voxelmorph

Unsupervised Learning with CNNs for Image Registration
This repository incorporates several variants, first presented at CVPR2018 (initial unsupervised learning) and then MICCAI2018 (probabilistic & diffeomorphic formulation)

Instructions

Setup

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.

Training

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.

Testing (measuring Dice scores)

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]

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 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.

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

Significant Updates

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

Contact:

For and problems or questions please open an issue in github or email us at voxelmorph@mit.edu

lalithnag/voxelmorph