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

Denoising Diffusion-Weighted Images Using Grouped Iterative Hard Thresholding of Multi-Channel Framelets.

Computational diffusion MRI : MICCAI Workshop·2017
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

Related Experiment Video

Updated: Jun 10, 2026

Whole-brain Segmentation and Change-point Analysis of Anatomical Brain MRI—Application in Premanifest Huntington's Disease
09:06

Whole-brain Segmentation and Change-point Analysis of Anatomical Brain MRI—Application in Premanifest Huntington's Disease

Published on: June 9, 2018

NEONATAL BRAIN MRI SEGMENTATION BY BUILDING MULTI-REGION-MULTI-REFERENCE ATLASES.

Feng Shi1, Pew-Thian Yap, Yong Fan

  • 1IDEA Lab, Department of Radiology and BRIC, University of North Carolina at Chapel Hill.

Proceedings. IEEE International Symposium on Biomedical Imaging
|July 17, 2010
PubMed
Summary

This study introduces a new multi-region, multi-reference strategy for building brain atlases to improve neonatal brain MRI segmentation. This method enhances structural variability, leading to more accurate gray and white matter segmentation.

More Related Videos

Whole-Brain Single-Cell Imaging and Analysis of Intact Neonatal Mouse Brains Using MRI, Tissue Clearing, and Light-Sheet Microscopy
08:49

Whole-Brain Single-Cell Imaging and Analysis of Intact Neonatal Mouse Brains Using MRI, Tissue Clearing, and Light-Sheet Microscopy

Published on: August 1, 2022

Manual Segmentation of the Human Choroid Plexus Using Brain MRI
04:25

Manual Segmentation of the Human Choroid Plexus Using Brain MRI

Published on: December 15, 2023

Related Experiment Videos

Last Updated: Jun 10, 2026

Whole-brain Segmentation and Change-point Analysis of Anatomical Brain MRI—Application in Premanifest Huntington's Disease
09:06

Whole-brain Segmentation and Change-point Analysis of Anatomical Brain MRI—Application in Premanifest Huntington's Disease

Published on: June 9, 2018

Whole-Brain Single-Cell Imaging and Analysis of Intact Neonatal Mouse Brains Using MRI, Tissue Clearing, and Light-Sheet Microscopy
08:49

Whole-Brain Single-Cell Imaging and Analysis of Intact Neonatal Mouse Brains Using MRI, Tissue Clearing, and Light-Sheet Microscopy

Published on: August 1, 2022

Manual Segmentation of the Human Choroid Plexus Using Brain MRI
04:25

Manual Segmentation of the Human Choroid Plexus Using Brain MRI

Published on: December 15, 2023

Area of Science:

  • Medical Imaging
  • Neuroscience
  • Computer Vision

Background:

  • Neonatal brain MRI segmentation is difficult due to poor image quality.
  • Current population atlases average images, losing crucial local structural variability.
  • This limits the accuracy of automated segmentation methods.

Purpose of the Study:

  • To propose a novel multi-region-multi-reference strategy for neonatal brain atlas construction.
  • To improve the accuracy of brain tissue segmentation in low-quality neonatal MRI scans.
  • To preserve and utilize local inter-subject structural variability in atlas building.

Main Methods:

  • The brain is parcellated into anatomical regions.
  • Images within each region are classified into sub-populations.
  • Exemplars from sub-populations serve as regional references for atlas generation.
  • A joint registration-segmentation strategy is used for final tissue segmentation.

Main Results:

  • The proposed atlas achieved high overlap rates with manual segmentation: 0.86 (SD 0.02) for gray matter (GM) and 0.83 (SD 0.03) for white matter (WM).
  • The method significantly outperforms existing atlas-based segmentation approaches.
  • Demonstrated improved segmentation accuracy despite challenging neonatal MRI data.

Conclusions:

  • The multi-region-multi-reference atlas building strategy effectively addresses challenges in neonatal brain MRI segmentation.
  • This approach enhances the preservation of local structural variability, leading to superior segmentation performance.
  • The proposed method offers a promising advancement for analyzing neonatal brain development and pathology.