whitepiggod / cdcSegNet

cdcSegNet

• This is a model for segmenting COVID-19 CT images.

Requirements

• Python3
• Pytorch version >= 1.2.0.
• Some basic python packages, such as Numpy, Pandas, SimpleITK.

Data Preparation

• Please put CT images and segmentation masks in the following directory: ./images/, and organize the data as follows:

     ├── train
        ├── image
           ├── 1.jpg, 2.jpg, xxxx
        ├── mask
           ├── 1.png, 2.png, xxxx
     ├── test
        ├── image
           ├── case01
               ├── 1.jpg, 2.jpg, xxxx
        ├── mask
           ├── case01
               ├── 1.png, 2.png, xxxx
  

Training & Testing

• Train the cdcSegNet:

  python train.py

  Weight values are saved in ./weight

• Test the cdcSegNet:

  python test.py

  The results will be saved to ./Results.

• Evaluate the segmentation maps:

  You can evaluate the segmentation maps using the tool in ./utils/evaluation.py.

  record_loss.txt recorded various data in the experiment

Acknowledgement

A collection of COVID-19 imaging-based AI research papers and datasets: https://github.com/HzFu/COVID19_imaging_AI_paper_list