An Unsupervised Learning Model for Deformable Medical Image Registration
Name
1802.02604.pdf
Description
Submitted version
Size
3.02 MB
Format
Adobe PDF
Checksum (MD5)
1fcbd9d27165b81090ff48460179e19c
Author(s)
Balakrishnan, Guha
Zhao, Amy
Sabuncu, Mert R.
Guttag, John
Dalca, Adrian V.
Date Issued
June 2018
Publisher
IEEE
Citation
Balakrishnan, Guha, Zhao, Amy, Sabuncu, Mert R., Guttag, John and Dalca, Adrian V. 2018. "An Unsupervised Learning Model for Deformable Medical Image Registration."
Version
Original manuscript
Abstract
© 2018 IEEE. We present a fast learning-based algorithm for deformable, pairwise 3D medical image registration. Current registration methods optimize an objective function independently for each pair of images, which can be time-consuming for large data. We define registration as a parametric function, and optimize its parameters given a set of images from a collection of interest. Given a new pair of scans, we can quickly compute a registration field by directly evaluating the function using the learned parameters. We model this function using a CNN, and use a spatial transform layer to reconstruct one image from another while imposing smoothness constraints on the registration field. The proposed method does not require supervised information such as ground truth registration fields or anatomical landmarks. We demonstrate registration accuracy comparable to state-of-the-art 3D image registration, while operating orders of magnitude faster in practice. Our method promises to significantly speed up medical image analysis and processing pipelines, while facilitating novel directions in learning-based registration and its applications. Our code is available at https://github.com/balakg/voxelmorph.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/cvpr.2018.00964
