Keras/Tensorflow implementation of TP-GAN for automating treatment planning for low-dose-rate prostate brachytherapy. This work has been published in Medical Image Computing and Computer Assisted Intervention – MICCAI 2021 (https://miccai2021.org/en/).
Python 3.6
Tensorflow: 2.0.0
Keras: 2.3.1
Recreate conda environment as follows:
conda env create -f environment.yml
Or if you are using Docker:
docker pull tazleef/tf2.0.0-cv-keras2.3.1-imgaug:latest
Due to privacy policy, we are unable to share our clinical dataset. However, we have included a few sample cases for reference. Format your centre's dataset in the same way and set the filepath and training parameters in train.py.
To train the model, run train.py.
The model for automatic needle plan generation from target volumes can be found here. Using these two techniques together will allow for an end-to-end treatment planning solution for prostate brachytherapy.
This code can be used for other 3D image to image translation task by modifying the network architectures accordingly.
Please cite the following paper if you use this code in your work:
@InProceedings{aleef2021,
title={Rapid Treatment Planning for Low-dose-rate Prostate Brachytherapy with TP-GAN},
author={Aleef, Tajwar Abrar, Spadinger, Ingrid T., Peacock, Michael D., Salcudean, Septimiu E. and Mahdavi, S. Sara},
booktitle="Medical Image Computing and Computer Assisted Intervention -- MICCAI 2021",
pages={581--590},
year={2021},
organization={Springer}
}
If you face any problem using this code then please create an issue in this repository or contact me at [email protected]
The 3D Resnet code is based on https://github.com/JihongJu/keras-resnet3d
MIT