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

What Are Outliers?01:12

What Are Outliers?

5.2K
Outliers are observed data points that are far from the least squares line. They have unusual values and need to be examined carefully. Though an outlier may result from erroneous data, at other times, it may hold valuable information about the population under study and should be included in the data. Hence, it is crucial to examine what causes a data point to be an outlier.
The z score is used to find outliers or unusual values. It should be noted that any values beyond -2 and +2 are...
5.2K
Outliers and Influential Points01:08

Outliers and Influential Points

6.3K
An outlier is an observation of data that does not fit the rest of the data. It is sometimes called an extreme value. When you graph an outlier, it will appear not to fit the pattern of the graph. Some outliers are due to mistakes (for example, writing down 50 instead of 500), while others may indicate that something unusual is happening. Outliers are present far from the least squares line in the vertical direction. They have large "errors," where the "error" or residual is the...
6.3K
Quantifying and Rejecting Outliers: The Grubbs Test01:02

Quantifying and Rejecting Outliers: The Grubbs Test

4.2K
Sometimes, a data set can have a recorded numerical observation that greatly  deviates from the rest of the data. Assuming that the data is normally distributed, a statistical method called the Grubbs test can be used to determine whether the observation is truly an outlier.  To perform a two-tailed Grubbs test, first, calculate the absolute difference between the outlier and the mean. Then, calculate the ratio between this difference and the standard deviation of the sample. This...
4.2K
Brain Imaging01:14

Brain Imaging

757
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...
757
Dehydration Synthesis01:15

Dehydration Synthesis

150.3K
Overview
Dehydration synthesis (also called a condensation reaction) is the chemical process in which two molecules covalently link together to form a new molecule, along with the release of a water molecule. Many physiologically important compounds form by dehydration synthesis reactions, such as complex carbohydrates, proteins, DNA, and RNA.
Synthesis of carbohydrates
Sugar molecules are covalently linked together by dehydration synthesis. During the reaction, the hydroxyl (-OH) group from...
150.3K
Synthesis and Decomposition Reactions02:17

Synthesis and Decomposition Reactions

38.3K
Synthesis and decomposition are two types of redox reactions. Synthesis means to make something, whereas decomposition means to break something. The reactions are accompanied by chemical and energy changes. 
38.3K

You might also read

Related Articles

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

Sort by
Same author

Water-based DNF@PLA microcapsules with excellent interface properties enhancing insecticidal activity against Nilaparvata lugens.

Pest management science·2026
Same author

The Prognostic Role of C-Reactive Protein-Triglyceride Glucose Index in Predicting Unfavorable Outcomes in Acute Ischemic Stroke: A Large-Scale Cohort Study.

Brain and behavior·2026
Same author

Does a monoclonal antibody targeting immune cells affect glutamatergic level in schizophrenia? A multimodal PET/MRS brain imaging study.

Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology·2026
Same author

Bioactive Magnesium Silicate Activating Myocardial Energy Metabolism For Infarcted Myocardium Repair.

Exploration (Beijing, China)·2026
Same author

TEMPORARY REMOVAL: PDZK1 disassembles HER2-HSP90 complexes to promote ubiquitin-mediated HER2 degradation and overcome therapy resistance.

Journal of advanced research·2026
Same author

Is there already value in "Total Body PET" imaging in Neurology?

The British journal of radiology·2026

Related Experiment Video

Updated: Feb 9, 2026

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping
10:25

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

Published on: September 25, 2019

49.4K

Brain lesion segmentation through image synthesis and outlier detection.

Christopher Bowles1, Chen Qin1, Ricardo Guerrero1

  • 1Department of Computing, Imperial College London, UK.

Neuroimage. Clinical
|June 6, 2018
PubMed
Summary

This study introduces an unsupervised method for detecting hyperintense lesions in cerebral small vessel disease (SVD) on MRI scans. The novel approach accurately segments abnormalities regardless of pathology or location, outperforming existing methods.

More Related Videos

Whole-brain Segmentation and Change-point Analysis of Anatomical Brain MRI—Application in Premanifest Huntington's Disease
09:06

Whole-brain Segmentation and Change-point Analysis of Anatomical Brain MRI—Application in Premanifest Huntington's Disease

Published on: June 9, 2018

12.7K
Monitoring Tumor Metastases and Osteolytic Lesions with Bioluminescence and Micro CT Imaging
08:04

Monitoring Tumor Metastases and Osteolytic Lesions with Bioluminescence and Micro CT Imaging

Published on: April 14, 2011

25.2K

Related Experiment Videos

Last Updated: Feb 9, 2026

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping
10:25

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

Published on: September 25, 2019

49.4K
Whole-brain Segmentation and Change-point Analysis of Anatomical Brain MRI—Application in Premanifest Huntington's Disease
09:06

Whole-brain Segmentation and Change-point Analysis of Anatomical Brain MRI—Application in Premanifest Huntington's Disease

Published on: June 9, 2018

12.7K
Monitoring Tumor Metastases and Osteolytic Lesions with Bioluminescence and Micro CT Imaging
08:04

Monitoring Tumor Metastases and Osteolytic Lesions with Bioluminescence and Micro CT Imaging

Published on: April 14, 2011

25.2K

Area of Science:

  • Medical imaging
  • Neurology
  • Artificial intelligence in medicine

Background:

  • Cerebral small vessel disease (SVD) causes hyperintense lesions on MRI, complicating diagnosis.
  • Existing lesion segmentation methods are often limited by pathology type or location.
  • Need for a universal, unsupervised method for SVD lesion detection.

Purpose of the Study:

  • To develop an unsupervised abnormality detection method for hyperintense lesions in SVD.
  • To segment lesions regardless of underlying pathology or location on FLAIR MRI.
  • To improve upon existing automated segmentation techniques for SVD.

Main Methods:

  • Utilized a combination of image synthesis, Gaussian mixture models, and one-class support vector machines.
  • Trained the model exclusively on healthy brain tissue for unsupervised learning.
  • Applied the method to segment lesions in 127 subjects with SVD.

Main Results:

  • The unsupervised method successfully detected hyperintense regions across various pathologies and locations.
  • Significantly superior performance was reported compared to three established segmentation methods.
  • Quantitative metrics demonstrated the enhanced accuracy of the proposed technique.

Conclusions:

  • The developed unsupervised method offers a robust and versatile approach for SVD lesion segmentation.
  • This technique overcomes limitations of previous methods by not requiring pathology-specific training.
  • The findings suggest a promising advancement in automated analysis of SVD on MRI.