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Updated: Nov 3, 2025
Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images
Published on: January 7, 2019
Semi-Supervised Learning in Medical MRI Segmentation: Brain Tissue with White Matter Hyperintensity Segmentation
ZunHyan Rieu1, JeeYoung Kim2, Regina Ey Kim1
1Research Institute, NEUROPHET Inc., Seoul 06247, Korea.
Abstract:
White-matter hyperintensity (WMH) is a primary biomarker for small-vessel cerebrovascular disease, Alzheimer's disease (AD), and others. The association of WMH with brain structural changes has also recently been reported. Although fluid-attenuated inversion recovery (FLAIR) magnetic resonance imaging (MRI) provide valuable information about WMH, FLAIR does not provide other normal tissue information. The multi-modal analysis of FLAIR and T1-weighted (T1w) MRI is thus desirable for WMH-related brain aging studies. In clinical settings, however, FLAIR is often the only available modality. In this study, we thus propose a semi-supervised learning method for full brain segmentation using FLAIR. The results of our proposed method were compared with the reference labels, which were obtained by FreeSurfer segmentation on T1w MRI. The relative volume difference between the two sets of results shows that our proposed method has high reliability. We further evaluated our proposed WMH segmentation by comparing the Dice similarity coefficients of the reference and the results of our proposed method. We believe our semi-supervised learning method has a great potential for use for other MRI sequences and will encourage others to perform brain tissue segmentation using MRI modalities other than T1w.
Insights
This study introduces a semi-supervised learning method for brain segmentation using only FLAIR MRI. The method accurately segments white-matter hyperintensities (WMH), crucial for diagnosing neurological conditions.
Area of Science:
- Neuroimaging
- Medical Image Analysis
- Artificial Intelligence
Background:
- White-matter hyperintensity (WMH) is a key biomarker for cerebrovascular disease and Alzheimer's disease.
- FLAIR MRI is valuable for WMH detection but lacks normal tissue information, necessitating multi-modal analysis.
- Clinical settings often limit MRI availability to FLAIR sequences.
Purpose of the Study:
- To develop a semi-supervised learning method for full brain segmentation using only FLAIR MRI.
- To assess the reliability and accuracy of the proposed segmentation method for WMH analysis.
Main Methods:
- A semi-supervised learning approach was employed for brain segmentation.
- Segmentation results were compared against reference labels derived from FreeSurfer segmentation on T1w MRI.
- Relative volume differences and Dice similarity coefficients were used for evaluation.
Main Results:
- The proposed method demonstrated high reliability in segmenting brain structures using FLAIR MRI.
- Quantitative comparisons showed good agreement between the proposed method's results and reference segmentations.
Conclusions:
- The semi-supervised learning method offers a reliable approach for brain segmentation using FLAIR MRI, even without T1w data.
- This method has potential for broader applications in MRI-based brain tissue segmentation and analysis of neurological conditions.
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