By Haiqiao Wang, Dong Ni, Yi Wang.
Paper link: [TMI]
An unsupervised brain MR deformable registration method that achieves precise alignment through a pure convolutional pyramid structure and a semantics-infused progressive recursive inter-level looping strategy for modeling complex deformations, even without pre-alignment of brain MR images.
The official access addresses of the public data sets are as follows：
LPBA [link]
Mindboggle [link]
Note that we use the processed IXI dataset provided by freesurfer.
For convenience, we are sharing the preprocessed LPBA dataset used in our experiments. Once uncompressed, simply modify the "LPBA_path" in train.py to the path name of the extracted data. Next, you can execute train.py to train the network, and after training, you can run infer.py to test the network performance.
If you use the code in your research, please cite:
@ARTICLE{10423043,
author={Wang, Haiqiao and Ni, Dong and Wang, Yi},
journal={IEEE Transactions on Medical Imaging},
title={Recursive Deformable Pyramid Network for Unsupervised Medical Image Registration},
year={2024},
volume={},
number={},
pages={1-1},
keywords={Deformation;Decoding;Feature extraction;Deformable models;Training;Image resolution;Image registration;Deformable image registration;convolutional neural networks;brain MRI},
doi={10.1109/TMI.2024.3362968}}
The overall framework and some network components of the code are heavily based on TransMorph and VoxelMorph. We are very grateful for their contributions.
The file makePklDataset.py shows how to make a pkl dataset from the original LPBA dataset. If you have any other questions about the .pkl format, please refer to the github page of [TransMorph_on_IXI].
Several PyTorch implementations of some baseline methods can be found at [SmileCode].