Table of Contents Author Guidelines Submit a Manuscript
Multiple Sclerosis International
Volume 2015 (2015), Article ID 136295, 10 pages
Research Article

Applying an Open-Source Segmentation Algorithm to Different OCT Devices in Multiple Sclerosis Patients and Healthy Controls: Implications for Clinical Trials

1Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
2Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD 21287, USA

Received 12 March 2015; Accepted 1 May 2015

Academic Editor: Wolfgang Bruck

Copyright © 2015 Pavan Bhargava et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Background. The lack of segmentation algorithms operative across optical coherence tomography (OCT) platforms hinders utility of retinal layer measures in MS trials. Objective. To determine cross-sectional and longitudinal agreement of retinal layer thicknesses derived from an open-source, fully-automated, segmentation algorithm, applied to two spectral-domain OCT devices. Methods. Cirrus HD-OCT and Spectralis OCT macular scans from 68 MS patients and 22 healthy controls were segmented. A longitudinal cohort comprising 51 subjects (mean follow-up: 1.4 ± 0.9 years) was also examined. Bland-Altman analyses and interscanner agreement indices were utilized to assess agreement between scanners. Results. Low mean differences (−2.16 to 0.26 μm) and narrow limits of agreement (LOA) were noted for ganglion cell and inner and outer nuclear layer thicknesses cross-sectionally. Longitudinally we found low mean differences (−0.195 to 0.21 μm) for changes in all layers, with wider LOA. Comparisons of rate of change in layer thicknesses over time revealed consistent results between the platforms. Conclusions. Retinal thickness measures for the majority of the retinal layers agree well cross-sectionally and longitudinally between the two scanners at the cohort level, with greater variability at the individual level. This open-source segmentation algorithm enables combining data from different OCT platforms, broadening utilization of OCT as an outcome measure in MS trials.