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 Experiment Video

Updated: Apr 10, 2026

Human Fetal Blood Flow Quantification with Magnetic Resonance Imaging and Motion Compensation
06:56

Human Fetal Blood Flow Quantification with Magnetic Resonance Imaging and Motion Compensation

Published on: January 7, 2021

3.0K

Consistency Regularization Improves Placenta Segmentation in Fetal EPI MRI Time Series.

Yingcheng Liu1, Neerav Karani1, Neel Dey1

  • 1Computer Science and Artificial Intelligence Lab, Massachusetts Institute of Technology, Cambridge, MA, USA.

Perinatal, Preterm and Paediatric Image Analysis (2025)
|April 9, 2026
PubMed
Summary

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

Intraoperative effects of ETV and CPC on intraventricular pressure and pulsation amplitude: A preliminary investigation of the hydrodynamic model of infant hydrocephalus.

medRxiv : the preprint server for health sciences·2026
Same author

SparseMed3D: Foundation Models for Sparse Instance Medical Segmentation.

Journal of imaging informatics in medicine·2026
Same author

Linking maternal blood pressure with fetal cerebral haemodynamics and cortical growth in congenital heart disease.

EBioMedicine·2026
Same author

Equivariant Filters for Efficient Tracking in 3D Imaging.

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention·2026
Same author

Improved, rapid fetal-brain localization and orientation detection for auto-slice prescription.

Proceedings of the International Society for Magnetic Resonance in Medicine ... Scientific Meeting and Exhibition. International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition·2026
Same author

Fast, automated slice prescription of standard anatomical planes for fetal brain MRI.

Proceedings of the International Society for Magnetic Resonance in Medicine ... Scientific Meeting and Exhibition. International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition·2026

This study introduces a semi-supervised learning method to improve 3D placenta segmentation in fetal MRI. The technique enhances accuracy and temporal consistency, aiding prenatal care and biomarker analysis.

Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Fetal Development

Background:

  • The placenta is vital for fetal development.
  • Accurate placenta segmentation from fetal MRI is crucial for prenatal care.
  • Existing methods may struggle with segmentation accuracy and temporal consistency.

Purpose of the Study:

  • To develop an effective semi-supervised learning method for 3D placenta segmentation in fetal EPI MRI time series.
  • To improve segmentation accuracy, especially for challenging cases.
  • To enhance the temporal coherence of segmentation predictions.

Main Methods:

  • Implemented a semi-supervised learning approach utilizing consistency regularization.
  • Applied spatial transformation consistency within individual images.
Keywords:
Fetal MRIconsistency regularizationimage segmentationplacenta segmentationsemi-supervised learning (SSL)

More Related Videos

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

4.1K
Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images
06:48

Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images

Published on: January 7, 2019

9.6K

Related Experiment Videos

Last Updated: Apr 10, 2026

Human Fetal Blood Flow Quantification with Magnetic Resonance Imaging and Motion Compensation
06:56

Human Fetal Blood Flow Quantification with Magnetic Resonance Imaging and Motion Compensation

Published on: January 7, 2021

3.0K
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

4.1K
Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images
06:48

Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images

Published on: January 7, 2019

9.6K
  • Ensured temporal consistency across adjacent images in the MRI time series.

    • Main Results:

    • Achieved improved overall segmentation accuracy for the placenta.
    • Demonstrated enhanced performance on outliers and difficult-to-segment samples.
    • Showcased improved temporal coherency in segmentation predictions.

    Conclusions:

    • The proposed method effectively improves 3D placenta segmentation in fetal EPI MRI.
    • Enhanced temporal consistency facilitates more accurate placental biomarker computation.
    • This work advances placenta study and prenatal clinical decision-making.