A variational Bayesian method for similarity learning in non-rigid image registration
File(s)
Author(s)
Type
Conference Paper
Abstract
We propose a novel variational Bayesian formulation for diffeomorphic non-rigid registration of medical images, which learns in an unsupervised way a data-specific similarity metric. The proposed framework is general and may be used together with many existing image registration models. We evaluate it on brain MRI scans from the UK Biobank and show that use of the learnt similarity metric, which is parametrised as a neural network, leads to more accurate results than use of traditional functions, e.g. SSD and LCC, to which we initialise the model, without a negative impact on image registration speed or transformation smoothness. In addition, the method estimates the uncertainty associated with the transformation. The code and the trained models are available in a public repository: https://github.com/dgrzech/learnsim.
Date Issued
2022-09-27
Date Acceptance
2022-06-01
Citation
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022, pp.119-128
ISBN
978-1-6654-6946-3
ISSN
1063-6919
Publisher
IEEE Computer Society
Start Page
119
End Page
128
Journal / Book Title
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Volume
2022-June
Copyright Statement
Copyright © 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Identifier
https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000867754200014&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=a2bf6146997ec60c407a63945d4e92bb
Source
IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Subjects
Computer Science
Computer Science, Artificial Intelligence
FRAMEWORK
Imaging Science & Photographic Technology
Science & Technology
Technology
Publication Status
Published
Start Date
2022-06-18
Finish Date
2022-06-24
Coverage Spatial
New Orleans, LA, USA