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emma-sjwang / pOSAL

Code for TMI paper: Patch-based Output Space Adversarial Learning for Joint Optic Disc and Cup Segmentation
MIT License
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refuge-challenge

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pOSAL: Patch-based Output Space Adversarial Learning for Joint Optic Disc and Cup Segmentation.

We provide the Keras implements based on Tensorflow Backend for REFUGE challenge segmentation task.

Getting Started

Install requirments

conda create -n posal python=3.5
conda activate posal
pip install keras==2.2.0
pip insatll tensorflow-gpu==1.4.0
conda install tqdm
conda install -c anaconda scikit-image
conda install opencv

Prerequisites

Running Evaluation

To reproduce the results for the rank in REFUGE challenge in MICCAI 2018, please do

python predict.py 0 # 0 is the avaliable GPU id, change is neccesary

Running Training for Dri-GS dataset

Remember to check/change the data path and weight path

python train_DGS.py 0
python test_DGS.py 0

The CDR values used for glaucoma diagnsis are generated with MATLAB.

cd matlab-code

Please change the input and output path in the generate_CDR_values.m file.

Acknowledge Some codes are revised according to selimsef/dsb2018_topcoders, HzFu/MNet_DeepCDR and evaluateion code . Thank them very much.

Citation

@article{wang2019patch,
  journal={IEEE Transactions on Medical Imaging},
  title={Patch-Based Output Space Adversarial Learning for Joint Optic Disc and Cup Segmentation},
  author={Wang, Shujun and Yu, Lequan and Yang, Xin and Fu, Chi-Wing and Heng, Pheng-Ann},
  year={2019},
  volume={38},
  number={11},
  pages={2485-2495},
  publisher={IEEE},
  doi={10.1109/TMI.2019.2899910},
  }