Purpose: To report an image segmentation algorithm that was developed to provide quantitative thickness measurement of six retinal layers in optical coherence tomography (OCT) images.
Design: Prospective cross-sectional study.
Methods: Imaging was performed with time- and spectral-domain OCT instruments in 15 and 10 normal healthy subjects, respectively. A dedicated software algorithm was developed for boundary detection based on a 2-dimensional edge detection scheme, enhancing edges along the retinal depth while suppressing speckle noise. Automated boundary detection and quantitative thickness measurements derived by the algorithm were compared with measurements obtained from boundaries manually marked by three observers. Thickness profiles for six retinal layers were generated in normal subjects.
Results: The algorithm identified seven boundaries and measured thickness of six retinal layers: nerve fiber layer, inner plexiform layer and ganglion cell layer, inner nuclear layer, outer plexiform layer, outer nuclear layer and photoreceptor inner segments (ONL+PIS), and photoreceptor outer segments (POS). The root mean squared error between the manual and automatic boundary detection ranged between 4 and 9 mum. The mean absolute values of differences between automated and manual thickness measurements were between 3 and 4 mum, and comparable to interobserver differences. Inner retinal thickness profiles demonstrated minimum thickness at the fovea, corresponding to normal anatomy. The OPL and ONL+PIS thickness profiles respectively displayed a minimum and maximum thickness at the fovea. The POS thickness profile was relatively constant along the scan through the fovea.
Conclusions: The application of this image segmentation technique is promising for investigating thickness changes of retinal layers attributable to disease progression and therapeutic intervention.