Progressvie-and-Coarse-to-fine-Registration-Network

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.

Install

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

Training

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

Testing

Use this command to obtain the testing results.

python predict.py -g 0 --batch 1 -d datasets/brain.json -c weights/Apr06-1516

Pre-trained model and testing data on LPBA40

The pre-trained model and testing data are available. Please unzip these files, and move the lpba_val.h5 to /datasets/ folder.

Citation

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}}

Acknowledgment

Some codes are modified from RCN and VoxelMorph. Thanks a lot for their great contribution.