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Pulmonary nodule classification in lung cancer from 3D thoracic CT scans

This code is written by Alexander Selvikvåg Lundervold and Satheshkumar Kaliyugarasan .

Project Organization

├── figures            <- Generated figures
│   
├── notebooks          <- Jupyter notebooks 
│   
├── src                <- Source code for use in this project
│   
├── .ignore            <- Local files and folder to be ignored 
│   
├── README.md          <- The top-level README for developers using this project
│
└── environment.yml    <- Config for conda and pip

Steps to run the experiment

  1. Download the processed LIDC_IDRI Version 2 data used in this project from: https://wiki.cancerimagingarchive.net/display/DOI/Standardized+representation+of+the+TCIA+LIDC-IDRI+annotations+using+DICOM

  2. Run the following command to create a new conda environment from yml file:

conda env create --file environment.yml
conda activate lung-ct

[Optional] Run the following command with your conda environment activated:

conda env update --file environment.yml
  1. Run:
python prepare_images.py <IMAGE_PATH>
  1. Go through the notebook: 1.0-classification.ipynb.

[Note] If conda environment is not showing up in Jupyter Notebook run the following lines:

python -m ipykernel install --user --name <ENVIRONMENT> --display-name "Python (lung-ct)"

Acknowledgement

Our work was supported by the Trond Mohn Research Foundation through the project “Computational medical imaging and machine learning – methods, infrastructure and applications" at the Mohn Medical Imaging and Visualization Center, grant number BFS2018TMT07.