Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Search research articles

Related Experiment Video

Updated: May 29, 2026

Neuroimaging-Guided TMS–EEG for Real-Time Cortical Network Mapping

Neuroimaging-Guided TMS–EEG for Real-Time Cortical Network Mapping

Published on: June 13, 2025

Correlation-Guided Recursive Pyramid Network for Deformable Brain MRI Registration.

Weihao Zhang, Yu Liu

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society

|May 27, 2026

Summary

This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Application of failure mode and effects analysis combined with bundled management measures in reducing ICU-acquired infections.

Frontiers in medicine·2026

Same author

Modulatory effects of exercise interventions on the gut microbiota in older adults: a systematic review and meta-analysis.

The journal of nutrition, health & aging·2026

Same author

Clinical outcomes and prognostic factors of systemic therapy combined with interventional treatment in hepatocellular carcinoma: a multicenter retrospective case series.

Translational cancer research·2026

Same author

UHRF1-mediated DNA 5-mC modification drives super-enhancer redistribution and impedes osteogenesis via TGM2-regulated autophagic flux in senile osteoporosis.

Journal of advanced research·2026

Same author

Identification of nodule number-related loci and the candidate gene GmbHLH135 in soybean under low phosphorus stress.

TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik·2026

Same author

Hydrophobic carbon dots as spatial confiners for isolating Pd nanoparticles for efficient nitrobenzene hydrogenation.

Chemical communications (Cambridge, England)·2026

Same journal

Hyperbolic Cycle Alignment for Infrared-Visible Image Fusion.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026

Same journal

Learning Gaze Synthesizer via 3D-eye Controlled Diffusion and Cross-domain Feature Alignment.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026

Same journal

Underlying Semantic Diffusion for Effective and Efficient In-Context Learning.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026

Same journal

DiffRES: Unleashing Text-to-Image Diffusion Models for Generative Referring Expression Segmentation without Information Leakage.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026

Same journal

Location Matters: Frequency-Spatial Dual Space Adaptation for Cross-Domain Few-Shot Segmentation.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026

Same journal

BayeTopo: Bayesian-based Topology-guided Learning for Vascular Imaging Segmentation.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026

See all related articles

This study introduces the Correlation-Guided Recursive Pyramid Network (CRPNet) for medical image analysis. CRPNet enhances deformable image registration by unifying large deformation handling and accurate feature matching, achieving state-of-the-art results.

Area of Science:

Medical Image Analysis
Deep Learning
Computer Vision

Background:

Deformable image registration is crucial for medical image analysis.
Deep learning methods are now mainstream but struggle with large deformations and accurate feature matching.
Existing pyramid architectures have unbalanced focuses, either on coarse feature interactions or unreliable static matching.

Purpose of the Study:

To propose a novel network, CRPNet, that unifies handling large deformations and accurate feature matching in deformable image registration.
To address the limitations of existing methods by embedding explicit correlation modeling into recursive optimization.
To improve the robustness and accuracy of medical image registration, particularly for brain MRI.

Main Methods:

Developed the Correlation-Guided Recursive Pyramid Network (CRPNet).

More Related Videos

Multi-modal Pulmonary Imaging: Using Complementary Information from CT and Hyperpolarized 129Xe MRI to Evaluate Lung Structure-Function

Multi-modal Pulmonary Imaging: Using Complementary Information from CT and Hyperpolarized ¹²⁹Xe MRI to Evaluate Lung Structure-Function

Published on: April 12, 2024

Related Experiment Videos

Last Updated: May 29, 2026

Neuroimaging-Guided TMS–EEG for Real-Time Cortical Network Mapping

Neuroimaging-Guided TMS–EEG for Real-Time Cortical Network Mapping

Published on: June 13, 2025

Multi-modal Pulmonary Imaging: Using Complementary Information from CT and Hyperpolarized 129Xe MRI to Evaluate Lung Structure-Function

Multi-modal Pulmonary Imaging: Using Complementary Information from CT and Hyperpolarized ¹²⁹Xe MRI to Evaluate Lung Structure-Function

Published on: April 12, 2024

Introduced a Correlation-Guided Intra-layer Recursive Strategy (CGIRS) for continuous refinement and error prevention.

Designed Spatial Correlation Module (SPCM) and Semantic Correlation Module (SECM) for correspondence and alignment.

Main Results:

Achieved state-of-the-art performance on three brain imaging datasets.
Demonstrated exceptional robustness under extreme deformations.
Validated the efficacy of CRPNet for deformable brain MRI registration.

Conclusions:

CRPNet effectively bridges the gap between handling large deformations and achieving accurate feature matching.
The proposed CGIRS, SPCM, and SECM modules contribute to robust and accurate deformable image registration.
CRPNet represents a significant advancement in medical image analysis, particularly for brain MRI applications.