title: Automatic segmentation of white matter hyperintensities by an extended FitzHugh & Nagumo reaction diffusion model.
abstract: OBJECTIVE
  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.
  
  
  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).
  
  
  CONCLUSIONS
  The proposed approach is efficient and could provide a more effective and convenient tool for clinical quantitative WMH analysis.
authors.affiliation: College of Engineering, Peking University, Beijing, China.
authors.forename: Shuangxi
authors.initials: S
authors.lastname: Ji
authors.forename: Changqing
authors.initials: C
authors.lastname: Ye
authors.forename: Fan
authors.initials: F
authors.lastname: Li
authors.forename: Wei
authors.initials: W
authors.lastname: Sun
authors.forename: Jue
authors.initials: J
authors.lastname: Zhang
authors.forename: Yining
authors.initials: Y
authors.lastname: Huang
authors.forename: Jing
authors.initials: J
authors.lastname: Fang
mesh_terms: D000465:Algorithms; D001927:Brain Diseases / Q000473:pathology*; D003198:Computer Simulation; D038524:Diffusion Magnetic Resonance Imaging / Q000379:methods*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008959:Models, Neurological; D009413:Nerve Fibers, Myelinated / Q000473:pathology*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity