Improved assessment of multiple sclerosis lesion segmentation agreement via detection and outline error estimates

David S. Wack, Michael G. Dwyer, Niels Bergsland, Carol Di Perri, Laura Ranza, Sara Hussein, Deepa Ramasamy, Guy Poloni, Robert Zivadinov

Research output: Contribution to journalArticle

Abstract

Background: Presented is the method " Detection and Outline Error Estimates" (DOEE) for assessing rater agreement in the delineation of multiple sclerosis (MS) lesions. The DOEE method divides operator or rater assessment into two parts: 1) Detection Error (DE) -- rater agreement in detecting the same regions to mark, and 2) Outline Error (OE) -- agreement of the raters in outlining of the same lesion.Methods: DE, OE and Similarity Index (SI) values were calculated for two raters tested on a set of 17 fluid-attenuated inversion-recovery (FLAIR) images of patients with MS. DE, OE, and SI values were tested for dependence with mean total area (MTA) of the raters' Region of Interests (ROIs).Results: When correlated with MTA, neither DE (ρ = .056, p=.83) nor the ratio of OE to MTA (ρ = .23, p=.37), referred to as Outline Error Rate (OER), exhibited significant correlation. In contrast, SI is found to be strongly correlated with MTA (ρ = .75, p <.001). Furthermore, DE and OER values can be used to model the variation in SI with MTA.Conclusions: The DE and OER indices are proposed as a better method than SI for comparing rater agreement of ROIs, which also provide specific information for raters to improve their agreement.

Original languageEnglish
Article number17
JournalBMC Medical Imaging
Volume12
DOIs
Publication statusPublished - Jul 19 2012

Keywords

  • Detection and outline error estimates
  • Index
  • Jaccard Index
  • Kappa
  • Lesion
  • Measure
  • Metric
  • MRI
  • Multiple sclerosis
  • Operator agreement
  • Rater agreement
  • ROI
  • Similarity index

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging

Fingerprint Dive into the research topics of 'Improved assessment of multiple sclerosis lesion segmentation agreement via detection and outline error estimates'. Together they form a unique fingerprint.

  • Cite this

    Wack, D. S., Dwyer, M. G., Bergsland, N., Di Perri, C., Ranza, L., Hussein, S., Ramasamy, D., Poloni, G., & Zivadinov, R. (2012). Improved assessment of multiple sclerosis lesion segmentation agreement via detection and outline error estimates. BMC Medical Imaging, 12, [17]. https://doi.org/10.1186/1471-2342-12-17