Tools to preprocess the data of the HECKTOR challenge and run a baseline CNN segmentation on the training data (using NiftyNet). This repository contains the code used to prepare the data of the challenge (DICOM to NIfTI, SUV computation and bounding box generation, not needed for the participants). It also contains an example of implementation to resample the data within the bounding boxes and resample back to the original resolution, as well as a NiftyNet baseline implementation to train a CNN on 90% of the training data and evaluate the results on the remaining 10%.

To access the data, visit the challenge website: https://www.aicrowd.com/challenges/hecktor and follow the instructions. The code included here was intended to work with a specific repository structure described in Section Project Organization. Following git clone https://github.com/voreille/hecktor.git, create a data/ folder in the repository and place the unzipped data in it.

To install the necessary dependencies you can use pip install -r requirements.txt. It is advised to use it within a python3 virtual environment.

Run python src/resampling/cli_resampling.py to crop and resample the data following the repository structure or use arguments (type python src/resamping/cli_resampling.py --help for more informations).

Use cuda-10 for GPU usage. cd src/niftynet and run net_segment train -c config3D.ini for training, followed by net_segment inference -c config3D.ini for inference and net_segment evaluation -c config3D.ini for evaluation. A random 90%-10% split is used for training and testing. Note that the HECKTOR test data will come from a center different from the four training centers, you may want to evaluate your generalization across centers. A renaming function should be used to comply with the format needed for the test evaluation; run python rename_output.py to create a folder with correct file names.

An example of how the evaluation will be computed is illustrated in the notebook notebooks/evaluate_predictions.ipynb. For the submission of the test results, you will need to resample back to the original CT resolution. This is implemented in this ipynb prior to evaluation and can be used with niftynet output or other algorithms' outputs. Alternatively, run python src/resampling/cli_get_resolution.py followed by python src/resampling/cli_resampling_back.py to resample your results back to the original CT resolution. (For more information python src/resampling/cli_resampling_back.py --help)

├── README.md                     
├── data                           <- NOT in the version control.
│   ├── resampled                  <- The data in the nifty resampled and cropped according to the bounding boxes (bbox.csv).
│   ├── hecktor_nii                <- The data converted in the nifty format with the original geometric frame
│   └── bbox.csv                   <- The bounding box for each patient computed with AUTO_CROP_FUNC
├── requirements.txt               <- The requirements file for reproducing the analysis environment, e.g.
│                                    generated with `pip freeze > requirements.txt`
├── Makefile                       <- Used to do set up the environment and make the conversion of DICOM to NIFTI.
├── notebooks
│   ├── crop_dataset.ipynb
│   └── evaluate_predictions.ipynb <- Example of how the evalution will be computed. This example use the output of the NiftyNet model.
└── src                            <- Source code for use in this project.
    ├── data                       <- Scripts to download or generate data
    │   ├── __init__.py
    │   ├── bounding_box.py
    │   ├── dicom_conversion.py
    │   └── make_dataset.py
    ├── evaluation
    |   ├── __init__.py
    │   ├── cli_compute_scores.py
    │   └── scores.py
    ├── niftynet
    │   ├── __init__.py
    │   ├── config2D.ini
    │   ├── config3D.ini
    │   ├── dataset_split.csv
    │   └── rename_output.py
    ├── resampling
    │   ├── __init__.py
    |   ├── cli_get_resolution.py
    |   ├── cli_resampling_back.py
    |   ├── cli_resampling.py
    |   ├── resampling.py
    |   └── utils.py
    └── tox.ini                       <- tox file with settings for running tox; see tox.readthedocs.io

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