title: Accuracy and reproducibility of manual and semiautomated quantification of MS lesions by MRI.
abstract: OBJECTIVE
  To evaluate the accuracy, reproducibility, and speed of two semiautomated methods for quantifying total white matter lesion burden in multiple sclerosis (MS) patients with respect to manual tracing and to other methods presented in recent literature.
  
  
  METHODS
  Two methods involving the use of MRI for semiautomated quantification of total lesion burden in MS patients were examined. The first method, geometrically constrained region growth (GEORG), requires user specification of lesion location. The second technique, directed multispectral segmentation (DMSS), requires only the location of a single exemplar lesion. Test data sets included both clinical MS data and MS brain phantoms.
  
  
  RESULTS
  The mean processing times were 60 minutes for manual tracing, 10 minutes for region growth, and 3 minutes for directed segmentation. Intra- and interoperator coefficients of variation (CVs) were 5.1% and 16.5% for manual tracing, 1.4% and 2.3% for region growth, and 1.5% and 5.2% for directed segmentation. The average deviations from manual tracing were 9% for region growth and 5.7% for directed segmentation.
  
  
  CONCLUSIONS
  Both semiautomated methods were shown to have a significant advantage over manual tracing in terms of speed and precision. The accuracy of both methods was acceptable, given the high variability of the manual results.
authors.affiliation: Department of Radiology, University of Rochester Medical Center, Rochester, New York 14580, USA. edashton@ece.rochester.edu
authors.forename: Edward A
authors.initials: EA
authors.lastname: Ashton
authors.forename: Chihiro
authors.initials: C
authors.lastname: Takahashi
authors.forename: Michel J
authors.initials: MJ
authors.lastname: Berg
authors.forename: Andrew
authors.initials: A
authors.lastname: Goodman
authors.forename: Saara
authors.initials: S
authors.lastname: Totterman
authors.forename: Sven
authors.initials: S
authors.lastname: Ekholm
mesh_terms: D000465:Algorithms; D001921:Brain / Q000473:pathology*; D001932:Brain Neoplasms / Q000473:pathology*; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods* / Q000706:statistics & numerical data; D008279:Magnetic Resonance Imaging / Q000379:methods* / Q000706:statistics & numerical data; D009103:Multiple Sclerosis / Q000473:pathology*; D015588:Observer Variation; D019047:Phantoms, Imaging; D015203:Reproducibility of Results; D013997:Time Factors