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README.txt
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README.txt
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************************** DeepFLASH **************************
This repository contains source code and data of a predictive diffeomorphic image registration (https://arxiv.org/abs/2004.02097).
************************** Disclaimer **************************
This software is published for academic and non-commercial use only.
The implementation includes network training, testing for 2D and 3D medical image registration. We request you to cite our research paper if you use it:
DeepFLASH: An Efficient Network for Learning-based Medical Image Registration.
Jian Wang, Miaomiao Zhang. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2020.
```
@inproceedings{wang2020deepflash,
title={DeepFLASH: An Efficient Network for Learning-based Medical Image Registration},
author={Wang, Jian and Zhang, Miaomiao},
booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
pages={4444--4452},
year={2020}
}
```
************************** Setup **************************
* [PyTorch 3.4](http://pytorch.org/)
* [PyCA](https://bitbucket.org/scicompanat/pyca)
* [FLASH] (https://bitbucket.org/FlashC/flashc/src/master/)
* [CUDA 9.0](https://developer.nvidia.com/cuda-downloads)
* [Anaconda 4.3.1](https://anaconda.org)
************************** Usage **************************
Below is a *QuickStart* guide on how to use DeepFLASH for network training and testing.
========================= Training ========================
Required Input Data: pairwise images and associated initial velocity field.
Please note that the optimal registration solutions (bandlimited initial velocity fields) of our network training is generated by FLASH algorithm [1] (https://bitbucket.org/FlashC/flashc).
You may also run other registration algorithm to estimate the initial velocity fields and use our provided MATLAB preprocess scripts (/DeepFLASH/preprocess/processmhd.m, please follow the instruction in /DeepFLASH/preprocess/Preprocessing_instruction.txt) to extract the frequencies.
Steps:
cd DeepFLASH/
sh runDeepFLASH_training.sh
python3 DeepFLASH_train.py -h
--network_type
specify network type
--im_src_realpart IM_SRC_REALPART
root directory of real parts of source images
--im_tar_realpart IM_TAR_REALPART
root directory of real parts of target images
--im_vel_realX IM_VEL_REALX
root directory of real parts of velocity fields
(X direction)
--im_vel_realY IM_VEL_REALY
root directory of real parts of velocity fields
(Y direction)
--im_vel_realZ IM_VEL_REALZ
root directory of real parts of velocity fields
(Z direction)
--im_src_imaginarypart IM_SRC_IMAGINARYPART
root directory of imaginary parts of source images
--im_tar_imaginarypart IM_TAR_IMAGINARYPART
root directory of imaginary parts of target images
--im_vel_imagX IM_VEL_IMAGX
root directory of imaginary parts of velocity fields
(X direction)
--im_vel_imagY IM_VEL_IMAGY
root directory of imaginary parts of velocity fields
(Y direction)
--im_vel_imagZ IM_VEL_IMAGZ
root directory of imaginary parts of velocity fields
(Z direction)
========================= Testing ========================
sh runDeepFLASH_testing.sh
python3 DeepFLASH_test.py -h
--network_type
specify network type
--saved_model
root directory of the saved model
--im_src_realpart IM_SRC_REALPART
root directory of real parts of source images
--im_tar_realpart IM_TAR_REALPART
root directory of real parts of target images
--im_src_imaginarypart IM_SRC_IMAGINARYPART
root directory of imaginary parts of source images
--im_tar_imaginarypart IM_TAR_IMAGINARYPART
root directory of imaginary parts of target images
************************** Reference **************************
[1]. Zhang et. al, Fast diffeomorphic image registration via Fourier-approximated Lie algebras, 2019 IJCV.