title: Automated white matter total lesion volume segmentation in diabetes.
abstract: OBJECTIVE
  WM lesion segmentation is often performed with the use of subjective rating scales because manual methods are laborious and tedious; however, automated methods are now available. We compared the performance of total lesion volume grading computed by use of an automated WM lesion segmentation algorithm with that of subjective rating scales and expert manual segmentation in a cohort of subjects with type 2 diabetes.
  
  
  METHODS
  Structural T1 and FLAIR MR imaging data from 50 subjects with diabetes (age, 67.7 ± 7.2 years) and 50 nondiabetic sibling pairs (age, 67.5 ± 9.4 years) were evaluated in an institutional review board-approved study. WM lesion segmentation maps and total lesion volume were generated for each subject by means of the Statistical Parametric Mapping (SPM8) Lesion Segmentation Toolbox. Subjective WM lesion grade was determined by means of a 0-9 rating scale by 2 readers. Ground-truth total lesion volume was determined by means of manual segmentation by experienced readers. Correlation analyses compared manual segmentation total lesion volume with automated and subjective evaluation methods.
  
  
  RESULTS
  Correlation between average lesion segmentation and ground-truth total lesion volume was 0.84. Maximum correlation between the Lesion Segmentation Toolbox and ground-truth total lesion volume (ρ = 0.87) occurred at the segmentation threshold of k = 0.25, whereas maximum correlation between subjective lesion segmentation and the Lesion Segmentation Toolbox (ρ = 0.73) occurred at k = 0.15. The difference between the 2 correlation estimates with ground-truth was not statistically significant. The lower segmentation threshold (0.15 versus 0.25) suggests that subjective raters overestimate WM lesion burden.
  
  
  CONCLUSIONS
  We validate the Lesion Segmentation Toolbox for determining total lesion volume in diabetes-enriched populations and compare it with a common subjective WM lesion rating scale. The Lesion Segmentation Toolbox is a readily available substitute for subjective WM lesion scoring in studies of diabetes and other populations with changes of leukoaraiosis.
authors.affiliation: From Advanced Neuroscience Imaging Research Laboratory.
authors.forename: J A
authors.initials: JA
authors.lastname: Maldjian
authors.forename: C T
authors.initials: CT
authors.lastname: Whitlow
authors.forename: B N
authors.initials: BN
authors.lastname: Saha
authors.forename: G
authors.initials: G
authors.lastname: Kota
authors.forename: C
authors.initials: C
authors.lastname: Vandergriff
authors.forename: E M
authors.initials: EM
authors.lastname: Davenport
authors.forename: J
authors.initials: J
authors.lastname: Divers
authors.forename: B I
authors.initials: BI
authors.lastname: Freedman
authors.forename: D W
authors.initials: DW
authors.lastname: Bowden
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D001921:Brain / Q000473:pathology*; D001931:Brain Mapping; D015331:Cohort Studies; D003924:Diabetes Mellitus, Type 2 / Q000150:complications / Q000473:pathology*; 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 / Q000150:complications / Q000473:pathology*; D008297:Male; D008875:Middle Aged; D009413:Nerve Fibers, Myelinated / Q000473:pathology*; D015588:Observer Variation; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity