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Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images
Published on: January 7, 2019
A fully automated method for quantifying and localizing white matter hyperintensities on MR images
Minjie Wu1, Caterina Rosano, Meryl Butters
1Department of Electrical and Computer Engineering, University of Pittsburgh, USA.
Abstract:
White matter hyperintensities (WMH), commonly found on T2-weighted FLAIR brain MR images in the elderly, are associated with a number of neuropsychiatric disorders, including vascular dementia, Alzheimer's disease, and late-life depression. Previous MRI studies of WMHs have primarily relied on the subjective and global (i.e., full-brain) ratings of WMH grade. In the current study we implement and validate an automated method for quantifying and localizing WMHs. We adapt a fuzzy-connected algorithm to automate the segmentation of WMHs and use a demons-based image registration to automate the anatomic localization of the WMHs using the Johns Hopkins University White Matter Atlas. The method is validated using the brain MR images acquired from eleven elderly subjects with late-onset late-life depression (LLD) and eight elderly controls. This dataset was chosen because LLD subjects are known to have significant WMH burden. The volumes of WMH identified in our automated method are compared with the accepted gold standard (manual ratings). A significant correlation of the automated method and the manual ratings is found (P<0.0001), thus demonstrating similar WMH quantifications of both methods. As has been shown in other studies (e.g. [Taylor, W.D., MacFall, J.R., Steffens, D.C., Payne, M.E., Provenzale, J.M., Krishnan, K.R., 2003. Localization of age-associated white matter hyperintensities in late-life depression. Progress in Neuro-Psychopharmacology and Biological Psychiatry. 27 (3), 539-544.]), we found there was a significantly greater WMH burden in the LLD subjects versus the controls for both the manual and automated method. The effect size was greater for the automated method, suggesting that it is a more specific measure. Additionally, we describe the anatomic localization of the WMHs in LLD subjects as well as in the control subjects, and detect the regions of interest (ROIs) specific for the WMH burden of LLD patients. Given the emergence of large NeuroImage databases, techniques, such as that described here, will allow for a better understanding of the relationship between WMHs and neuropsychiatric disorders.
Insights
This study introduces an automated method to quantify and locate white matter hyperintensities (WMH) in elderly brains. The automated approach accurately measures WMH burden, showing greater sensitivity in detecting differences in late-life depression patients.
Area of Science:
- Neuroimaging
- Medical Image Analysis
- Neurology
Background:
- White matter hyperintensities (WMH) are common in the elderly and linked to neuropsychiatric disorders like dementia and depression.
- Previous WMH assessments relied on subjective global ratings, limiting precision.
- Late-onset late-life depression (LLD) is associated with a significant WMH burden.
Purpose of the Study:
- To develop and validate an automated method for quantifying and localizing WMHs using MRI.
- To compare the automated method's WMH quantification with manual ratings.
- To investigate WMH burden differences between LLD patients and elderly controls.
Main Methods:
- Adapted a fuzzy-connected algorithm for automated WMH segmentation.
- Employed demons-based image registration for automated anatomic localization using the Johns Hopkins University White Matter Atlas.
- Validated the method on MRI data from 11 LLD subjects and 8 elderly controls.
Main Results:
- The automated WMH quantification strongly correlated with manual ratings (P<0.0001).
- LLD subjects exhibited a significantly greater WMH burden than controls, confirmed by both methods.
- The automated method demonstrated a larger effect size, indicating higher specificity.
Conclusions:
- The automated method provides accurate and reliable quantification and localization of WMHs.
- This technique enhances the specificity of WMH measurement, particularly in neuropsychiatric conditions like LLD.
- Automated WMH analysis is crucial for understanding their role in neuropsychiatric disorders, especially with growing neuroimaging databases.
