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Semi-supervised Fetal Brain Parcellation via Hierarchical Learning Framework.

Shijie Huang¹, Kai Zhang¹, Fangmei Zhu²

¹School of Biomedical Engineering & State Key Laboratory of Advanced Medical Materials and Devices, ShanghaiTech University, Shanghai, China.

Medical Image Analysis

|October 12, 2025

Summary

This study presents a novel hierarchical method for segmenting fetal brain regions in MRI scans. The approach improves accuracy and robustness, addressing limitations in current automated brain parcellation techniques.

Keywords:

Fetal brain MRI Hierarchical modeling Segmentation Semi-supervised learning

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Area of Science:

Neuroimaging
Medical Image Analysis
Developmental Neuroscience

Background:

Automatic fetal brain region segmentation using MRI is crucial for studying prenatal development.
Manual segmentation is labor-intensive and limited by scarce annotated data.
Existing methods often overlook the hierarchical structure and inter-regional relationships in the brain.

Purpose of the Study:

To introduce a novel hierarchical segmentation method for detailed fetal brain parcellation into 87 regions.
To address the challenges of limited annotated data and improve segmentation robustness.
To enhance the understanding of prenatal brain growth and development.

Main Methods:

A three-level coarse-to-fine network architecture is proposed for hierarchical segmentation.
A data augmentation module simulates imaging variations to enhance robustness.
Semi-supervised learning combines simulated and limited real labeled data for training.

Main Results:

The method achieved a high Dice score of 91.42% on fetal brain MR images.
It outperformed the leading nnUNet method (88.77%) in segmentation accuracy.
Demonstrated robust performance across diverse imaging conditions and scanner variability.

Conclusions:

The proposed hierarchical segmentation method effectively addresses limitations in current fetal brain parcellation.
It offers a robust and accurate approach for studying prenatal brain development using MRI.
This technique has the potential to advance research in developmental neuroscience and clinical applications.