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Development of Gestational Age-Based Fetal Brain and Intracranial Volume Reference Norms Using Deep Learning.

C B N Tran¹, P Nedelec¹, D A Weiss¹

¹From the Department of Radiology & Biomedical Imaging, University of California, San Francisco, San Francisco, California.

AJNR. American Journal of Neuroradiology

|December 22, 2022

Summary

This summary is machine-generated.

A new deep learning algorithm accurately measures fetal brain and intracranial volumes using MRI scans. This tool helps identify abnormal fetal brain development across gestational ages.

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Area of Science:

Medical imaging
Artificial intelligence
Fetal medicine

Background:

Fetal brain MRI interpretation is subjective and requires specialized expertise.
Accurate measurement of fetal intracranial and brain volumes is crucial for assessing development.

Purpose of the Study:

To develop a deep learning algorithm for automated measurement of fetal intracranial and brain volumes.
To establish normative reference standards for these volumes across gestational ages.

Main Methods:

A retrospective study of 246 fetal brain MRIs was conducted.
Two 3D U-Net models were trained for segmentation of intracranial and brain compartments.
The algorithm was validated against manual measurements and applied to normative and pathologic cases.

Main Results:

Automated segmentation achieved high accuracy (Dice scores 0.95/0.90) and speed (mean 6.8 seconds).
High correlation (Pearson r = 0.996) was observed between automated and manual volume measurements.
Normative volume data were generated, and 8/9 pathologic cases were correctly identified as abnormal.

Conclusions:

Deep learning enables rapid and accurate quantification of fetal brain and intracranial volumes.
The algorithm can identify abnormal volumes using age-specific normative reference standards.