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

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

Maternal gut microbiota and placenta-derived tissues microbes are important for initial gut microbial colonization in infants.

Frontiers in microbiology·2025

Same author

Deep learning radiomics and <sup>18</sup>F-FDG PET/CT imaging: mediastinal lymph node characteristics as predictors of metastasis in non-small cell lung cancer.

Translational lung cancer research·2025

Same author

Plant and Animal-Based Dietary Patterns and Cardiometabolic Diseases in the Brazilian Population: Cross-Sectional Analysis of the Brazilian National Health Survey.

Nutrients·2025

Same author

Efficient multi-objective optimization for switchable digital coding metasurface absorber empowered by machine learning.

Optics express·2025

Same author

Beyond Energy Fuel: Ketone Bodies as Multifaceted Modulators of T Cell and Anti-Tumour Immunity.

Scandinavian journal of immunology·2025

Same author

A Meta-Analysis of the Impact of Mindfulness-Based Stress Reduction Therapy Intervention on Post-traumatic Stress Disorder.

Cureus·2025

Search research articles

Related Experiment Video

Updated: Feb 27, 2026

Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images

Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images

Published on: January 7, 2019

Automatic Thalamus Segmentation from Magnetic Resonance Images Using Multiple Atlases Level Set Framework (MALSF).

Minghui Zhang¹, Zhentai Lu², Qianjin Feng¹

¹Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, 510515, China.

Scientific Reports

|June 29, 2017

Summary

This study introduces a novel Multiple Atlases Level Set Framework (MALSF) for accurate automatic thalamus segmentation in MRI scans. The MALSF method significantly improves segmentation accuracy, achieving high Dice metrics for both left and right thalami.

More Related Videos

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

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

Published on: June 9, 2018

A Comprehensive Protocol for Manual Segmentation of the Medial Temporal Lobe Structures

A Comprehensive Protocol for Manual Segmentation of the Medial Temporal Lobe Structures

Published on: July 2, 2014

Related Experiment Videos

Last Updated: Feb 27, 2026

Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images

Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images

Published on: January 7, 2019

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

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

Published on: June 9, 2018

A Comprehensive Protocol for Manual Segmentation of the Medial Temporal Lobe Structures

A Comprehensive Protocol for Manual Segmentation of the Medial Temporal Lobe Structures

Published on: July 2, 2014

Area of Science:

Medical Imaging
Computational Neuroscience
Biomedical Engineering

Background:

Accurate segmentation of subcortical structures like the thalamus is crucial for neurological studies.
Existing segmentation methods often struggle with accuracy and robustness in Magnetic Resonance Images (MRI).

Purpose of the Study:

To develop an automatic, accurate, and robust framework for thalamus segmentation in MRI.
To introduce a novel label fusion strategy within a level set framework.

Main Methods:

Proposed the Multiple Atlases Level Set Framework (MALSF).
Implemented a novel label fusion strategy minimizing an energy functional (label fusion, image-based, regularization terms).
Integrated information from multiple registration methods and atlases to leverage complementary data.

Main Results:

The MALSF method demonstrated improved segmentation accuracy for the thalamus.
Achieved mean Dice metrics of 0.9239 for the left thalamus and 0.9200 for the right thalamus compared to ground truth.

Conclusions:

The MALSF framework offers a robust and accurate solution for automatic thalamus segmentation.
The novel label fusion strategy and integration of multiple registration/atlases enhance segmentation performance.