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.
Highlights
Automated quantitative metrics of structural magnetic resonance imaging (MRI), such as cortical volume or thickness, have commonly been used as objective indicators of neurodegeneration related to aging, stroke, and dementia
We introduced a semi-supervised learning method for brain tissue segmentation using only fluidattenuated inversion recovery (FLAIR) MRI
With our brain segmentation results, we demonstrated that our FLAIR MRI segmentation is just as reliable as segmentation using its paired T1w MRI
Summary
Automated quantitative metrics of structural magnetic resonance imaging (MRI), such as cortical volume or thickness, have commonly been used as objective indicators of neurodegeneration related to aging, stroke, and dementia. They have been combined with other MRI-based biomarkers such as white-matter hyperintensity (WMH) for investigation in Alzheimer’s disease (AD) or aging research [1,2]. Larger WMH regions are associated with an accelerated cognitive decline and increased risk for AD [4]. WMH predicts conversion from mild cognitive impairment to AD [6]. WMH has been reported to have a relationship with structural changes and cognitive performance, especially with respect to processing speed, even in cognitively unimpaired participants [7]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
