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- Title
Improved assessment of multiple sclerosis lesion segmentation agreement via detection and outline error estimates.
- Authors
Wack, David S.; Dwyer, Michael G.; Bergsland, Niels; Di Perri, Carol; Ranza, Laura; Sara Hussein, Sara Hussein; Deepa Ramasamy, Deepa Ramasamy; Guy Poloni, Guy Poloni; Robert Zivadinov, Robert Zivadinov
- 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 (p = .056, p=.83) nor the ratio of OE to MTA (p = .23, p=.37), referred to as Outline Error Rate (OER), exhibited significant correlation. In contrast, SI is found to be strongly correlated with MTA (p = .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.
- Subjects
MULTIPLE sclerosis; MYELIN sheath diseases; DEMYELINATION; VIRUS diseases; NEUROLOGICAL disorders
- Publication
BMC Medical Imaging, 2012, Vol 12, Issue 1, p17
- ISSN
1471-2342
- Publication type
Article
- DOI
10.1186/1471-2342-12-17