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

Brain Imaging01:14

Brain Imaging

998
Brain imaging technologies provide critical insights into both the structure and function of the human brain, enabling medical professionals and researchers to diagnose, study, and treat neurological disorders or psychiatric disorders more effectively.
These technologies include computerized axial tomography (CAT or CT scans), positron-emission tomography (PET scans),  magnetic resonance imaging (MRI),  functional magnetic resonance imaging (fMRI), and Transcranial Magnetic...
998

You might also read

Related Articles

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

Sort by
Same author

FLAME: a model for duration-dependent risk accumulation in episodic temporal exposures.

Biometrics·2026
Same author

Fetal Echocardiography and Hyperoxia Testing after Serial Amnioinfusions: Results from the RAFT Trial Bilateral Renal Agenesis Arm.

Fetal diagnosis and therapy·2026
Same author

Neonatal Survival After Serial Amnioinfusions for Anhydramnios Due to Fetal Kidney Failure: The RAFT Clinical Trial.

JAMA·2026
Same author

Does early gastrostomy tube placement after stroke improve functional recovery and quality of life? A literature-informed pathway-decomposition analysis.

Neurological research·2026
Same author

Corticospinal tract risk modifies motor recovery after minimally invasive surgery for intracerebral hemorrhage: a secondary analysis of MISTIE-III.

medRxiv : the preprint server for health sciences·2026
Same author

Discussion on "INTACT: a method for integration of longitudinal physical activity data from multiple sources" by Jingru Zhang, Erjia Cui, Hongzhe Li, and Haochang Shou.

Biometrics·2026

Related Experiment Video

Updated: Apr 15, 2026

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

43.8K

Validated automatic brain extraction of head CT images.

John Muschelli1, Natalie L Ullman2, W Andrew Mould2

  • 1Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA.

Neuroimage
|April 12, 2015
PubMed
Summary

Brain extraction using FSL's BET tool on CT scans is effective, especially with smoothed images and specific parameters. This method accurately segments brain tissue, crucial for research involving intracranial hemorrhage.

Keywords:
Brain extractionCTSkull strippingValidation

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

3.7K
Lesion Explorer: A Video-guided, Standardized Protocol for Accurate and Reliable MRI-derived Volumetrics in Alzheimer's Disease and Normal Elderly
12:50

Lesion Explorer: A Video-guided, Standardized Protocol for Accurate and Reliable MRI-derived Volumetrics in Alzheimer's Disease and Normal Elderly

Published on: April 14, 2014

41.1K

Related Experiment Videos

Last Updated: Apr 15, 2026

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

43.8K
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

3.7K
Lesion Explorer: A Video-guided, Standardized Protocol for Accurate and Reliable MRI-derived Volumetrics in Alzheimer's Disease and Normal Elderly
12:50

Lesion Explorer: A Video-guided, Standardized Protocol for Accurate and Reliable MRI-derived Volumetrics in Alzheimer's Disease and Normal Elderly

Published on: April 14, 2014

41.1K

Area of Science:

  • Neuroimaging
  • Medical Image Analysis

Background:

  • X-ray computed tomography (CT) is vital for brain imaging research.
  • Brain extraction is a critical step for analyzing CT scans, separating brain tissue from other structures.
  • Existing brain extraction methods lack formal validation or full automation.

Purpose of the Study:

  • To systematically validate FSL's BET tool for brain extraction on head CT images in patients with intracranial hemorrhage.
  • To compare automated BET results against manual segmentations and assess longitudinal scan reliability.
  • To investigate the impact of BET parameters and data smoothing on performance.

Main Methods:

  • CT images were thresholded (0-100 HU), with and without Gaussian smoothing (σ=1mm³).
  • FSL's BET tool was applied using fractional intensity (FI) thresholds of 0.01, 0.1, or 0.35.
  • Performance was validated against manual segmentations using sensitivity, specificity, accuracy, and Dice Similarity Index (DSI) on 36 scans. Intracranial volume (ICV) ratios and intraclass correlation (ICC) were calculated on 1095 longitudinal scans.

Main Results:

  • Image smoothing significantly improved BET performance across most metrics (p<0.01), except specificity.
  • BET with FI thresholds of 0.01 or 0.1 yielded superior results compared to 0.35.
  • Smoothed CT images with FI 0.01 showed high accuracy (median DSI 0.9895) and a low failure rate (5.2%) on longitudinal scans with high reliability (ICC 0.929).

Conclusions:

  • FSL's BET tool effectively performs brain extraction on smoothed CT images with FI thresholds of 0.01 or 0.1.
  • Data smoothing is a crucial preprocessing step for optimizing BET performance on CT scans.
  • The study provides validated methods and analysis code for automated brain extraction in neuroimaging research.