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

Sutures of the Skull01:22

Sutures of the Skull

13.8K
The human skull is composed of several bones that come together to protect the brain and support the structures of the face. The junctions where these bones meet are called sutures.
Sutures are immobile joints between adjacent bones of the skull. The narrow gap between the bones is filled with dense, fibrous connective tissue that unites the bones. The long sutures located between the skull bones are not straight but instead follow irregular, tightly twisting paths. These twisting lines tightly...
13.8K

You might also read

Related Articles

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

Sort by
Same author

An evaluation of brain volume and cortical thickness measurement at 0.55 T.

Magma (New York, N.Y.)·2026
Same author

Optimization of fetal brain MRI at 0.55 T with slice-to-volume reconstruction.

Magma (New York, N.Y.)·2026
Same author

Chronic Anemia Patients Demonstrate Diffuse Demyelination.

American journal of hematology·2026
Same author

A hierarchical brain MRI atlas of the coppery titi monkey (Plecturocebus cupreus).

NeuroImage·2026
Same author

Optimizing electrode placement and information capacity for local field potentials in cortex.

NeuroImage·2026
Same author

Clinical Manifestations.

Alzheimer's & dementia : the journal of the Alzheimer's Association·2025

Related Experiment Video

Updated: Apr 25, 2026

Intravital Longitudinal Imaging of Vascular Dynamics in the Calvarial Bone Marrow
10:49

Intravital Longitudinal Imaging of Vascular Dynamics in the Calvarial Bone Marrow

Published on: April 11, 2025

1.2K

Skull-stripping with machine learning deformable organisms.

Gautam Prasad1, Anand A Joshi2, Albert Feng3

  • 1Imaging Genetics Center & Laboratory of Neuro Imaging, Institute for Neuroimaging and Informatics, Keck School of Medicine of USC, Los Angeles, CA, USA; Department of Psychology, Stanford University, Stanford, CA, USA.

Journal of Neuroscience Methods
|August 16, 2014
PubMed
Summary

This study introduces a novel artificial life-inspired framework for medical image segmentation, improving brain extraction (skull-stripping) accuracy and adaptability for diverse datasets and tasks.

Keywords:
AdaboostDeformable organismsHausdorffMRIOverlapRegistrationSkull-stripping

More Related Videos

Three-Dimensional Shape Modeling and Analysis of Brain Structures
05:33

Three-Dimensional Shape Modeling and Analysis of Brain Structures

Published on: November 14, 2019

6.6K
A MRI-Based Toolbox for Neurosurgical Planning in Nonhuman Primates
08:41

A MRI-Based Toolbox for Neurosurgical Planning in Nonhuman Primates

Published on: July 17, 2020

4.4K

Related Experiment Videos

Last Updated: Apr 25, 2026

Intravital Longitudinal Imaging of Vascular Dynamics in the Calvarial Bone Marrow
10:49

Intravital Longitudinal Imaging of Vascular Dynamics in the Calvarial Bone Marrow

Published on: April 11, 2025

1.2K
Three-Dimensional Shape Modeling and Analysis of Brain Structures
05:33

Three-Dimensional Shape Modeling and Analysis of Brain Structures

Published on: November 14, 2019

6.6K
A MRI-Based Toolbox for Neurosurgical Planning in Nonhuman Primates
08:41

A MRI-Based Toolbox for Neurosurgical Planning in Nonhuman Primates

Published on: July 17, 2020

4.4K

Area of Science:

  • Artificial intelligence
  • Medical imaging
  • Computational biology

Background:

  • Current medical image segmentation methods lack generalizability across datasets and tasks.
  • This limits their practical application in clinical settings.
  • Deformable models offer potential for customizable segmentation plans.

Purpose of the Study:

  • To develop a flexible and accurate framework for medical image segmentation.
  • To address the limitations of existing methods in terms of generalizability.
  • To validate the framework for brain extraction (skull-stripping) in 3D MRI.

Main Methods:

  • A novel approach inspired by artificial life principles governs deformable models.
  • Incorporates control processes like sensing, planning, and reactive behavior.
  • Utilizes machine learning for enhanced segmentation accuracy.
  • Allows easy incorporation of dataset-specific landmarks and features.

Main Results:

  • Achieved the lowest Hausdorff distance error in brain segmentation.
  • Demonstrated the lowest false positive error rate.
  • Performed comparably to specialized skull-stripping methods on other metrics.
  • The learning step significantly improved segmentation accuracy.

Conclusions:

  • The developed framework effectively segments diverse categories of information for brain extraction.
  • It offers a robust foundation for addressing various medical image segmentation challenges.
  • The method shows promise for improving the utility of medical image analysis tools.