A Pytorch Implementation of Automated segmentation of choroidal neovascularization on optical coherence tomography angiography images of neovascular age-related macular degeneration patients based on deep learning (Journal of Big Data)
Prerequisites
- python3
- numpy
- pillow
- opencv-python
- scikit-learn
- tensorboardX
- visdom
- pytorch
- torchvision
By default, we put the datasets in ./data/datasets/ and save trained models in ./models/ (soft link is suggested). You can set the --data_dir argument to /your/train_data/path/, the --val_data_dir argument to /your/val_data/path/, and the --models_dir argument to /your/models/path/ when running all experiments below.
In order to train the segmentation model, run the following command:
CUDA_VISIBLE_DEVICES=0 python front_main.py
The related model parameters can be modified in option.py.
Please cite our paper if you find it useful for your research.
@article{feng2023automated,
title={Automated segmentation of choroidal neovascularization on optical coherence tomography angiography images of neovascular age-related macular degeneration patients based on deep learning},
author={Feng, Wei and Duan, Meihan and Wang, Bingjie and Du, Yu and Zhao, Yiran and Wang, Bin and Zhao, Lin and Ge, Zongyuan and Hu, Yuntao},
journal={Journal of Big Data},
volume={10},
number={1},
pages={111},
year={2023},
publisher={Springer}
}