Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

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

Tumour-macrophage crosstalk initiated by NFIC/METTL3 negative feedback loop via exosomal miR-194-5p promotes NSCLC progression.

Clinical and translational medicine·2026
Same author

Mining resistance genes of soybean cyst nematode based on genome-wide association study.

Frontiers in plant science·2026
Same author

Gradient-Driven Galvanic Effect Enables Self-Sustained Peroxymonosulfate Activation in a Stacked Flow Reactor.

Environmental science & technology·2026
Same author

Optimal surgical timing following neoadjuvant chemoradiotherapy in patients with rectal cancer: a systematic review and network meta-analysis.

American journal of translational research·2026
Same author

The dialogue between breast cancer and microorganisms.

Frontiers in cellular and infection microbiology·2026
Same author

CLCA1 Modulates Pancreatic Cancer Proliferation via SIRT1-HIF-1α Pathway.

Clinical laboratory·2026

Related Experiment Video

Updated: Jul 1, 2025

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
14:08

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

Published on: April 13, 2013

42.6K

Adaptive mask-based brain extraction method for head CT images.

Dingyuan Hu1, Shiya Qu1, Yuhang Jiang1

  • 1School of Mechanical Engineering and Automation, University of Science and Technology Liaoning, Qianshan District, Anshan City, Liaoning Province, China.

Plos One
|March 11, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces an adaptive mask-based brain extraction method (AMBBEM) for faster and more accurate head CT image analysis. The novel approach achieves high accuracy comparable to DeepLabv3+ while significantly improving processing speed.

More Related Videos

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

2.7K
Human Brown Adipose Tissue Depots Automatically Segmented by Positron Emission Tomography/Computed Tomography and Registered Magnetic Resonance Images
09:21

Human Brown Adipose Tissue Depots Automatically Segmented by Positron Emission Tomography/Computed Tomography and Registered Magnetic Resonance Images

Published on: February 18, 2015

12.2K

Related Experiment Videos

Last Updated: Jul 1, 2025

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
14:08

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

Published on: April 13, 2013

42.6K
Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

2.7K
Human Brown Adipose Tissue Depots Automatically Segmented by Positron Emission Tomography/Computed Tomography and Registered Magnetic Resonance Images
09:21

Human Brown Adipose Tissue Depots Automatically Segmented by Positron Emission Tomography/Computed Tomography and Registered Magnetic Resonance Images

Published on: February 18, 2015

12.2K

Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Neuroscience

Background:

  • Automated brain extraction is crucial for diagnosing intracranial lesions.
  • Traditional methods lack robustness, while deep learning models are slow.

Purpose of the Study:

  • To develop a novel, fast, and robust brain extraction method for head CT images.
  • To improve the accuracy and efficiency of automated lesion diagnosis.

Main Methods:

  • An adaptive mask-based brain extraction method (AMBBEM) was proposed.
  • Combines threshold segmentation, median filtering, closed operations, ResNet50, region growing, and image property analysis.
  • Final extraction achieved by multiplying the original image with the generated mask.

Main Results:

  • AMBBEM achieved high performance metrics (MPA=0.9963, MIoU=0.9924, MBF=0.9914), comparable to DeepLabv3+.
  • The method processes approximately 6.16 head CT images per second, significantly faster than other models.
  • Demonstrated robust performance on 22 test sets with diverse lesions.

Conclusions:

  • AMBBEM offers a rapid and accurate solution for brain extraction from head CT scans.
  • This method facilitates subsequent brain volume measurement and lesion feature extraction.
  • The approach provides a strong foundation for enhanced automated diagnosis of neurological conditions.