title: Improved operator agreement and efficiency using the minimum area contour change method for delineation of hyperintense multiple sclerosis lesions on FLAIR MRI.
abstract: BACKGROUND
  Activity of disease in patients with multiple sclerosis (MS) is monitored by detecting and delineating hyper-intense lesions on MRI scans. The Minimum Area Contour Change (MACC) algorithm has been created with two main goals: a) to improve inter-operator agreement on outlining regions of interest (ROIs) and b) to automatically propagate longitudinal ROIs from the baseline scan to a follow-up scan.
  
  
  METHODS
  The MACC algorithm first identifies an outer bound for the solution path, forms a high number of iso-contour curves based on equally spaced contour values, and then selects the best contour value to outline the lesion. The MACC software was tested on a set of 17 FLAIR MRI images evaluated by a pair of human experts and a longitudinal dataset of 12 pairs of T2-weighted Fluid Attenuated Inversion Recovery (FLAIR) images that had lesion analysis ROIs drawn by a single expert operator.
  
  
  RESULTS
  In the tests where two human experts evaluated the same MRI images, the MACC program demonstrated that it could markedly reduce inter-operator outline error. In the longitudinal part of the study, the MACC program created ROIs on follow-up scans that were in close agreement to the original expert's ROIs. Finally, in a post-hoc analysis of 424 follow-up scans 91% of propagated MACC were accepted by an expert and only 9% of the final accepted ROIS had to be created or edited by the expert.
  
  
  CONCLUSIONS
  When used with an expert operator's verification of automatically created ROIs, MACC can be used to improve inter- operator agreement and decrease analysis time, which should improve data collected and analyzed in multicenter clinical trials.
authors.affiliation: Buffalo Neuroimaging Analysis Center, Department of Neurology, School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York at Buffalo, Buffalo, NY, USA. dswack@buffalo.edu.
authors.forename: David S
authors.initials: DS
authors.lastname: Wack
authors.forename: Michael G
authors.initials: MG
authors.lastname: Dwyer
authors.forename: Niels
authors.initials: N
authors.lastname: Bergsland
authors.forename: Deepa
authors.initials: D
authors.lastname: Ramasamy
authors.forename: Carol
authors.initials: C
authors.lastname: Di Perri
authors.forename: Laura
authors.initials: L
authors.lastname: Ranza
authors.forename: Sara
authors.initials: S
authors.lastname: Hussein
authors.forename: Christopher
authors.initials: C
authors.lastname: Magnano
authors.forename: Kevin
authors.initials: K
authors.lastname: Seals
authors.forename: Robert
authors.initials: R
authors.lastname: Zivadinov
mesh_terms: D000465:Algorithms; D001921:Brain / Q000473:pathology*; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D009103:Multiple Sclerosis / Q000473:pathology*; D015588:Observer Variation; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity