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Unsupervised Learning for Fast Probabilistic Diffeomorphic Registration
Name
1805.04605.pdf
Description
Accepted version
Size
1.17 MB
Format
Adobe PDF
Checksum (MD5)
903a1395fba5f1783c3eb0ea7b74a291
Author(s)
Dalca, Adrian V.
Balakrishnan, Guha
Guttag, John
Sabuncu, Mert R.
Date Issued
2018
Publisher
Springer International Publishing
Citation
Dalca, Adrian V., Balakrishnan, Guha, Guttag, John and Sabuncu, Mert R. 2018. "Unsupervised Learning for Fast Probabilistic Diffeomorphic Registration."
Version
Author's final manuscript
Abstract
© Springer Nature Switzerland AG 2018. Traditional deformable registration techniques achieve impressive results and offer a rigorous theoretical treatment, but are computationally intensive since they solve an optimization problem for each image pair. Recently, learning-based methods have facilitated fast registration by learning spatial deformation functions. However, these approaches use restricted deformation models, require supervised labels, or do not guarantee a diffeomorphic (topology-preserving) registration. Furthermore, learning-based registration tools have not been derived from a probabilistic framework that can offer uncertainty estimates. In this paper, we present a probabilistic generative model and derive an unsupervised learning-based inference algorithm that makes use of recent developments in convolutional neural networks (CNNs). We demonstrate our method on a 3D brain registration task, and provide an empirical analysis of the algorithm. Our approach results in state of the art accuracy and very fast runtimes, while providing diffeomorphic guarantees and uncertainty estimates. Our implementation is available online at http://voxelmorph.csail.mit.edu.
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
10.1007/978-3-030-00928-1_82
