VoxelMorph: A Learning Framework for Deformable Medical Image Registration
Name
1809.05231.pdf
Description
Accepted version
Size
8.93 MB
Format
Adobe PDF
Checksum (MD5)
06b03e224e2261c61227bf88e07a6ab6
Author(s)
Balakrishnan, Guha
Zhao, Amy (Xiaoyu Amy)
Sabuncu, Mert R
Guttag, John V
Dalca, Adrian Vasile
Date Issued
August 2019
Journal
IEEE Transactions on Medical Imaging
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Balakrishnan, Guha et al. “VoxelMorph: A Learning Framework for Deformable Medical Image Registration.” IEEE Transactions on Medical Imaging, 38, 8 (August 2019): 1788 - 1800 © 2019 The Author(s)
Version
Author's final manuscript
Abstract
We present VoxelMorph, a fast learning-based framework for deformable, pairwise medical image registration. Traditional registration methods optimize an objective function for each pair of images, which can be time-consuming for large datasets or rich deformation models. In contrast to this approach and building on recent learning-based methods, we formulate registration as a function that maps an input image pair to a deformation field that aligns these images. We parameterize the function via a convolutional neural network and optimize the parameters of the neural network on a set of images. Given a new pair of scans, VoxelMorph rapidly computes a deformation field by directly evaluating the function. In this paper, we explore two different training strategies. In the first (unsupervised) setting, we train the model to maximize standard image matching objective functions that are based on the image intensities. In the second setting, we leverage auxiliary segmentations available in the training data. We demonstrate that the unsupervised model's accuracy is comparable to the state-of-the-art methods while operating orders of magnitude faster. We also show that VoxelMorph trained with auxiliary data improves registration accuracy at test time and evaluate the effect of training set size on registration. Our method promises to speed up medical image analysis and processing pipelines while facilitating novel directions in learning-based registration and its applications. Our code is freely available at https://github.com/voxelmorph/voxelmorph.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://doi.org/10.1109/TMI.2019.2897538
