
  │   │   │   │   │   │   ├── val.txt
  │   │   │   │   │   │   ├── images
  │   │   │   │   │   │   │   ├── train
  │   │   │   │   |   │   │   │   ├── xxx.png
  │   │   │   │   |   │   │   │   ├── ...
  │   │   │   │   |   │   │   │   └── xxx.png
  │   │   │   │   │   │   │   ├── test
  │   │   │   │   |   │   │   │   ├── yyy.png
  │   │   │   │   |   │   │   │   ├── ...
  │   │   │   │   |   │   │   │   └── yyy.png
  │   │   │   │   │   │   ├── masks
  │   │   │   │   │   │   │   ├── train
  │   │   │   │   |   │   │   │   ├── xxx.png
  │   │   │   │   |   │   │   │   ├── ...
  │   │   │   │   |   │   │   │   └── xxx.png

Divided Dataset Information

Note: The table information below is divided by ourselves.

Class Name	Num. Train	Pct. Train	Num. Val	Pct. Val	Num. Test	Pct. Test
background	80	99.01	20	99.07	-	-
optic disc	80	0.68	20	0.63	-	-
optic cup	80	0.32	20	0.31	-	-

Training commands

To train models on a single server with one GPU. (default）

mim train mmseg ./configs/${CONFIG_PATH}

Testing commands

To test models on a single server with one GPU. (default）

mim test mmseg ./configs/${CONFIG_PATH}  --checkpoint ${CHECKPOINT_PATH}

Checklist

Milestone 1: PR-ready, and acceptable to be one of the projects/.
- Finish the code
- Basic docstrings & proper citation
- Test-time correctness
- A full README
Milestone 2: Indicates a successful model implementation.
- Training-time correctness
Milestone 3: Good to be a part of our core package!
- Type hints and docstrings
- Unit tests
- Code polishing
- Metafile.yml
Move your modules into the core package following the codebase's file hierarchy structure.
Refactor your modules into the core package following the codebase's file hierarchy structure.

README.md Unescape Escape

Glaucoma grAding from Multi-Modality imAges Task3

Description

Dataset Overview