/dam

Code to generate anatomical changes observed over the course of radiotherapy treatments.

Primary LanguagePython

Daily Anatomy Generative Model

This repository contains the code to generate artificial repeat CTs based on a planning CT recorded at the beginning of the treatment.

  • Apache 2.0 License
  • Copyright: Oscar Pastor-Serrano, TU Delft

Credits

If you like this repository, please click on Star!

If you use the code for your research, please consider citing:

  • Anatomy models: A probabilistic deep learning model of inter-fraction anatomical variations in radiotherapy Oscar Pastor-Serrano, Steven Habraken, Mischa Hoogeman, Danny Lathouwers, Dennis Schaart, Yusuke Nomura, Lei Xing, Zoltán Perkó Physics in Medicine & Biology 66 (23), 235003 (https://iopscience.iop.org/article/10.1088/1361-6560/ac383f/meta)

  • The code is based on Voxelmorph:

    Unsupervised Learning of Probabilistic Diffeomorphic Registration for Images and Surfaces
    Adrian V. Dalca, Guha Balakrishnan, John Guttag, Mert R. Sabuncu MedIA: Medial Image Analysis. 2019. eprint arXiv:1903.03545

    Unsupervised Learning for Fast Probabilistic Diffeomorphic Registration
    Adrian V. Dalca, Guha Balakrishnan, John Guttag, Mert R. Sabuncu MICCAI 2018. eprint arXiv:1805.04605

    VoxelMorph: A Learning Framework for Deformable Medical Image Registration
    Guha Balakrishnan, Amy Zhao, Mert R. Sabuncu, John Guttag, Adrian V. Dalca IEEE TMI: Transactions on Medical Imaging. 2019. eprint arXiv:1809.05231

    An Unsupervised Learning Model for Deformable Medical Image Registration
    Guha Balakrishnan, Amy Zhao, Mert R. Sabuncu, John Guttag, Adrian V. Dalca CVPR 2018. eprint arXiv:1802.02604

This project is supported by the following institutions:

  • KWF Kanker Bestrijding
  • Department of Radiation Sciences and Technology (TU Delft)

Requirements

  • Pytorch
  • hdf5
  • scipy
  • scikit-learn