Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

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

Robust Construction of Diffusion MRI Atlases with Correction for Inter-Subject Fiber Dispersion.

Computational diffusion MRI : MICCAI Workshop·2017
Same author

Robust Fusion of Diffusion MRI Data for Template Construction.

Scientific reports·2017
Same author

Learning-Based Multimodal Image Registration for Prostate Cancer Radiation Therapy.

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention·2017
Same author

Segmenting hippocampal subfields from 3T MRI with multi-modality images.

Medical image analysis·2017
Same author

Joint Discriminative and Representative Feature Selection for Alzheimer's Disease Diagnosis.

Machine learning in medical imaging. MLMI (Workshop)·2017
Same author

Single- and Multiple-Shell Uniform Sampling Schemes for Diffusion MRI Using Spherical Codes.

IEEE transactions on medical imaging·2017
Same journal

BrainCL: Transformer-Based Brain Network Contrastive Learning with Multi-Order Topology and Salience Masking.

IEEE transactions on medical imaging·2026
Same journal

LLM-enhanced Neuron Segmentation and Reconstruction in Complex Mouse Brain Images.

IEEE transactions on medical imaging·2026
Same journal

Matrixed-Spectrum Decomposition Accelerated Linear Boltzmann Transport Equation Solver for Fast Scatter Correction in Multi-Spectral CT.

IEEE transactions on medical imaging·2026
Same journal

The Ritz Adjoint Method for MRI Pulse Design.

IEEE transactions on medical imaging·2026
Same journal

Physiology-guided Self-supervised Learning for Simultaneous Dual-Tracer PET Separation.

IEEE transactions on medical imaging·2026
Same journal

Informed-Exploration Reinforcement Learning for Automated Virtual Coronary Intervention Planning.

IEEE transactions on medical imaging·2026
See all related articles

Related Experiment Video

Updated: Nov 11, 2025

Four-Dimensional CT Analysis Using Sequential 3D-3D Registration
05:05

Four-Dimensional CT Analysis Using Sequential 3D-3D Registration

Published on: November 23, 2019

8.2K

S3Reg: Superfast Spherical Surface Registration Based on Deep Learning.

Fenqiang Zhao, Zhengwang Wu, Fan Wang

    IEEE Transactions on Medical Imaging
    |March 30, 2021
    PubMed
    Summary
    This summary is machine-generated.

    We developed Superfast Spherical Surface Registration (S3Reg), a rapid deep learning framework for aligning brain cortical surfaces. S3Reg significantly accelerates neuroimaging analysis by efficiently handling multimodal data.

    More Related Videos

    Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
    04:48

    Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

    Published on: November 30, 2022

    3.1K
    Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
    03:31

    Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

    Published on: December 15, 2023

    753

    Related Experiment Videos

    Last Updated: Nov 11, 2025

    Four-Dimensional CT Analysis Using Sequential 3D-3D Registration
    05:05

    Four-Dimensional CT Analysis Using Sequential 3D-3D Registration

    Published on: November 23, 2019

    8.2K
    Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
    04:48

    Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

    Published on: November 30, 2022

    3.1K
    Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
    03:31

    Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

    Published on: December 15, 2023

    753

    Area of Science:

    • Neuroimaging
    • Computational Neuroscience
    • Medical Image Analysis

    Background:

    • Cortical surface registration is crucial for surface-based neuroimaging analysis, enabling cross-sectional and longitudinal studies.
    • Existing methods are often time-consuming and lack flexibility for multimodal or high-dimensional data.
    • The increasing volume of large-scale, multimodal brain MRI data necessitates faster and more adaptable registration techniques.

    Purpose of the Study:

    • To develop a Superfast Spherical Surface Registration (S3Reg) framework for the cerebral cortex.
    • To create a fast, flexible, and unsupervised deep learning method for multimodal cortical surface registration.
    • To significantly reduce registration time while maintaining or improving accuracy.

    Main Methods:

    • Utilized an end-to-end unsupervised learning strategy with a spherical Convolutional Neural Network (CNN).
    • Implemented a diffeomorphic design using "scaling and squaring" layers for topology-preserving deformations.
    • Employed three orthogonal Spherical U-Nets to address polar distortion in spherical space.

    Main Results:

    • S3Reg demonstrated superior or comparable performance against state-of-the-art methods on adult and infant multimodal cortical features.
    • Registration time was reduced from approximately 1 minute to 10 seconds.
    • The framework showed flexibility in handling diverse feature sets and similarity measures.

    Conclusions:

    • S3Reg offers a significant advancement in speed and flexibility for cortical surface registration.
    • The developed framework is well-suited for large-scale, multimodal neuroimaging studies.
    • This method facilitates more efficient cross-sectional and longitudinal analysis of brain structure.