MDReg-Net: Multi-resolution diffeomorphic image registration using fully convolutional networks with deep self-supervision

by   Hongming Li, et al.

We present a diffeomorphic image registration algorithm to learn spatial transformations between pairs of images to be registered using fully convolutional networks (FCNs) under a self-supervised learning setting. The network is trained to estimate diffeomorphic spatial transformations between pairs of images by maximizing an image-wise similarity metric between fixed and warped moving images, similar to conventional image registration algorithms. It is implemented in a multi-resolution image registration framework to optimize and learn spatial transformations at different image resolutions jointly and incrementally with deep self-supervision in order to better handle large deformation between images. A spatial Gaussian smoothing kernel is integrated with the FCNs to yield sufficiently smooth deformation fields to achieve diffeomorphic image registration. Particularly, spatial transformations learned at coarser resolutions are utilized to warp the moving image, which is subsequently used for learning incremental transformations at finer resolutions. This procedure proceeds recursively to the full image resolution and the accumulated transformations serve as the final transformation to warp the moving image at the finest resolution. Experimental results for registering high resolution 3D structural brain magnetic resonance (MR) images have demonstrated that image registration networks trained by our method obtain robust, diffeomorphic image registration results within seconds with improved accuracy compared with state-of-the-art image registration algorithms.


page 9

page 14

page 16


Non-Rigid Image Registration Using Self-Supervised Fully Convolutional Networks without Training Data

A novel non-rigid image registration algorithm is built upon fully convo...

BIRNet: Brain Image Registration Using Dual-Supervised Fully Convolutional Networks

In this paper, we propose a deep learning approach for image registratio...

CLAIRE – Parallelized Diffeomorphic Image Registration for Large-Scale Biomedical Imaging Applications

We study the performance of CLAIRE – a diffeomorphic multi-node, multi-G...

SuperWarp: Supervised Learning and Warping on U-Net for Invariant Subvoxel-Precise Registration

In recent years, learning-based image registration methods have graduall...

Closed-loop Feedback Registration for Consecutive Images of Moving Flexible Targets

Advancement of imaging techniques enables consecutive image sequences to...

A training-free recursive multiresolution framework for diffeomorphic deformable image registration

Diffeomorphic deformable image registration is one of the crucial tasks ...

NeurEPDiff: Neural Operators to Predict Geodesics in Deformation Spaces

This paper presents NeurEPDiff, a novel network to fast predict the geod...

Please sign up or login with your details

Forgot password? Click here to reset