title: Automatic segmentation and quantitative analysis of white matter hyperintensities on FLAIR images using trimmed-likelihood estimator.
abstract: OBJECTIVE
  Quantitative analysis of white matter hyperintensities (WMHs) on fluid-attenuated inversion recovery (FLAIR) images provides information for disease tracking, therapeutic efficacy assessment, and cognitive science research. This study developed an automatic segmentation method to detect and quantify WMHs on FLAIR images. This study aims to assess the accuracy and reproducibility of the proposed method.
  
  
  METHODS
  The FLAIR images of 82 patients (58-84 years) with different WMH burdens were acquired with the same 3T scanner (Intera-achieva SMI-2.1; Philip Medical System, Sixth Affiliated People's Hospital, Shanghai, China). The FLAIR images were preprocessed through brain extraction and intensity inhomogeneity correction. An anatomy atlas built from a set of 20 patients with different WMH burdens (mild, 11 patients; moderate, 6 patients; and severe, 3 patients) was used to estimate a control parameter in the framework of segmentation. The general flow for WMH segmentation included classification of foreground and background regions, detection of abnormally high signals, and WMH refinement. The performance of automatic segmentation was evaluated by a volumetric comparison with manual segmentation on patients with different WMH burdens.
  
  
  RESULTS
  Similarity index values for the accuracy of automatic segmentation compared to manual segmentation were consistently high for patients with different WMH burdens (mild, 0.78 ± 0.08; moderate, 0.83 ± 0.06; severe, 0.84 ± 0.08; and total, 0.80 ± 0.08). Linear regression demonstrated a strong correlation between the WMH volumes measured by the two methods in all patients (r = 0.98, P = .006). Small average differences were detected between the WMH volumes obtained through manual and automatic segmentations (mild, 4.76%; moderate, 6.84%; and severe, 7.59%). The results of Bland-Altman analysis show a system bias of 0.68 cm(3) and a standard deviation of 2.10 cm(3) over the range of 2.58-53.9 cm(3).
  
  
  CONCLUSIONS
  The developed method is accurate and efficient in detecting and quantifying differently sized WMHs on FLAIR images. Automatic segmentation is a promising computer-aided diagnostic tool to study WMHs in aging and dementia in basic research and even in clinical trials.
authors.affiliation: School of Biomedical Engineering and Med-X Research Institute, Shanghai Jiao Tong University, Room 123, 3 Teaching Building, No. 1954, Huashan Rd, Shanghai 200030, China.
authors.forename: Rui
authors.initials: R
authors.lastname: Wang
authors.affiliation: School of Biomedical Engineering and Med-X Research Institute, Shanghai Jiao Tong University, Room 123, 3 Teaching Building, No. 1954, Huashan Rd, Shanghai 200030, China.
authors.forename: Chao
authors.initials: C
authors.lastname: Li
authors.affiliation: School of Biomedical Engineering and Med-X Research Institute, Shanghai Jiao Tong University, Room 123, 3 Teaching Building, No. 1954, Huashan Rd, Shanghai 200030, China.
authors.forename: Jie
authors.initials: J
authors.lastname: Wang
authors.affiliation: Institute of Diagnostic and Interventional Radiology, Sixth Affiliated People's Hospital, Shanghai Jiao Tong University, Shanghai, China.
authors.forename: Xiaoer
authors.initials: X
authors.lastname: Wei
authors.affiliation: Institute of Diagnostic and Interventional Radiology, Sixth Affiliated People's Hospital, Shanghai Jiao Tong University, Shanghai, China.
authors.forename: Yuehua
authors.initials: Y
authors.lastname: Li
authors.affiliation: School of Biomedical Engineering and Med-X Research Institute, Shanghai Jiao Tong University, Room 123, 3 Teaching Building, No. 1954, Huashan Rd, Shanghai 200030, China.
authors.forename: Chun
authors.initials: C
authors.lastname: Hui
authors.affiliation: CREATICS, CNRS UMR 5220, Inserm 1044, INSA Lyon, Villeurbanne, France.
authors.forename: Yuemin
authors.initials: Y
authors.lastname: Zhu
authors.affiliation: School of Biomedical Engineering and Med-X Research Institute, Shanghai Jiao Tong University, Room 123, 3 Teaching Building, No. 1954, Huashan Rd, Shanghai 200030, China. Electronic address: suzhang@sjtu.edu.cn.
authors.forename: Su
authors.initials: S
authors.lastname: Zhang
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D001921:Brain / Q000473:pathology*; D001927:Brain Diseases / Q000473:pathology*; D005260:Female; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D008875:Middle Aged; D009413:Nerve Fibers, Myelinated / Q000473:pathology*
keywords: Gaussian mixture model; White matter hyperintensities; automatic segmentation; trimmed likelihood estimator