This is the official Pytorch implementation of "Fast Symmetric Diffeomorphic Image Registration with Convolutional Neural Networks" (CVPR 2020), written by Tony C. W. Mok and Albert C. S. Chung.
** Please also check out our new deep learning-based image registration framework (LapIRN - MICCAI2020) at https://github.com/cwmok/LapIRN, which achieved promising registration performance in large deformation settings. **
Python 3.5.2+Pytorch 1.3.0NumPyNiBabel
This code has been tested with Pytorch and GTX1080TI GPU.
python Test_SYMNet.py
If you want to train a new model using your own dataset, please define your own data generator for train_SYMNet.py and perform the following script.
python train_sym_onepass.py
If you find this repository useful, please cite:
- Fast Symmetric Diffeomorphic Image Registration with Convolutional Neural Networks
Tony C. W. Mok, Albert C. S. Chung
CVPR 2020. eprint arXiv:2003.09514
We found that estimating the time 1 solution instead of 0.5 solution tends to produce a smoother result in our later experiments. If you want to switch back to 0.5 solution, please replace the "self.time_step" with "self.time_step-1" at line 165 in Models.py and train a new model from scratch.
Some codes in this repository are modified from IC-Net and VoxelMorph.
Keywords: Diffeomorphic Image Registration, convolutional neural networks, alignment, Symmetric Image Registration