Thickness Profiles of Retinal Layers by Optical Coherence Tomography Image Segmentation

Abstract

Purpose To report an image segmentation algorithm that was developed to provide quantitative thickness measurement of 6 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-D 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 3 observers. Thickness profiles for 6 retinal layers were generated in normal subjects. Results The algorithm identified 7 boundaries and measured thickness of 6 retinal layers: nerve fiber layer (NFL), inner plexiform layer and ganglion cell layer (IPL+GCL), inner nuclear layer (INL), outer plexiform layer (OPL), outer nuclear layer and photoreceptor inner segments (ONL+PIS), and photoreceptor outer segments (POS). The root mean squared error (RMSE) between the manual and automatic boundary detection ranged between 4 and 9 microns. The mean absolute values of differences between automated and manual thickness measurements were between 3 – 4 microns, and comparable to inter-observer differences. Inner retinal thickness profiles demonstrated minimum thickness at the fovea, corresponding to normal anatomy. The OPL and ONL+PIS thickness profiles displayed a minimum and maximum thickness at the fovea, respectively. 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 due to disease progression and therapeutic intervention.