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

White matter reflects the childhood exposome.

bioRxiv : the preprint server for biology·2026

Same author

Mapping the spatiotemporal continuum of structural connectivity development across the human connectome in youth.

Nature communications·2026

Same author

Reproducibility of Diffusion, Shape, and Connectivity Metrics Across Scanners: Implications for Multi-Site Tractography.

bioRxiv : the preprint server for biology·2026

Same author

Tractometry-Based Quantification of Along-Tract White-Matter Hemispheric Asymmetry in Alzheimer's Disease.

bioRxiv : the preprint server for biology·2026

Same author

Anatomical White Matter Tracts Span the Cortical Hierarchy to Support Cognitive Diversity.

bioRxiv : the preprint server for biology·2026

Same author

Topological turning points across the human lifespan.

Nature communications·2025

Same journal

Poisoning the Genome: Targeted Backdoor Attacks on DNA Foundation Models.

ArXiv·2026

Same journal

Mechanistic mathematical model of the in vitro infection dynamics of Bunyamwera and Batai viruses including MOI-dependent shortening of the eclipse phase.

ArXiv·2026

Same journal

AI-Driven Lumped-Element Modeling of Human Respiratory System for Studying Voice Mechanics.

ArXiv·2026

Same journal

Beyond Algorithms: Conceptual Innovation in Medical Imaging AI.

ArXiv·2026

Same journal

Feynman Kac Reweighted Schrödinger Bridge Matching for Surface-Based Tau PET Harmonization.

ArXiv·2026

Same journal

Agentic Discovery of Non-Canonical Antimicrobial Peptides with AMPGAN v3.

ArXiv·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jul 19, 2025

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping

Published on: September 25, 2019

Brain MRI Segmentation using Template-Based Training and Visual Perception Augmentation.

Fang-Cheng Yeh^1,2

¹Department of Neurological Surgery, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.

|August 14, 2023

Summary

This summary is machine-generated.

This study introduces a novel template-based training method for deep learning models, enabling accurate brain MRI segmentation using just one template. This approach overcomes data limitations, offering a unified solution for training neural networks in medical image analysis.

Keywords:

U-Net brain segmentation deep learning

More Related Videos

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

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Published on: April 13, 2013

Related Experiment Videos

Last Updated: Jul 19, 2025

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping

Published on: September 25, 2019

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

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Published on: April 13, 2013

Area of Science:

Neuroimaging
Artificial Intelligence
Medical Image Analysis

Background:

Deep learning models for medical image analysis, such as brain MRI segmentation, typically require extensive labeled datasets.
Acquiring large volumes of annotated neuroimaging data is often resource-intensive, posing a significant bottleneck for model development and application.
Existing methods struggle with limited data availability, hindering the widespread adoption of advanced AI techniques in neuroscience research.

Conclusions:

The proposed template-based training method offers an efficient solution for developing deep learning models for brain MRI analysis when labeled data is scarce.
This approach significantly reduces the dependency on large annotated datasets, democratizing the use of advanced AI in neuroimaging research.
The developed tool provides a unified framework for training deep neural networks for segmentation tasks using only a single image sample, applicable across different species and research contexts.