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

Enhanced in vitro cytotoxicity of G-rich ssDNA-templated Cu nanoclusters with multienzyme-like activity and glutathione depletion capacity.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy·2026

Same author

Metabolomic Signatures of Brain Atrophy and Ibudilast Response in Progressive Multiple Sclerosis.

medRxiv : the preprint server for health sciences·2026

Same author

Diffusion-relaxation MRI as virtual histology: separable microstructural signatures of AD pathology in ex vivo human brain.

Research square·2026

Same author

Proteomic Age Acceleration in Multiple Sclerosis Precedes Symptom Onset and Associates with Severity.

medRxiv : the preprint server for health sciences·2026

Same author

Accelerometry-derived activity fragmentation as a predictor of brain atrophy and disability progression in multiple sclerosis.

Multiple sclerosis (Houndmills, Basingstoke, England)·2026

Same author

Separable multidimensional MRI signatures of cellular and structural pathology in Alzheimer's disease.

bioRxiv : the preprint server for biology·2026

Same journal

MetaboNet-Bench: A Multi-modal Benchmark for Glucose Forecasting in Type 1 Diabetes.

ArXiv·2026

Same journal

A Positron Range Correction with Texture Preservation Framework in PET Imaging.

ArXiv·2026

Same journal

Automated optimization of force field parameters against ensemble-averaged measurements with Bayesian Inference of Conformational Populations.

ArXiv·2026

Same journal

Droplet Fusion as a Relaxation Process: Comparison with Shape Recovery of Newtonian and Viscoelastic Droplets.

ArXiv·2026

Same journal

Ridge-filter crosstalk in conformal proton FLASH planning: dependence on beamlet pitch and iterative mitigation.

ArXiv·2026

Same journal

Electrochemical DNA Hairpin Sensors for Differentiating Small Molecule Intercalation from Minor Groove Binding.

ArXiv·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jan 9, 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

CATNUS: Coordinate-Aware Thalamic Nuclei Segmentation Using T1-Weighted MRI.

Anqi Feng^1,2, Zhangxing Bian¹, Samuel W Remedios³

¹Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD, USA.

|December 11, 2025

Summary

This summary is machine-generated.

Accurate segmentation of thalamic nuclei is crucial for understanding brain function and disorders. CATNUS, a novel deep learning method, achieves precise and reliable segmentation across diverse MRI data, aiding neuroimaging research and clinical applications.

Keywords:

Coordinate Convolution Quantitative T1 Mapping Thalamic Nuclei Segmentation

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: Jan 9, 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:

Neuroimaging
Medical Image Analysis
Computational Neuroscience

Background:

Thalamic nuclei segmentation is vital for understanding brain function and neurological disorders.
Challenges include small nuclei size, low contrast, and anatomical variability.
Accurate segmentation is essential for large-scale neuroimaging studies and clinical assessment.

Purpose of the Study:

To develop an accurate and generalizable method for segmenting 13 thalamic nuclei.
To improve upon existing segmentation techniques for thalamic nuclei.
To provide a tool applicable to diverse MRI acquisition protocols.

Main Methods:

Developed CATNUS (Coordinate-Aware Thalamic Nuclei Segmentation), a 3D U-Net architecture with coordinate convolutions.
Trained and validated models on T1 maps, MPRAGE, and FGATIR sequences.
Benchmarked against FreeSurfer, THOMAS, and HIPS-THOMAS.

Main Results:

CATNUS demonstrated improved segmentation accuracy and test-retest reliability compared to established methods.
Achieved robust out-of-distribution generalization across multiple scanners, field strengths, and vendors.
Produced reliable and anatomically coherent segmentations on diverse datasets.

Conclusions:

CATNUS offers an accurate and generalizable solution for thalamic nuclei segmentation.
The method shows strong potential for advancing neuroimaging research and clinical practice.
Facilitates large-scale studies and real-world clinical assessment of thalamic nuclei.