title: Automatic segmentation and volumetric quantification of white matter hyperintensities on fluid-attenuated inversion recovery images using the extreme value distribution.
abstract: BACKGROUND
  This study aims to develop an automatic segmentation framework on the basis of extreme value distribution (EVD) for the detection and volumetric quantification of white matter hyperintensities (WMHs) on fluid-attenuated inversion recovery (FLAIR) images.
  
  
  METHODS
  Two EVD-based segmentation methods, namely the Gumbel and Fréchet segmentation, were developed to detect WMHs on FLAIR (slice thickness = 5 mm; TR/TE/TI = 11,000/120/2,800 ms; flip angle = 90°) images. Another automatic segmentation method using a trimmed likelihood estimator (TLE) was implemented for comparison with our proposed segmentation framework. The performances of the three automatic segmentation methods were evaluated by comparing with the manual segmentation method.
  
  
  RESULTS
  The Dice similarity coefficients (DSCs) of the two EVD-based segmentation methods were larger than those of the TLE-based segmentation method (Gumbel, 0.823 ± 0.063; Fréchet, 0.843 ± 0.057; TLE, 0.817 ± 0.068), demonstrating that the EVD-based segmentation outperformed the TLE-based segmentation. The Fréchet segmentation obtained larger DSCs on patients with moderate to severe lesion loads and a comparable performance on patients with mild lesion loads, indicating that the Fréchet segmentation was superior to the Gumbel segmentation. The Gumbel segmentation underestimated the lesion volumes of all patients, whereas the Fréchet and TLE-based segmentation methods obtained overestimated lesion volumes (Manual, 13.71 ± 14.02 cc; Gumbel, 12.73 ± 13.21 cc; Fréchet, 13.88 ± 13.96 cc; TLE, 13.54 ± 12.27 cc). Moreover, the EVD-based segmentation was demonstrated to be comparable to other state-of-the-art methods on a publicly available dataset.
  
  
  CONCLUSIONS
  The proposed EVD-based segmentation framework is a promising, effective, and convenient tool for volumetric quantification and further study of WMHs in aging and dementia.
authors.affiliation: School of Biomedical Engineering and Med-X Research Institute, Shanghai Jiao Tong University, Room 123, 3 Teaching Building, No. 1954, Huashan Road, Shanghai, China, 200030.
authors.forename: Rui
authors.initials: R
authors.lastname: Wang
authors.forename: Chao
authors.initials: C
authors.lastname: Li
authors.forename: Jie
authors.initials: J
authors.lastname: Wang
authors.forename: Xiaoer
authors.initials: X
authors.lastname: Wei
authors.forename: Yuehua
authors.initials: Y
authors.lastname: Li
authors.forename: Yuemin
authors.initials: Y
authors.lastname: Zhu
authors.forename: Su
authors.initials: S
authors.lastname: Zhang
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D003627:Data Interpretation, Statistical; D056324:Diffusion Tensor Imaging / Q000379:methods*; D005260:Female; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D049292:Leukoaraiosis / Q000473:pathology*; D008297:Male; D008875:Middle Aged; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D016008:Statistical Distributions; D066127:White Matter