opaserr / dam

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

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

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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