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Related Experiment Video

Updated: Jul 12, 2025

Whole-Brain Single-Cell Imaging and Analysis of Intact Neonatal Mouse Brains Using MRI, Tissue Clearing, and Light-Sheet Microscopy

Whole-Brain Single-Cell Imaging and Analysis of Intact Neonatal Mouse Brains Using MRI, Tissue Clearing, and Light-Sheet Microscopy

Published on: August 1, 2022

Automatic biometry of fetal brain MRIs using deep and machine learning techniques.

Jiayan She¹, Haiying Huang², Zhijun Ye¹

¹Key Laboratory of Birth Defects and Related Diseases of Women and Children, Ministry of Education, Department of Radiology, West China Second University Hospital, Sichuan University, Chengdu, 610041, China.

Scientific Reports

|October 19, 2023

Summary

This study introduces an automatic method for measuring fetal brain development using MRI scans, improving speed and accuracy in prenatal diagnosis. The AI-driven approach enhances clinical work efficiency and prenatal diagnostic capabilities.

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Last Updated: Jul 12, 2025

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Area of Science:

Medical Imaging
Artificial Intelligence
Fetal Development

Background:

Accurate fetal brain development assessment via MRI is crucial but labor-intensive.
Current methods rely on expert knowledge, limiting efficiency and accessibility.

Purpose of the Study:

To develop an automated, segmentation-based method for rapid and precise fetal brain biometric measurements from MRI.
To validate the accuracy and reliability of the automated method against manual measurements.

Main Methods:

A deep segmentation network was employed to segment the fetal brain into cerebrum, cerebellum, and lateral ventricles.
Key biometric parameters (CBPD, TCD, LAD/RAD) were automatically calculated based on clinical guidelines.
Pearson correlation and Bland-Altman plots assessed agreement between automated and manual measurements.

Main Results:

High correlation coefficients were observed for cerebral biparietal diameter (0.977) and transverse cerebellar diameter (0.990).
Mean differences for automated measurements were minimal, indicating strong agreement with manual assessments.
No significant correlation was found between measurement errors and gestational age.

Conclusions:

The automated method demonstrates excellent performance for linear measurements on fetal brain MRI.
This technique offers a fast, accurate, and potentially transformative tool for prenatal diagnosis and clinical workflow optimization.