# ACTA-Reg-Net

**Repository Path**: ximenadai/ACTA-Reg-Net

## Basic Information

- **Project Name**: ACTA-Reg-Net
- **Description**: An Auto-Context based Tissue-Aware Deformable Registration Network (ACTA-Reg-Net) for Infant Brain MR Images
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2020-05-21
- **Last Updated**: 2020-12-19

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# An Auto-Context based Tissue-Aware Deformable Registration Network for Infant Brain MRI
Coming soon! Code review processing. Welcome for any bugs report.

  * If you use ACTA-Reg-Net, please cite:

    **An Auto-Context Deformable Registration Network for Infant Brain MRI**    
 Dongming Wei, Sahar Ahmad, Yunzhi Huang, Lei Ma, Qian Wang, Pew-Thian Yap, Dinggang Shen    
 [eprint arXiv:2005.09230](https://arxiv.org/abs/2005.09230)

## Instruction
<img src='./Fig/Auto_Context.png' />
<img src='./Fig/TAReg.png' />


This code is for registering infant brain MR images. The registration is based on the segmentation map (gray matter, white matter). You can try to modify the directory in train.py to train your model. Or you can directly use test.py to test your data, where a trained model is given in ./models.

For obtaining an ACTA-Reg-Net, you need to work on ./src/demo.sh to run all the experiments.

## Requirements
- Python 3.6 (3.7 should work well)
- Tensorflow 1.10 (any 1.xx version should work well)
- Keras 2.2.4
- Bash

You can choose to run
```bash
pip install -r requirements.txt
```
Or you can perform
```bash
conda -n tf14-py36 python=3.6
conda activate tf10-py36
conda install tensorflow-gpu==1.10
```
## Train
```bash
python train.py
```
For training your dataset, you need to modify the data directory in the trian.py. For our task, we save the infant brian images into the ../data/MAPS_DATASET/Train_Set. 
After this step, you have obtained your Reg-Net, which is supposed to generate smooth deformation fields. Then, you can execute the demo.sh to perform an 'auto-context' manner to boost the registration performance.

## Test
```bash
python test.py gpu_id ../models/ iteration_num fixed.nii.gz moving.nii.gz moving_label.nii.gz
```

## Demo
```bash
cd ./src
./demo.sh -m moving.nii.gz -l moving_label.nii.gz -n save_dir -f fixed.nii.gz
```
The results are saved into ../data/results/*, including warped moving image, moving label, deformation field, and displacement uncertainty map.
## Result
<img src='./Fig/Result_with_Grid.png' />
<img src='./Fig/Smoothness_Comparison.png'>

# Acknowledgement:
We would like to acknowledge the contribution of [VoxelMorph](https://github.com/voxelmorph/voxelmorph).


# Contact:
For any problems, please open an [issue](https://github.com/Barnonewdm/ACTA-Reg-Net/issues/new).