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

Automatized spleen segmentation in non-contrast-enhanced MR volume data using subject-specific shape priors.

Physics in medicine and biology·2017
Same author

Living alone and activation of the renin-angiotensin-aldosterone-system: Differential effects depending on alexithymic personality features.

Journal of psychosomatic research·2017
Same author

Corrigendum: 1000 Genomes-based meta-analysis identifies 10 novel loci for kidney function.

Scientific reports·2017
Same author

Use of Repeated Blood Pressure and Cholesterol Measurements to Improve Cardiovascular Disease Risk Prediction: An Individual-Participant-Data Meta-Analysis.

American journal of epidemiology·2017
Same author

Sex-specific metabolic profiles of androgens and its main binding protein SHBG in a middle aged population without diabetes.

Scientific reports·2017
Same author

Investigating the causal effect of smoking on hay fever and asthma: a Mendelian randomization meta-analysis in the CARTA consortium.

Scientific reports·2017

Related Experiment Video

Updated: Mar 7, 2026

Author Spotlight: Bridging Gaps in Anatomy and Establishing a Foundation for Algorithmic Studies
04:25

Author Spotlight: Bridging Gaps in Anatomy and Establishing a Foundation for Algorithmic Studies

Published on: December 15, 2023

4.0K

Automatic MRI segmentation of para-pharyngeal fat pads using interactive visual feature space analysis for

Muhammad Laiq Ur Rahman Shahid1, Teodora Chitiboi2,3, Tetyana Ivanovska4

  • 1Jacobs University, Bremen, Germany. m.shahid@jacobs-university.de.

BMC Medical Imaging
|February 16, 2017
PubMed
Summary

We developed an automatic method to segment parapharyngeal fat pads from MRI scans, crucial for understanding obstructive sleep apnea (OSA) causes and developing interventions.

Keywords:
Interactive visual analysis toolMagnetic resonance imaging (MRI)Obstructive sleep apnea (OSA)Para-pharyngeal fat pads segmentationUpper airway segmentation

More Related Videos

Automated Joint Space Detection Improves Bone Segmentation Accuracy
06:45

Automated Joint Space Detection Improves Bone Segmentation Accuracy

Published on: November 28, 2025

233
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

12.7K

Related Experiment Videos

Last Updated: Mar 7, 2026

Author Spotlight: Bridging Gaps in Anatomy and Establishing a Foundation for Algorithmic Studies
04:25

Author Spotlight: Bridging Gaps in Anatomy and Establishing a Foundation for Algorithmic Studies

Published on: December 15, 2023

4.0K
Automated Joint Space Detection Improves Bone Segmentation Accuracy
06:45

Automated Joint Space Detection Improves Bone Segmentation Accuracy

Published on: November 28, 2025

233
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

12.7K

Area of Science:

  • Medical Imaging
  • Sleep Medicine
  • Anatomy

Background:

  • Obstructive sleep apnea (OSA) is a significant public health concern.
  • Understanding the pathogenesis of OSA requires detailed analysis of parapharyngeal fat pads.
  • Identifying anatomical risk factors for OSA necessitates reliable segmentation techniques for these fat pads.

Purpose of the Study:

  • To develop a context-based automatic segmentation algorithm for parapharyngeal fat pads.
  • To delineate fat pads from magnetic resonance images (MRIs) in a population-based study.
  • To enable automated segregation of fat pads from other anatomical structures.

Main Methods:

  • A segmentation pipeline integrating texture analysis, connected component analysis, and object-based image analysis.
  • Supervised classification employing an interactive visual analysis tool for automated segregation.
  • Development of a fully automatic segmentation technique requiring no user interaction.

Main Results:

  • The developed algorithm automatically extracts parapharyngeal fat pads efficiently.
  • The method is suitable for large-scale population-based epidemiological studies.
  • Quantitative evaluation yielded an average 78% detected volume fraction and 79% Dice coefficient, comparable to inter-observer variability.

Conclusions:

  • The automatic segmentation method provides accurate results for parapharyngeal fat pads.
  • This technique holds potential for large-scale studies investigating OSA pathogenesis.
  • The findings contribute to understanding anatomical risk factors in OSA syndrome.