testbed/Project-MONAI__MONAI/research/coplenet-pneumonia-lesion-segmentation/README.md

COPLE-Net for COVID-19 Pneumonia Lesion Segmentation

If you use this work in your research, please cite the paper.

A reimplementation of the COPLE-Net originally proposed by:

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

This research prototype is adapted from:

• The HiLab-git/COPLE-Net GitHub repo
• PyMIC, a Pytorch-based toolkit for medical image computing.

To run the inference demo:

• Download and switch to MONAI 0.2.0 source code:

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 to ./images.
• download the adapted pretrained model from google drive folder to ./model.
• run python run_inference.py and segmentation results will be saved at ./output.

(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.)