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Updated: May 18, 2026
Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images
Published on: January 7, 2019
Automatic segmentation of white matter hyperintensities by an extended FitzHugh & Nagumo reaction diffusion model
Shuangxi Ji1, Changqing Ye, Fan Li
1College of Engineering, Peking University, Beijing, China.
Purpose:
To evaluate the efficiency and reproducibility of the extended FitzHugh & Nagumo (FHN) reaction-diffusion model proposed in this study for white matter hyperintensities (WMH) segmentation.
Materials And Methods:
Five types of magnetic resonance T2-weighted fluid-attenuated inversion-recovery (T2FLAIR) images of 127 patients with different scanning parameters from five clinical scanner systems were selected for this study. After skull and scalp removal and denoise, the T2FLAIR images were processed by the proposed extended FHN model to obtain WMH. This new technique replaced the global threshold constant with a local threshold matrix.
Results:
There was no significant difference between the segmentation results of the training set and the manual contouring against those between the test set and the manual contouring based on similarity index (SI) values (P = 0.5217). The SI values of the five types of T2FLAIR images were 86.0% ± 15.4%, 85.8% ± 10.5%, 84.1% ± 14.8%, 87.2% ± 14.6%, 86.3% ± 12.7%, respectively, comparing the segmentation results using the proposed method to the manual delineations. The overall SI value of the images was 86.5% ± 14.5%. This approach also demonstrated a better WMH segmentation performance over its classic form (P < 0.001).
Conclusion:
The proposed approach is efficient and could provide a more effective and convenient tool for clinical quantitative WMH analysis.
