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

Imaging Studies IV: Magnetic Resonance Imaging01:27

Imaging Studies IV: Magnetic Resonance Imaging

Introduction:Magnetic Resonance Imaging, or MRI, can include a specialized imaging technique of the urinary system known as Magnetic Resonance Urography (MRU). This radiation-free technique uses strong magnetic fields and radio waves to produce detailed images with the help of a computer. MRU is particularly effective for visualizing fluid-filled structures like the kidneys, ureters, and bladder.Applications of MRI in the Genitourinary SystemKidneys and Ureters: MRI detects tumors, cysts,...
Magnetic Resonance Imaging01:24

Magnetic Resonance Imaging

Magnetic resonance imaging (MRI) is a noninvasive medical imaging technique based on a phenomenon of nuclear physics discovered in the 1930s, in which matter exposed to magnetic fields and radio waves was found to emit radio signals. In 1970, a physician and researcher named Raymond Damadian noticed that malignant (cancerous) tissue gave off different signals than normal body tissue. He applied for a patent for the first MRI scanning device in clinical use by the early 1980s. The early MRI...

You might also read

Related Articles

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

Sort by
Same author

Effects of Intraperitoneal Colchicine on the Metabolic Consequences of High Fat Diet-Induced Obesity in Mice.

Endocrine, metabolic & immune disorders drug targets·2026
Same author

Effect of a partial loss of function MC3R mutation on MC3R+GHSR-1A heterodimer activity and RNF11 regulation.

Journal of endocrinological investigation·2025
Same author

Components of Adolescent Behavioural Interventions With Eating Disorder Outcomes: Systematic Review With Intervention Mapping.

Pediatric obesity·2025
Same author

Automated detection and characterization of small cell lung cancer liver metastasis on computed tomography.

Diagnostic and interventional radiology (Ankara, Turkey)·2025
Same author

The Effect of Experimentally Induced Cognitive Fatigue on Energy Intake Among Youth With and Without Recent Reported Dietary Restraint.

The International journal of eating disorders·2025
Same author

Generative Artificial Intelligence in Prostate Cancer Imaging.

Balkan medical journal·2025

Related Experiment Video

Updated: May 14, 2026

Whole Ovary Immunofluorescence, Clearing, and Multiphoton Microscopy for Quantitative 3D Analysis of the Developing Ovarian Reserve in Mouse
12:36

Whole Ovary Immunofluorescence, Clearing, and Multiphoton Microscopy for Quantitative 3D Analysis of the Developing Ovarian Reserve in Mouse

Published on: September 3, 2021

Artificial Intelligence-Based Approach for Automated Gonad Volume Quantification Using Magnetic Resonance Imaging in

Fahmida Haque1,2, Stephanie A Harmon1,2, Allison Kumnick3

  • 1Artificial Intelligence Resource, National Cancer Institute, National Institute of Health, Bethesda, MD 20892, USA.

Diagnostics (Basel, Switzerland)
|May 13, 2026
PubMed
Summary

This study developed open-source AI models for segmenting gonads in MRI scans, improving volumetric analysis of ovaries and testicles during puberty. These AI tools offer reliable performance for evaluating reproductive health.

Keywords:
MRIadolescentsartificial intelligencegonadspuberty

More Related Videos

Quantification of Levator Ani Hiatus Enlargement by Magnetic Resonance Imaging in Males and Females with Pelvic Organ Prolapse
07:41

Quantification of Levator Ani Hiatus Enlargement by Magnetic Resonance Imaging in Males and Females with Pelvic Organ Prolapse

Published on: April 17, 2019

Quantification of Optic Nerve Cross Sectional Area on MRI: A Novel Protocol using Fiji Software
08:57

Quantification of Optic Nerve Cross Sectional Area on MRI: A Novel Protocol using Fiji Software

Published on: September 4, 2021

Related Experiment Videos

Last Updated: May 14, 2026

Whole Ovary Immunofluorescence, Clearing, and Multiphoton Microscopy for Quantitative 3D Analysis of the Developing Ovarian Reserve in Mouse
12:36

Whole Ovary Immunofluorescence, Clearing, and Multiphoton Microscopy for Quantitative 3D Analysis of the Developing Ovarian Reserve in Mouse

Published on: September 3, 2021

Quantification of Levator Ani Hiatus Enlargement by Magnetic Resonance Imaging in Males and Females with Pelvic Organ Prolapse
07:41

Quantification of Levator Ani Hiatus Enlargement by Magnetic Resonance Imaging in Males and Females with Pelvic Organ Prolapse

Published on: April 17, 2019

Quantification of Optic Nerve Cross Sectional Area on MRI: A Novel Protocol using Fiji Software
08:57

Quantification of Optic Nerve Cross Sectional Area on MRI: A Novel Protocol using Fiji Software

Published on: September 4, 2021

Area of Science:

  • Radiology and Medical Imaging
  • Artificial Intelligence in Medicine
  • Pediatric Endocrinology

Background:

  • Magnetic Resonance Imaging (MRI) is crucial for assessing gonadal anatomy and pubertal changes.
  • Volumetric analysis of gonads provides reproductive insights, but manual annotation is time-consuming.
  • No existing AI tools effectively segment bilateral gonads in MRI scans.

Purpose of the Study:

  • To develop and validate open-source AI models for segmenting bilateral ovaries and testicles in healthy subjects.
  • To enable accurate volumetric and morphologic evaluation of gonads during puberty using MRI.
  • To address the lack of automated tools for gonadal segmentation in medical imaging.

Main Methods:

  • Utilized a longitudinal dataset of 182 MRIs from girls and 266 MRIs from boys.
  • Trained three-dimensional nnUnet AI models for ovary, cyst, and testicle segmentation.
  • Evaluated model performance using Dice Similarity Coefficient (DSC) on in-house and external datasets.

Main Results:

  • Achieved high segmentation accuracy with DSCOV of 0.86 for ovaries, DSCCY of 0.69 for cysts, and DSCTS of 0.90 for testicles.
  • Demonstrated reliable volumetric measurements with minimal mean differences for total ovary, cyst, and testicle volumes.
  • Validated model performance on an external dataset of adult subjects.

Conclusions:

  • The developed open-source AI models demonstrate promising and reliable performance for gonadal segmentation in MRI.
  • These AI tools can significantly aid in the volumetric and morphologic evaluation of gonads during puberty.
  • The study provides a foundation for automated gonadal assessment in clinical and research settings.