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Updated: Jun 15, 2026
Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images
Published on: January 7, 2019
Topology-corrected segmentation and local intensity estimates for improved partial volume classification of brain
Andrea Rueda1, Oscar Acosta, Michel Couprie
1CSIRO Preventative Health National Research Flagship, ICTC, The Australian e-Health Research Centre-BioMedIA, Herston, Australia.
Abstract:
In magnetic resonance imaging (MRI), accuracy and precision with which brain structures may be quantified are frequently affected by the partial volume (PV) effect. PV is due to the limited spatial resolution of MRI compared to the size of anatomical structures. Accurate classification of mixed voxels and correct estimation of the proportion of each pure tissue (fractional content) may help to increase the precision of cortical thickness estimation in regions where this measure is particularly difficult, such as deep sulci. The contribution of this work is twofold: on the one hand, we propose a new method to label voxels and compute tissue fractional content, integrating a mechanism for detecting sulci with topology preserving operators. On the other hand, we improve the computation of the fractional content of mixed voxels using local estimation of pure tissue intensity means. Accuracy and precision were assessed using simulated and real MR data and comparison with other existing approaches demonstrated the benefits of our method. Significant improvements in gray matter (GM) classification and cortical thickness estimation were brought by the topology correction. The fractional content root mean squared error diminished by 6.3% (p<0.01) on simulated data. The reproducibility error decreased by 8.8% (p<0.001) and the Jaccard similarity measure increased by 3.5% on real data. Furthermore, compared with manually guided expert segmentations, the similarity measure was improved by 12.0% (p<0.001). Thickness estimation with the proposed method showed a higher reproducibility compared with the measure performed after partial volume classification using other methods.
Insights
This study introduces a novel method to improve magnetic resonance imaging (MRI) analysis by accurately classifying brain tissue in mixed voxels. The new approach enhances precision in estimating brain structure volumes and cortical thickness, particularly in challenging deep sulci regions.
Area of Science:
- Neuroimaging
- Medical Image Analysis
- Computational Anatomy
Background:
- Partial volume (PV) effect in MRI limits accuracy and precision of brain structure quantification due to limited spatial resolution.
- Accurate classification of mixed voxels and estimation of tissue fractional content are crucial for improving cortical thickness precision, especially in deep sulci.
Purpose of the Study:
- To propose a novel method for voxel labeling and tissue fractional content computation using topology-preserving operators for sulci detection.
- To enhance mixed voxel fractional content estimation by incorporating local pure tissue intensity means.
Main Methods:
- Developed a new method integrating sulci detection with topology-preserving operators for voxel labeling and fractional content calculation.
- Improved fractional content estimation using local means of pure tissue intensities.
- Validated the method using simulated and real MRI data, comparing it with existing approaches.
Main Results:
- The proposed method significantly improved gray matter (GM) classification and cortical thickness estimation.
- Fractional content root mean squared error decreased by 6.3% on simulated data (p<0.01).
- On real data, reproducibility error decreased by 8.8% (p<0.001), Jaccard similarity increased by 3.5%, and similarity to expert segmentations improved by 12.0% (p<0.001).
Conclusions:
- The novel method enhances the accuracy and precision of MRI-based brain structure quantification, particularly cortical thickness.
- Topology correction and improved fractional content estimation lead to significant gains in classification and reproducibility.
- The method offers superior performance compared to existing partial volume classification techniques.
