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

The Longitudinal Role of Mother-Child Synchrony in Predicting Functional Connectivity in Early Childhood.

Developmental science·2026
Same author

Comparing the association between out-of-pocket cost burden and cost-related care avoidance among individuals with and without a history of cancer.

Cancer·2026
Same author

Compound Yeast Culture Reshapes Gut Microbiota and Functional Pathways to Enhance Antioxidant Capacity and Immune Homeostasis in Suckling Calves.

Microorganisms·2026
Same author

m<sup>6</sup>A Modification Genetic Variants Associated with Autism Spectrum Disorder Risks.

Molecular neurobiology·2026
Same author

Curcumin-Embedded Magnesium-Polyphenol Network Hydrogel for Dual Delivery of aPD1 and Promotion of Pleural Sealing in Lung Cancer Immunotherapy.

Advanced healthcare materials·2026
Same author

Caspase-8 mediates E. coli-induced cell death and innate immune responses.

Journal of immunology (Baltimore, Md. : 1950)·2026

Related Experiment Video

Updated: Mar 26, 2026

A Pipeline to Characterize Structural Heart Defects in the Fetal Mouse
08:19

A Pipeline to Characterize Structural Heart Defects in the Fetal Mouse

Published on: December 16, 2022

2.5K

Automatic classification framework for ventricular septal defects: a pilot study on high-throughput mouse embryo

Zhongliu Xie1, Xi Liang2, Liucheng Guo3

  • 1Imperial College London, Department of Computing, South Kensington Campus, London SW7 2AZ, United Kingdom; National Institute of Informatics, 2-1-2 Hitotsubashi, Chiyoda-ku, Tokyo 101-8430, Japan.

Journal of Medical Imaging (Bellingham, Wash.)
|February 3, 2016
PubMed
Summary

This study introduces the first automated framework for classifying ventricular septal defects (VSDs) in mouse embryo images. The method accurately identifies VSDs by analyzing heart ventricle segmentation, aiding high-throughput genetic studies.

Keywords:
atlas-based segmentationmouse embryo phenotypingsnake evolutionventricular septal defects

More Related Videos

Analysis of Congenital Heart Defects in Mouse Embryos Using Qualitative and Quantitative Histological Methods
08:28

Analysis of Congenital Heart Defects in Mouse Embryos Using Qualitative and Quantitative Histological Methods

Published on: March 10, 2020

7.4K
Analysis of Cardiac Chamber Development During Mouse Embryogenesis Using Whole Mount Epifluorescence
06:27

Analysis of Cardiac Chamber Development During Mouse Embryogenesis Using Whole Mount Epifluorescence

Published on: April 17, 2019

8.2K

Related Experiment Videos

Last Updated: Mar 26, 2026

A Pipeline to Characterize Structural Heart Defects in the Fetal Mouse
08:19

A Pipeline to Characterize Structural Heart Defects in the Fetal Mouse

Published on: December 16, 2022

2.5K
Analysis of Congenital Heart Defects in Mouse Embryos Using Qualitative and Quantitative Histological Methods
08:28

Analysis of Congenital Heart Defects in Mouse Embryos Using Qualitative and Quantitative Histological Methods

Published on: March 10, 2020

7.4K
Analysis of Cardiac Chamber Development During Mouse Embryogenesis Using Whole Mount Epifluorescence
06:27

Analysis of Cardiac Chamber Development During Mouse Embryogenesis Using Whole Mount Epifluorescence

Published on: April 17, 2019

8.2K

Area of Science:

  • Developmental biology
  • Medical imaging informatics
  • Cardiovascular research

Background:

  • Genome-wide phenotyping initiatives require advanced imaging analysis for detecting subtle defects.
  • Current methods for mouse embryo cardiac phenotyping struggle with pathologies lacking significant volumetric changes, such as VSDs.
  • Existing approaches demand manual intervention and lack robust embryonic heart segmentation, hindering high-throughput analysis.

Purpose of the Study:

  • To develop the first fully automated framework for classifying ventricular septal defects (VSDs) in mouse embryo images.
  • To overcome limitations of existing phenotyping methods that rely on volumetric analysis and manual classification.
  • To enable high-throughput screening of cardiac phenotypes in genetically modified mouse embryos.

Main Methods:

  • Utilized a combination of atlas-based segmentation and snake evolution techniques for precise heart ventricle segmentation.
  • Developed a VSD classification strategy based on the spatial relationship (bordering or overlapping) of left and right ventricles.
  • Implemented a fully automatic pipeline for VSD detection and classification in mouse embryo cardiac images.

Main Results:

  • Achieved 100% classification accuracy for VSDs in a pilot study.
  • Demonstrated the framework's effectiveness on a database of 15 mouse embryo images.
  • Successfully validated the approach at a proof-of-concept level.

Conclusions:

  • The proposed automated framework represents a significant advancement in high-throughput embryonic cardiac phenotyping.
  • This method accurately classifies VSDs without relying on volumetric measurements, addressing limitations of current techniques.
  • The approach holds promise for accelerating genetic studies requiring precise cardiac defect analysis in mouse models.