VoxelMorph: A Learning Framework for Deformable Medical Image Registration
Massachusetts Institute of Technology · Cornell University · +1 more institution
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 (CNN), 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…
Citation impact
- FWCI
- 66.95
- Percentile
- 100%
- References
- 68
Authors
5- GBGuha BalakrishnanCorresponding
Massachusetts Institute of Technology
- AZAmy Zhao
Massachusetts Institute of Technology
- MRMert R. Sabuncu
Cornell University
- JGJohn Guttag
Massachusetts Institute of Technology
- AVAdrian V. Dalca
Athinoula A. Martinos Center for Biomedical Imaging
Topics & keywords
- Image registration
- Pairwise comparison
- Leverage (statistics)
- Convolutional neural network
- Matching (statistics)
- Medical imaging
- Code (set theory)
- Pattern recognition (psychology)