The implementation of our paper "Joint Progressive and Coarse-to-fine Registration of Brain MRI via Deformation Field Integration and Non-Rigid Feature Fusion".
The motivation of this work is to decompose the deformation field in both progressive and coarse-to-fine manner for alleviating the difficulty of prediction. Specifically, we first built a unified CNN which can decompose the deformation filed in a coarse-to-fine manner, and then proposed the DFI and NFF modules for the progressive decomposition relying on light-weight decoding blocks instead of heavy-weight CNN models, i.e. VTN.
For more details, please refer to our paper.
The packages and their corresponding version we used in this repository are listed in below.
- Tensorflow==1.15.4
- Keras==2.3.1
- tflearn==0.5.0
After configuring the environment, please use this command to train the model.
python train.py -g 0 --batch 1 -d datasets/brain.json -b PCNet -n 1 --round 10000 --epoch 10
Use this command to obtain the testing results.
python predict.py -g 0 --batch 1 -d datasets/brain.json -c weights/Apr06-1516
The pre-trained model and testing data are available. Please unzip these files, and move the lpba_val.h5
to /datasets/
folder.
If you use this code as part of any published research, we'd really appreciate it if you could cite the following paper:
@ARTICLE{9765391,
author={Lv, Jinxin and Wang, Zhiwei and Shi, Hongkuan and Zhang, Haobo and Wang, Sheng and Wang, Yilang and Li, Qiang},
journal={IEEE Transactions on Medical Imaging},
title={Joint Progressive and Coarse-to-Fine Registration of Brain MRI via Deformation Field Integration and Non-Rigid Feature Fusion},
year={2022},
volume={41},
number={10},
pages={2788-2802},
doi={10.1109/TMI.2022.3170879}}
Some codes are modified from RCN and VoxelMorph. Thanks a lot for their great contribution.