This repository contains code and data for paper:
Quicksilver: Fast Predictive Image Registration - a Deep Learning Approach"
X. Yang, R. Kwitt and M. Niethammer
2017
This software is published for academic and non-commercial use only.
This code is based or PyTorch and PyCA. It has been tested on Ubuntu 14.04/16.04 LTS with Python 2.7 (using CUDA 8.0 with one Nvidia TitanX).
Dependencies:
Remark: NiftyReg is used for optional affine alignment to the ICBM152 atlas before performing deformable registration.
We recommend using Anaconda as your development platform, as this cleanly separates the Python/PyTorch installation (and all required libraries) from your system libraries. We exemplify such an installation below, assuming that the full installation is done under /scratch and CUDA (8.0) is installed under /usr/local/cuda.
Install Anaconda 4.3.1 (Python 2.7)
mkdir /scratch
cd /scratch
wget https://repo.continuum.io/archive/Anaconda2-4.3.1-Linux-x86_64.sh
bash Anaconda2-4.3.1-Linux-x86_64.sh
Follow the installation instructions. We assume that you installed
Anaconda under /scratch/anaconda2.
Install PyCA
For our code we use a specific version of PyCA, so please use the following command to download the PyCA code:
cd /scratch
git clone git@bitbucket.org:scicompanat/pyca.git
cd ./pyca
git checkout 9954dd5319efaa0ac5f58977e57acf004ad73ed7
mkdir Build && cd Build
ccmake ...
Configure and compile PyCA.
Final configuration
Finally, we clone the Quicksilver repository as
cd /scratch
git clone https://github.com/rkwitt/quicksilver.git
and create a config file conda.cfg with the following content:
export PATH=$PATH:/usr/local/cuda/bin
export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/cuda/lib64
export PYTHONPATH=$PYTHONPATH:/scratch/pyca/Build/python_module
export CUDA_HOME=/usr/local/cuda
export PATH="/scratch/anaconda2/bin:$PATH"
Source this file with source conda.cfg to setup the environment.
cd /scratch/quicksilver/code
python qs_predict.py --moving-image moving_1.nii moving_2.nii moving_3.nii ...
--target-image target_1.nii target_2.nii target_3.nii ...
--output-prefix prefix_1 prefix_2 prefix_3 ...
Add --use-correct option if want to use correction network.
To use multiple GPUs for prediction, add --n-GPU N option, where N indicates the number of GPUs to use. For maximum performance, combine this option with --batch-size K to specify the number of 3D patches as network input, and set K to be a multiple of N.