A deep learning pathogen recognition system for pneumonia based on CT

This is the code for pathogen recorgnition of pneumonia. The base of this work forked our former work https://github.com/ChenWWWeixiang/diagnosis_covid19.

Guidance to Use

Environment

run pip install -r requirements.txt to install all above packages.

Usage

python testengine.py -p <path to trainedmodel> -m <list of paths for lung segmentation> -i <list of paths for image data> -o <path to save record> -g <gpuid>

Train on Your Own Data

1. Data Preparation : The datasets from Wuhan Union Hospital, Western Campus of Wuhan Union Hospital, and Jianghan Mobile Cabin Hospital were used under the license of the current study and are not publicly available. Applications for access to the LIDC-IDRI database can be made at https://wiki.cancerimagingarchive.net/display/Public/LIDC-IDRI. ILD-HUG database can be accessed at http://medgift.hevs.ch/wordpress/databases/ild-database/.
2. Volumes to Images: We suggest that test data should be in ".nii" format (any formats that SimpleITK can work on is OK with small changes in codes) and training data should be in ".jpg" format (any formats that opencv-python can work on is OK with small changes in codes). A script "data/test_hu.py" is used to cut volumes into images.
3. Lung Segmentation : using Deeplabv1 (https://github.com/DrSleep/tensorflow-deeplab-resnet) or any other segmentation methods.
4. Split Dataset:

python data/get_set_seperate_jpg.py -p <list of paths to jpgs for seperate> -t <train list output path> -v <validation list output path>

5. Begin Training: training parameters are listed on options_stack.toml. Run python main.py to train the model.
6. Metrics: to evaluate the performances, run python testengine.py -p <path to trainedmodel> -m <list of paths for lung segmentation> -i <list of paths for image data> -o <path to save record> -g <gpuid> and then the script python ploc_roc.py -i <list of paths for recording npys> -o <path to save metrics>

More Research Tools

• Model Visualization: A script to show Grad-CAM/CAM result is available. Input images should be in jpg formats and should be concatenated with lung mask as Red channel. The input raw jpgs and input masked jpgs should be in pair:

python models/gradcam.py --image_path <raw jpg img path> --mask_path <jpg img with mask path> --model_path <path to trained model> --output_path <path to output>

You can also use our volume cam script, which demands raw data volumes and lung segmentation volumes as inputs.

python models/grad_volume.py --image_path <raw data nii path> --mask_path <lung mask nii file path> --model_path <path to trained model> --output_path <path to output>

• Fractal Dimension Features :

  • Extract Fractal Dimension: extract fractal dimension of a region.python fractal-dimension/fractals.py -i <binary nii file determined regions> -o <output txt path>
  • Extract 3D mesh Fractal Dimension: extract fractal dimension of gray level mesh. The input data and input region should be in pair.python fractal-dimension/fractal.py -m <binary nii file determined regions> -r <nii file of raw data> -o <output txt path>

• Extract Radiomics Features : parameters of radiomics are listed in radiomics/RadiomicsParams.yaml. Run python get_r_features.py m <binary nii file determined regions> -r <nii file of raw data> -o <output csv path> to get radiomics features of a region. The input data and input region should be in pair.

• LASSO Analysis : this script analysis radiomics features using LASSO. python plot_lasso_mse.py -i <input csv file >

• Abnormal Locating : we fine-tune the trained model in slices from only COVID-19 positives in order to train a model to locate abnormal slices in COVID-19 positive volumes. Test the model using python multi_period_scores/get_abnormal_scores.py and visualize the results using python analysis_mp.py. Some of our patients have multi-period CTs, the visualization of abnormal slices shows the changes of lesion with time.

Citation

If you find this project helpful, please cite our paper:

@article{chen2021deep,
  title={Deep diagnostic agent forest (DDAF): A deep learning pathogen recognition system for pneumonia based on CT},
  author={Chen, Weixiang and Han, Xiaoyu and Wang, Jian and Cao, Yukun and Jia, Xi and Zheng, Yuting and Zhou, Jie and Zeng, Wenjuan and Wang, Lin and Shi, Heshui and others},
  journal={Computers in biology and medicine},
  pages={105143},
  year={2021},
  publisher={Elsevier}
}