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

Magnetic Resonance Imaging

Magnetic Resonance Imaging

Magnetic resonance imaging (MRI) is a noninvasive medical imaging technique based on a phenomenon of nuclear physics discovered in the 1930s, in which matter exposed to magnetic fields and radio waves was found to emit radio signals. In 1970, a physician and researcher named Raymond Damadian noticed that malignant (cancerous) tissue gave off different signals than normal body tissue. He applied for a patent for the first MRI scanning device in clinical use by the early 1980s. The early MRI...

Imaging Studies for Cardiovascular System IV: CMRI

Imaging Studies for Cardiovascular System IV: CMRI

Cardiovascular magnetic resonance imaging, or CMRI, is a non-invasive diagnostic test that employs a magnetic field and radiofrequency waves to create precise images of the heart and arteries. It provides comprehensive information about cardiac anatomy, function, perfusion, and tissue characterization without ionizing radiation.IndicationsCMRI diagnoses various heart conditions, including tissue damage from heart attacks, ischemic heart disease, myocarditis, aortic issues (tears, aneurysms,...

Imaging Studies IV: Magnetic Resonance Imaging

Imaging Studies IV: Magnetic Resonance Imaging

Introduction:Magnetic Resonance Imaging, or MRI, can include a specialized imaging technique of the urinary system known as Magnetic Resonance Urography (MRU). This radiation-free technique uses strong magnetic fields and radio waves to produce detailed images with the help of a computer. MRU is particularly effective for visualizing fluid-filled structures like the kidneys, ureters, and bladder.Applications of MRI in the Genitourinary SystemKidneys and Ureters: MRI detects tumors, cysts,...

You might also read

Related Articles

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

Sort by

Same author

Optical Coherence Tomography Harmonization with Anatomy-Guided Latent Metric Schrödinger Bridges.

Advances in neural information processing systems·2026

Same author

Optical Coherence Tomography Harmonization via Dual Diffusion Implicit Bridges.

Proceedings of SPIE--the International Society for Optical Engineering·2026

Same author

A scoping review of influenza RdRp-targeting inhibitors: mechanisms, clinical translation, and emerging challenges.

Frontiers in microbiology·2026

Same author

An Unsupervised Approach for Artifact Severity Scoring in Multi-Contrast MR Images.

Proceedings of machine learning research·2026

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

Search research articles

Related Experiment Video

Updated: Jul 14, 2026

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

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

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

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

Related Experiment Videos

Last Updated: Jul 14, 2026

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

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

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

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.