Learning conditional deformable templates with convolutional networks
Name
NeurIPS-2019-learning-conditional-deformable-templates-with-convolutional-networks-Paper.pdf
Description
Published version
Size
1.4 MB
Format
Adobe PDF
Checksum (MD5)
c78ca01b997277485d24f1b1c9e497bd
Author(s)
Dalca, Adrian Vasile
Rakic, Marianne
Guttag, John V
Sabuncu, Mert R
Date Issued
December 2019
Journal
Advances in Neural Information Processing Systems
Citation
Dalca, Adrian V. et al. “Learning conditional deformable templates with convolutional networks.” Advances in Neural Information Processing Systems, 32 (December 2019) © 2019 The Author(s)
Version
Final published version
Abstract
We develop a learning framework for building deformable templates, which play a fundamental role in many image analysis and computational anatomy tasks. Conventional methods for template creation and image alignment to the template have undergone decades of rich technical development. In these frameworks, templates are constructed using an iterative process of template estimation and alignment, which is often computationally very expensive. Due in part to this shortcoming, most methods compute a single template for the entire population of images, or a few templates for specific sub-groups of the data. In this work, we present a probabilistic model and efficient learning strategy that yields either universal or conditional templates, jointly with a neural network that provides efficient alignment of the images to these templates. We demonstrate the usefulness of this method on a variety of domains, with a special focus on neuroimaging. This is particularly useful for clinical applications where a pre-existing template does not exist, or creating a new one with traditional methods can be prohibitively expensive. Our code and atlases are available online as part of the VoxelMorph library 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
Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
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DOI of Published Version
https://papers.nips.cc/paper/2019/hash/bbcbff5c1f1ded46c25d28119a85c6c2-Abstract.html
