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VoxelMorph: learning-based image registration

VoxelMorph is a general purpose library for learning-based tools for alignment/registration, and more generally modelling with deformations.


We have several VoxelMorph tutorials:


To use the VoxelMorph library, either clone this repository and install the requirements listed in or install directly with pip.

pip install voxelmorph

Pre-trained models

See list of pre-trained models available here.


If you would like to train your own model, you will likely need to customize some of the data-loading code in voxelmorph/ for your own datasets and data formats. However, it is possible to run many of the example scripts out-of-the-box, assuming that you provide a list of filenames in the training dataset. Training data can be in the NIfTI, MGZ, or npz (numpy) format, and it's assumed that each npz file in your data list has a vol parameter, which points to the image data to be registered, and an optional seg variable, which points to a corresponding discrete segmentation (for semi-supervised learning). It's also assumed that the shape of all training image data is consistent, but this, of course, can be handled in a customized generator if desired.

For a given image list file /images/list.txt and output directory /models/output, the following script will train an image-to-image registration network (described in MICCAI 2018 by default) with an unsupervised loss. Model weights will be saved to a path specified by the --model-dir flag.

./scripts/tf/ --img-list /images/list.txt --model-dir /models/output --gpu 0

The --img-prefix and --img-suffix flags can be used to provide a consistent prefix or suffix to each path specified in the image list. Image-to-atlas registration can be enabled by providing an atlas file, e.g. --atlas atlas.npz. If you'd like to train using the original dense CVPR network (no diffeomorphism), use the --int-steps 0 flag to specify no flow integration steps. Use the --help flag to inspect all of the command line options that can be used to fine-tune network architecture and training.


If you simply want to register two images, you can use the script with the desired model file. For example, if we have a model model.h5 trained to register a subject (moving) to an atlas (fixed), we could run:

./scripts/tf/ --moving moving.nii.gz --fixed atlas.nii.gz --moved warped.nii.gz --model model.h5 --gpu 0

This will save the moved image to warped.nii.gz. To also save the predicted deformation field, use the --save-warp flag. Both npz or nifty files can be used as input/output in this script.

Testing (measuring Dice scores)

To test the quality of a model by computing dice overlap between an atlas segmentation and warped test scan segmentations, run:

./scripts/tf/ --model model.h5 --atlas atlas.npz --scans scan01.npz scan02.npz scan03.npz --labels labels.npz

Just like for the training data, the atlas and test npz files include vol and seg parameters and the labels.npz file contains a list of corresponding anatomical labels to include in the computed dice score.

Parameter choices

CVPR version

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.

MICCAI version

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.

Spatial transforms and integration

  • The spatial transform code, found at voxelmorph.layers.SpatialTransformer, accepts N-dimensional affine and dense transforms, including linear and nearest neighbor interpolation options. Note that original development of VoxelMorph used xy indexing, whereas we are now emphasizing ij indexing.

  • For the MICCAI2018 version, we integrate the velocity field using voxelmorph.layers.VecInt. By default we integrate using scaling and squaring, which we found efficient.

VoxelMorph papers

If you use VoxelMorph or some part of the code, please cite (see bibtex):


  • keywords: machine learning, convolutional neural networks, alignment, mapping, registration
  • data in papers: In our initial papers, we used publicly available data, but unfortunately we cannot redistribute it (due to the constraints of those datasets). We do a certain amount of pre-processing for the brain images we work with, to eliminate sources of variation and be able to compare algorithms on a level playing field. In particular, we perform FreeSurfer recon-all steps up to skull stripping and affine normalization to Talairach space, and crop the images via ((48, 48), (31, 33), (3, 29)).

We encourage users to download and process their own data. See a list of medical imaging datasets here. Note that you likely do not need to perform all of the preprocessing steps, and indeed VoxelMorph has been used in other work with other data.

Creation of deformable templates

To experiment with this method, please use for unconditional templates and for conditional templates, which use the same conventions as VoxelMorph (please note that these files are less polished than the rest of the VoxelMorph library).

We've also provided an unconditional atlas in data/generated_uncond_atlas.npz.npy.

Models in h5 format weights are provided for unconditional atlas here, and conditional atlas here.

Explore the atlases interactively here with tipiX!


SynthMorph is a strategy for learning registration without acquired imaging data, producing powerful networks agnostic to contrast induced by MRI (eprint arXiv:2004.10282). For a video and a demo showcasing the steps of generating random label maps from noise distributions and using these to train a network, visit

We provide model files for a "shapes" variant of SynthMorph, that we train using images synthesized from random shapes only, and a "brains" variant, that we train using images synthesized from brain label maps. We train the brains variant by optimizing a loss term that measures volume overlap of a selection of brain labels. For registration with either model, please use the script with the respective model weights.

Accurate registration requires the input images to be min-max normalized, such that voxel intensities range from 0 to 1, and to be resampled in the affine space of a reference image. The affine registration can be performed with a variety of packages, and we choose FreeSurfer. First, we skull-strip the images with SAMSEG, keeping brain labels only. Second, we run mri_robust_register:

mri_robust_register --mov in.nii.gz --dst out.nii.gz --lta transform.lta --satit --iscale
mri_robust_register --mov in.nii.gz --dst out.nii.gz --lta transform.lta --satit --iscale --ixform transform.lta --affine

where we replace --satit --iscale with --cost NMI for registration across MRI contrasts.


While we cannot release most of the data used in the VoxelMorph papers as they prohibit redistribution, we thorough processed and re-released OASIS1 while developing HyperMorph. We now include a quick VoxelMorph tutorial to train VoxelMorph on neurite-oasis data.


For any code-related problems or questions please open an issue or start a discussion of general registration/VoxelMorph topics.