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VoxelMorph is a framework for Unsupervised Learning with CNNs for Image Registration.
The repository incorporates several variants, first presented at CVPR2018 (initial unsupervised learning), then MICCAI2018 (probabilistic & diffeomorphic formulation), and several other extensions and analyses.
keywords: machine learning, convolutional neural networks, alignment, mapping, registration
It might be useful to have each folder inside the ext
folder on your python path.
assuming voxelmorph is setup at /path/to/voxelmorph/
:
export PYTHONPATH=$PYTHONPATH:/path/to/voxelmorph/ext/neuron/:/path/to/voxelmorph/ext/pynd-lib/:/path/to/voxelmorph/ext/pytools-lib/
If you would like to train/test your own model, you will likely need to write some of the data loading code in 'datagenerator.py' for your own datasets and data formats. There are several hard-coded elements related to data preprocessing and format.
These instructions are for the MICCAI2018 variant using train_miccai2018.py
.
If you'd like to run the CVPR version (no diffeomorphism or uncertainty measures, and using CC/MSE as a loss function) use train.py
- Change the top parameters in
train_miccai2018.py
to the location of your image files. - Run
train_miccai2018.py
with options described in the main function at the bottom of the file. Example:
train_miccai2018.py /my/path/to/data --gpu 0 --model_dir /my/path/to/save/models
In our experiments, /my/path/to/data
contains one npz
file for each subject saved in the variable vol_data
.
We provide a T1 brain atlas used in our papers at data/atlas_norm.npz
.
- Put test filenames in data/test_examples.txt, and anatomical labels in data/test_labels.mat.
- Run
python test_miccai2018.py [gpu-id] [model_dir] [iter-num]
For the CC loss function, we found a reg parameter of 1 to work best. For the MSE loss function, we found 0.01 to work best.
For our data, we found image_sigma=0.01
and prior_lambda=25
to work best.
In the original MICCAI code, the parameters were applied after the scaling of the velocity field. With the newest code, this has been "fixed", with different default parameters reflecting the change. We recommend running the updated code. However, if you'd like to run the very original MICCAI2018 mode, please use xy
indexing and use_miccai_int
network option, with MICCAI2018 parameters.
-
The spatial transform code, found at
neuron.layers.SpatialTransform
, accepts N-dimensional affine and dense transforms, including linear and nearest neighbor interpolation options. Note that original development of VoxelMorph usedxy
indexing, whereas we are now emphasizingij
indexing. -
For the MICCAI2018 version, we integrate the velocity field using
neuron.layers.VecInt
. By default we integrate using scaling and squaring, which we found efficient.
If you use voxelmorph or some part of the code, please cite:
Unsupervised Learning for Fast Probabilistic Diffeomorphic Registration
Adrian V. Dalca, Guha Balakrishnan, John Guttag, Mert R. Sabuncu
MICCAI 2018. eprint arXiv:1805.04605
An Unsupervised Learning Model for Deformable Medical Image Registration
Guha Balakrishnan, Amy Zhao, Mert R. Sabuncu, John Guttag, Adrian V. Dalca
CVPR 2018. eprint arXiv:1802.02604
2018-11-10: Added support for multi-gpu training
2018-10-12: Significant overhaul of code, especially training scripts and new model files.
2018-09-15: Added MICCAI2018 support and py3 transition
2018-05-14: Initial Repository for CVPR version, py2.7
For and problems or questions please open an issue in github or email us at voxelmorph@mit.edu