Diffeomorphic Image Registration using Lipschitz Continuous Residual Networks.Download PDF

10 Dec 2021, 15:56 (modified: 22 Jun 2022, 18:47)MIDL 2022Readers: Everyone
Keywords: Diffeomorphic image registration, residual networks, time dependent and stationary velocities.
Abstract: Image registration is an essential task in medical image analysis.We propose two novel unsupervised diffeomorphic image registration networks, which use deep Residual Networks(ResNets) as numerical approximations of the underlying continuous diffeomorphic setting governed by ordinary differential equations (ODEs), viewed as an Eulerian discretization scheme. While considering the ODE-based parameterizations of diffeomorphisms, we consider both stationary and non-stationary (time varying) velocity fields as the driving velocities to solve the ODEs, which gives rise to our two proposed architectures for diffeomorphic registration. We also employ Lipschitz-continuity on the Residual Networks in both architectures to define the admissible Hilbert space of velocity fields as a Reproducing Kernel Hilbert Spaces (RKHS) and regularize the smoothness of the velocity fields. We apply both registration networks to align and segment the OASIS brain MRI dataset.
Registration: I acknowledge that publication of this at MIDL and in the proceedings requires at least one of the authors to register and present the work during the conference.
Authorship: I confirm that I am the author of this work and that it has not been submitted to another publication before.
Paper Type: both
Primary Subject Area: Image Registration
Secondary Subject Area: Application: Other
Confidentiality And Author Instructions: I read the call for papers and author instructions. I acknowledge that exceeding the page limit and/or altering the latex template can result in desk rejection.
Code And Data: Currently, the authors are in preparation of an extended Journal version for this paper. The source code for this work will be made available then.
5 Replies

Loading