Unsupervised learning of probabilistic diffeomorphic registration for images and surfaces
Name
1903.03545.pdf
Description
Accepted version
Size
2.52 MB
Format
Adobe PDF
Checksum (MD5)
1e8d4d99486c45f0e58cf8e2c4fe684d
Author(s)
Dalca, Adrian Vasile
Balakrishnan, Guha
Guttag, John V
Date Issued
October 2019
Journal
Medical Image Analysis
Publisher
Elsevier BV
Citation
Dalca, Adrian V. et al. “Unsupervised learning of probabilistic diffeomorphic registration for images and surfaces.” Medical Image Analysis, 57 (October 2019): 226-236 © 2019 The Author(s)
Version
Author's final manuscript
Abstract
Classical 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 build a connection between classical and learning-based methods. We present a probabilistic generative model and derive an unsupervised learning-based inference algorithm that uses insights from classical registration methods and makes use of recent developments in convolutional neural networks (CNNs). We demonstrate our method on a 3D brain registration task for both images and anatomical surfaces, and provide extensive empirical analyses of the algorithm. Our principled approach results in state of the art accuracy and very fast runtimes, while providing diffeomorphic guarantees. Our implementation is available online at http://voxelmorph.csail.mit.edu.
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-NoDerivs License
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1016/J.MEDIA.2019.07.006
