An Unsupervised End-to-End Recursive Cascaded Parallel Network for Image Registration

Deformable image registration can align two images acquired at different times or under different conditions, and thus has important theoretical research and clinical application value. However, existing learning-based registration methods are typically narrowly restricted to small displacement defo...

Full description

Saved in:
Bibliographic Details
Published in:Neural processing letters 2023-12, Vol.55 (6), p.8255-8268
Main Authors: Wang, Longjian, Shao, Haijian, Deng, Xing
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Artificial Intelligence
Complex Systems
Computational Intelligence
Computer Science
Coordinate transformations
Deep learning
Deformation
Design
Formability
Image acquisition
Image enhancement
Image registration
Medical imaging
Methods
Neural networks
Optimization
Registration
Representations
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Deformable image registration can align two images acquired at different times or under different conditions, and thus has important theoretical research and clinical application value. However, existing learning-based registration methods are typically narrowly restricted to small displacement deformations. So to overcome this difficulty, we propose a recursive cascaded parallel network for image registration (RCPN). RCPN optimizes the image registration performance in a coarse-to-fine manner by multiple cascades to incrementally enhance the registration of target image pairs. In addition, the cascaded subnetwork always maintains a high-resolution representation. It connects multi-resolution streams in parallel, thus strengthening the feature representation capability in a finite perceptual field, which allows the registration network focus more on the small displacement deformations beyond the large displacement deformations of the image in space and enhances the registration capability. The experiments based on standard MRI images of the brain indicate that RCPN has significantly improved the registration performance compared with the benchmark method, and the effectiveness of this method can be effectively verified by combining other comparative results.
ISSN:1370-4621
1573-773X
DOI:10.1007/s11063-023-11311-3