| # COPLE-Net for COVID-19 Pneumonia Lesion Segmentation |
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| <p> |
| <img src="./fig/img.png" width="30%" alt='lung-ct'> |
| <img src="./fig/seg.png" width="30%" alt='lung-ct-seg'> |
| </p> |
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| > If you use this work in your research, please cite the paper. |
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| A reimplementation of the COPLE-Net originally proposed by: |
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| G. Wang, X. Liu, C. Li, Z. Xu, J. Ruan, H. Zhu, T. Meng, K. Li, N. Huang, S. Zhang. (2020) |
| "A Noise-robust Framework for Automatic Segmentation of COVID-19 Pneumonia Lesions from CT Images." |
| IEEE Transactions on Medical Imaging. 2020. DOI: [10.1109/TMI.2020.3000314](https://doi.org/10.1109/TMI.2020.3000314) |
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| This research prototype is adapted from: |
| - [The `HiLab-git/COPLE-Net` GitHub repo](https://github.com/HiLab-git/COPLE-Net/) |
| - [PyMIC, a Pytorch-based toolkit for medical image computing.](https://github.com/HiLab-git/PyMIC) |
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| To run the inference demo: |
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| - Download and switch to MONAI 0.2.0 source code: |
| ```bash |
| git clone https://github.com/Project-MONAI/MONAI |
| cd MONAI |
| git checkout 0.2.0 |
| pip install -e '.[nibabel]' # install from the source code |
| ``` |
| The rest of the steps assume that the current directory is the folder of this README file. |
| - download the input examples from [google drive folder](https://drive.google.com/drive/folders/1pIoSSc4Iq8R9_xXo0NzaOhIHZ3-PqqDC) to `./images`. |
| - download the adapted pretrained model from [google drive folder](https://drive.google.com/drive/folders/1HXlYJGvTF3gNGOL0UFBeHVoA6Vh_GqEw) to `./model`. |
| - run `python run_inference.py` and segmentation results will be saved at `./output`. |
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| _(To segment COVID-19 pneumonia lesions from your own images, make sure that the images have been cropped into the lung region, |
| and the intensity has been normalized into [0, 1] using window width/level of 1500/-650.)_ |
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