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Updated: May 28, 2026
Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images
Published on: January 7, 2019
The effect of hypointense white matter lesions on automated gray matter segmentation in multiple sclerosis
Rose Gelineau-Morel1, Valentina Tomassini, Mark Jenkinson
1Oxford Centre for Functional MRI of the Brain, Nuffield Department of Clinical Neurosciences, University of Oxford, United Kingdom.
Abstract:
Previous imaging studies assessing the relationship between white matter (WM) damage and matter (GM) atrophy have raised the concern that Multiple Sclerosis (MS) WM lesions may affect measures of GM volume by inducing voxel misclassification during intensity-based tissue segmentation. Here, we quantified this misclassification error in simulated and real MS brains using a lesion-filling method. Using this method, we also corrected GM measures in patients before comparing them with controls in order to assess the impact of this lesion-induced misclassification error in clinical studies. We found that higher WM lesion volumes artificially reduced total GM volumes. In patients, this effect was about 72% of that predicted by simulation. Misclassified voxels were located at the GM/WM border and could be distant from lesions. Volume of individual deep gray matter (DGM) structures generally decreased with higher lesion volumes, consistent with results from total GM. While preserving differences in GM volumes between patients and controls, lesion-filling correction revealed more lateralised DGM shape changes in patients, which were not evident with the original images. Our results confirm that WM lesions can influence MRI measures of GM volume and shape in MS patients through their effect on intensity-based GM segmentation. The greater effect of lesions at increasing levels of damage supports the use of lesion-filling to correct for this problem and improve the interpretability of the results. Volumetric or morphometric imaging studies, where lesion amount and characteristics may vary between groups of patients or change over time, may especially benefit from this correction.
Insights
Multiple Sclerosis (MS) white matter lesions can distort gray matter (GM) volume measurements in MRI scans. A lesion-filling method corrected these errors, improving the accuracy of GM volume and shape analysis in MS patients.
Area of Science:
- Neuroimaging
- Neurology
- Biomedical Engineering
Background:
- Multiple Sclerosis (MS) is characterized by white matter (WM) lesions.
- Previous studies raised concerns about WM lesions affecting gray matter (GM) volume measurements due to segmentation errors.
Purpose of the Study:
- To quantify misclassification errors in GM volume estimation caused by WM lesions in MS.
- To assess the impact of these errors in clinical studies using a lesion-filling method.
- To correct GM measures and evaluate their impact on volumetric and morphometric analyses.
Main Methods:
- Utilized a lesion-filling technique to quantify misclassification errors in simulated and real MS brain MRI scans.
- Applied the lesion-filling method to correct GM volumes in MS patients before comparing with controls.
- Analyzed the impact of corrected GM measures on volumetric and shape analyses.
Main Results:
- Higher WM lesion volumes were found to artificially reduce total GM volumes.
- The lesion-induced underestimation of GM volume in patients was approximately 72% of the simulated effect.
- Lesion-filling correction preserved GM volume differences between patients and controls while revealing subtle, lateralized deep GM shape changes.
Conclusions:
- WM lesions significantly influence MRI-based GM volume and shape measurements in MS patients via intensity-based segmentation.
- The lesion-filling method effectively corrects for lesion-induced misclassification, enhancing the interpretability of imaging results.
- This correction is particularly beneficial for longitudinal or comparative volumetric and morphometric studies in MS.
