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

Brain Waves

Brain Waves

Brain waves are electrical signals generated by the neurons in the brain, which are regularly monitored to measure mental activities. Brain waves and their frequency ranges can be measured using an electroencephalogram or EEG. There are four main types of brain waves, each with distinct characteristics:

You might also read

Related Articles

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

Sort by

Same author

4-Octyl Itaconate Promotes Diabetic Wound Healing by Enhancing Pro-Resolving Macrophages via the Efferocytosis-MCT1-Lactate-GPR132 Pathway and Macrophage-Independent Synergistic Effects (Diabetes Metab J 2026;50:707-23).

Diabetes & metabolism journal·2026

Same author

Knowledge-Based Feature Selection Substantially Enhances Data-Driven Wastewater Treatment Modeling.

Environmental science & technology·2026

Same author

Exploring the feasibility of modeling next-day fatigue and sleepiness using digital sleep tracker data in neurodegenerative and immune-mediated inflammatory diseases.

Frontiers in digital health·2026

Same author

Can real-world measures of activity, sleep, and cardiorespiratory function stratify Sjogren's disease participants with persistent fatigue? Insights from the BRC tools study.

EULAR rheumatology open·2026

Same author

Integrating comammox into anammox-dominated one-stage mainstream partial nitrification/anammox: nitrifier niche regulation and nitrite partitioning.

Water research·2026

Same author

Early ICU glycemic trajectory phenotypes predict short- and long-term mortality in critically ill patients: a dual-database cohort study.

Metabolism open·2026

Search research articles

Related Experiment Video

Updated: Jun 14, 2025

Live Imaging of Microtubule Dynamics in Glioblastoma Cells Invading the Zebrafish Brain

Live Imaging of Microtubule Dynamics in Glioblastoma Cells Invading the Zebrafish Brain

Published on: July 29, 2022

Rethinking Brain Tumor Segmentation From the Frequency Domain Perspective.

Minye Shao, Zeyu Wang, Haoran Duan

IEEE Transactions on Medical Imaging

|June 12, 2025

Summary

Harmonized Frequency Fusion Network (HFF-Net) improves brain tumor segmentation by analyzing MRI images in the frequency domain, enhancing contrast-enhancing region identification for better diagnosis and treatment planning.

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

Patient-Specific Polyvinyl Alcohol Phantom Fabrication with Ultrasound and X-Ray Contrast for Brain Tumor Surgery Planning

Patient-Specific Polyvinyl Alcohol Phantom Fabrication with Ultrasound and X-Ray Contrast for Brain Tumor Surgery Planning

Published on: July 14, 2020

Related Experiment Videos

Last Updated: Jun 14, 2025

Live Imaging of Microtubule Dynamics in Glioblastoma Cells Invading the Zebrafish Brain

Live Imaging of Microtubule Dynamics in Glioblastoma Cells Invading the Zebrafish Brain

Published on: July 29, 2022

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

Patient-Specific Polyvinyl Alcohol Phantom Fabrication with Ultrasound and X-Ray Contrast for Brain Tumor Surgery Planning

Patient-Specific Polyvinyl Alcohol Phantom Fabrication with Ultrasound and X-Ray Contrast for Brain Tumor Surgery Planning

Published on: July 14, 2020

Area of Science:

Medical Imaging
Artificial Intelligence
Neuro-Oncology

Background:

Accurate brain tumor segmentation, especially for contrast-enhancing regions in MRI, is vital for clinical decisions but challenging for current methods.
Existing techniques struggle with complex tumor textures and directional variations inherent in MRI data.
Insufficient analysis of frequency-domain features limits the performance of current brain tumor segmentation models.

Purpose of the Study:

To develop a novel deep learning framework, HFF-Net, for improved brain tumor segmentation by leveraging frequency-domain analysis.
To enhance the characterization of tumor regions by decomposing MRI images into frequency components.
To improve the sensitivity to tumor boundaries and effectively fuse multi-scale tumor features.

Main Methods:

Proposed the Harmonized Frequency Fusion Network (HFF-Net) for brain tumor segmentation.
Introduced a Frequency Domain Decomposition (FDD) module to separate MRI images into low and high-frequency components.
Developed an Adaptive Laplacian Convolution (ALC) module for emphasizing high-frequency details and a Frequency Domain Cross-Attention (FDCA) module for multi-scale feature fusion.

Main Results:

HFF-Net achieved an average relative improvement of 4.48% in mean Dice scores across major subregions.
Demonstrated a significant average relative improvement of 7.33% in segmenting contrast-enhancing tumor regions.
Validated frequency-domain improvements through visualization, theoretical reasoning, and extensive experiments on four public datasets.

Conclusions:

HFF-Net significantly enhances brain tumor segmentation accuracy, particularly for contrast-enhancing regions, by utilizing frequency-domain information.
The proposed FDD, ALC, and FDCA modules effectively capture and fuse critical tumor features, addressing limitations of existing methods.
HFF-Net offers a promising, computationally efficient, and clinically applicable solution for brain tumor segmentation.