Clinically Applicable Deep Learning Framework for Organs at Risk Delineation in CT images

License

Copyright (C) 2019 University of California Irvine and DEEPVOXEL Inc. All rights reserved. Licensed under the CC BY-NC-SA 4.0 license (https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode).

Note: The code/software is licensed for non-commerical academic research purpose only.

Reference

If you use the code or data in your research, we will appreciate it if you could cite the following paper:

Tang et al, Clinically applicable deep learning framework for organs at risk delineation in CT images

Nature Machine Intelligence, 1, pages 480–491 (2019)

Data

• Images, annotations and preprocessed files for dataset2 and dataset3 are freely available for non-commercial research pursposes at here.

• The original dicom images for dataset2 are freely available at Head-Neck Cetuximab and Head-Neck-PET-CT.

• The original images and annotations for dataset3 are freely available at PDDCA.

• Use this link to request a copy of the test data of dataset1.

Once you download the data, unzip them and put them under data/raw and data/preprocessed.

Trained models

• Use this link to request pre-trained model checkpoints for non-commercial academic research purposes.

Once you download the model checkpoints, change the config['initial_checkpoint'] to the path of the file you download.

System requirement

OS: Ubuntu 16.04

Memory: at least 64GB

GPU: Nvidia 1080ti (11GB memory) is minimum requirement, and you need to reduce the number of z slices input to the network, by setting train_max_crop_size to for example [112, 240, 240]; we recommend using Nvidia Titan RTX (24GB memory) with the default settings.

Install dependencies

1. Install libs using pip or conda

Python 3.7
pytorch 1.1.0 (a must if you want to use tensorboard to monitor the loss)
cuda == 9.0/10.0

conda install -c conda-forge opencv 
conda install -c kayarre pynrrd 
conda install -c conda-forge pydicom
conda install -c conda-forge tqdm

Please make sure your working directory is src/

cd src

2. Install a custom module for bounding box NMS and overlap calculation.

(Only needed if you want to train the model, NO need to run this for testing) to build two custom functions.

cd build/box
python setup.py install

3. In order to use Tensorboard for visualizing the losses during training, we need to install tensorboard.

pip install tb-nightly  # Until 1.14 moves to the release channel

Preprocess (optional)

Use utils/preprocess.py to preprocess the converted data.

If you have downloaded the raw and preprocessed data, please remeber to change config.py, or other places if necessary:

line 36 data_dir to '../data/raw'

line 37 preprocessed_data_dir to '../data/preprocessed'

Train

Change training configuration and data configuration in config.py, especially the path to your preprocessed data.

You can change network configuration in net/config.py, then run training script:

python train.py

Evaluating model performance

Please change the train_config['initial_checkpoint'] in config.py to the checkpoint you want to use for evaluating the model performance on test data sets. Then run:

python test.py eval

You should see the results for each patient, where each row is an OAR and the columns are: OAR name, DSC, DSC standard deviation, 95%HD, 95%HD standard deviation.

Test

python test.py test --weight $PATH_TO_WEIGHT --dicom-path $DICOM_PATH --out-dir $OUTPUT_DIR

$PATH_TO_WEIGHT is the path to best model weight used for prediction, e.g. "weights/1001_400.ckpt" or "weights/model_weights"

(If the --weight option is a directory, then the script will consider all files in this directory as weights and perform prediction using all weight files in this direcotry. Then a majority voting will be performed to merge multiple predictions. This is more robust and more accurate.

If the --weight option is a file, then simply the single model prediction will be performed.)