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
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:1903.03545Unsupervised Learning for Fast Probabilistic Diffeomorphic Registration
Adrian V. Dalca, Guha Balakrishnan, John Guttag, Mert R. Sabuncu MICCAI 2018. eprint arXiv:1805.04605VoxelMorph: 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:1809.05231An 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)
- Pytorch
- hdf5
- scipy
- scikit-learn