voxelmorph: Learning-Based Image Registration

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

Tutorial

Visit the VoxelMorph tutorial to learn about VoxelMorph and Learning-based Registration

Instructions

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

pip install voxelmorph

Training

If you would like to train your own model, you will likely need to customize some of the data loading code in voxelmorph/generators.py 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 have a directory containing training data files in npz (numpy) format. It's assumed that each npz file in your data folder has a vol parameter, which points to the numpy 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 image data in a directory is consistent.

For a given /path/to/training/data, the following script will train the dense network (described in MICCAI 2018 by default) using scan-to-scan registration. Model weights will be saved to a path specified by the --model-dir flag.

./scripts/tf/train.py /path/to/training/data --model-dir /path/to/models/output --gpu 0

Scan-to-atlas registration can be enabled by providing an atlas file with the --atlas atlas.npz command line flag. 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.

Registration

If you simply want to register two images, you can use the register.py 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/register.py --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/test.py --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):

• For the atlas formation model:

  Learning Conditional Deformable Templates with Convolutional Networks
  Adrian V. Dalca, Marianne Rakic, John Guttag, Mert R. Sabuncu
  NeurIPS 2019. eprint arXiv:1908.02738

• For the diffeomorphic or probabilistic model:

  Unsupervised Learning of Probabilistic Diffeomorphic Registration for Images and Surfaces
  Adrian V. Dalca, Guha Balakrishnan, John Guttag, Mert R. Sabuncu
  MedIA: Medial Image Analysis. 2019. eprint arXiv:1903.03545

  Unsupervised Learning for Fast Probabilistic Diffeomorphic Registration
  Adrian V. Dalca, Guha Balakrishnan, John Guttag, Mert R. Sabuncu
  MICCAI 2018. eprint arXiv:1805.04605

• For the original CNN model, MSE, CC, or segmentation-based losses:

  VoxelMorph: A Learning Framework for Deformable Medical Image Registration
  Guha Balakrishnan, Amy Zhao, Mert R. Sabuncu, John Guttag, Adrian V. Dalca
  IEEE TMI: Transactions on Medical Imaging. 2019. eprint arXiv:1809.05231

  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

Notes:

• keywords: machine learning, convolutional neural networks, alignment, mapping, registration
• The master branch is still in testing as we roll out a major refactoring of the library.
• If you'd like to run code from VoxelMorph publications, please use the legacy branch.
• 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

We present a template consturction method in this preprint:

• Learning Conditional Deformable Templates with Convolutional Networks
  Adrian V. Dalca, Marianne Rakic, John Guttag, Mert R. Sabuncu NeurIPS 2019. eprint arXiv:1908.02738

To experiment with this method, please use train_template.py for unconditional templates and train_cond_template.py 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!

Contact:

For any problems or questions please open an issue in github.