voxelmorph

Unsupervised Learning with CNNs for Image Registration
We incorporate several variants, presented at CVPR2018 (initial unsupervised learning) and MICCAI2018 (probabilistic & diffeomorphic)

Instructions

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/

Training:

These instructions are for the MICCAI2018 paper. If you'd like the CVPR version (no diffeomorphism or uncertainty measures and using CC) use train.py

1. Change the top parameters in train_miccai2018.py to the location of your image files.
2. Run train_miccai2018.py with options described in the main function. Example:

train_miccai2018.py --gpu 0 --model_dir /my/path/to/models 

Testing (measuring Dice scores):

1. Put test filenames in data/test_examples.txt, and anatomical labels in data/test_labels.mat.
2. Run test_miccai2018.py [gpu-id] [model_dir] [iter-num]

Notes

• We provide a T1 atlas used in our papers at data/atlas_norm.npz.

• 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 used xy indexing, whereas we are now emphasizing ij 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.

• 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.

Papers

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

Contact:

For and problems or questions please open an issue in github or email us at [email protected]