Nesterov accelerated ADMM for fast diffeomorphic image registration
File(s)
Accepted version
Author(s)
Type
Conference Paper
Abstract
Deterministic approaches using iterative optimisation have been historically successful in diffeomorphic image registration (DiffIR). Although these approaches are highly accurate, they typically carry a significant computational burden. Recent developments in stochastic approaches based on deep learning have achieved sub-second runtimes for DiffIR with competitive registration accuracy, offering a fast alternative to conventional iterative methods. In this paper, we attempt to reduce this difference in speed whilst retaining the performance advantage of iterative approaches in DiffIR. We first propose a simple iterative scheme that functionally composes intermediate non-stationary velocity fields to handle large deformations in images whilst guaranteeing diffeomorphisms in the resultant deformation. We then propose a convex optimisation model that uses a regularisation term of arbitrary order to impose smoothness on these velocity fields and solve this model with a fast algorithm that combines Nesterov gradient descent and the alternating direction method of multipliers (ADMM). Finally, we leverage the computational power of GPU to implement this accelerated ADMM solver on a 3D cardiac MRI dataset, further reducing runtime to less than 2 s. In addition to producing strictly diffeomorphic deformations, our methods outperform both state-of-the-art deep learning-based and iterative DiffIR approaches in terms of dice and Hausdorff scores, with speed approaching the inference time of deep learning-based methods.
Editor(s)
DeBruijne, M
Cattin, PC
Cotin, S
Padoy, N
Speidel, S
Zheng, Y
Essert, C
Date Issued
2021-09-21
Date Acceptance
2021-09-01
Citation
MEDICAL IMAGE COMPUTING AND COMPUTER ASSISTED INTERVENTION - MICCAI 2021, PT IVV, 2021, 12904, pp.150-160
ISBN
978-3-030-87201-4
ISSN
0302-9743
Publisher
SPRINGER INTERNATIONAL PUBLISHING AG
Start Page
150
End Page
160
Journal / Book Title
MEDICAL IMAGE COMPUTING AND COMPUTER ASSISTED INTERVENTION - MICCAI 2021, PT IVV
Volume
12904
Copyright Statement
© Springer Nature Switzerland AG 2021. The final publication is available at Springer via https://doi.org/10.1007/978-3-030-87202-1_15
Sponsor
The Academy of Medical Sciences
Imperial College Healthcare NHS Trust- BRC Funding
Imperial College Healthcare NHS Trust- BRC Funding
British Heart Foundation
Identifier
http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000712021400015&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
Grant Number
SGL015/1006
RDC04
RDB02
RG/19/6/34387
Source
International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI)
Subjects
Science & Technology
Technology
Life Sciences & Biomedicine
Computer Science, Artificial Intelligence
Computer Science, Software Engineering
Engineering, Biomedical
Imaging Science & Photographic Technology
Radiology, Nuclear Medicine & Medical Imaging
Surgery
Computer Science
Engineering
Image registration
Diffeomorphism
ADMM
FRAMEWORK
Publication Status
Published
Start Date
2021-09-27
Finish Date
2021-10-01
Coverage Spatial
ELECTR NETWORK
Date Publish Online
2021-09-21