  Background: Changes in choroidal thickness are associated with various ocular
diseases and the choroid can be imaged using spectral-domain optical coherence
tomography (SDOCT) and enhanced depth imaging OCT (EDIOCT). New Method: Eighty
macular SDOCT volumes from 80 patients were obtained using the Zeiss Cirrus
machine. Eleven additional control subjects had two Cirrus scans done in one
visit along with EDIOCT using the Heidelberg Spectralis machine. To
automatically segment choroidal layers from the OCT volumes, our
graph-theoretic approach was utilized. The segmentation results were compared
with reference standards from two graders, and the accuracy of automated
segmentation was calculated using unsigned to signed border positioning
thickness errors and Dice similarity coefficient (DSC). The repeatability and
reproducibility of our choroidal thicknesses were determined by intraclass
correlation coefficient (ICC), coefficient of variation (CV), and repeatability
coefficient (RC). Results: The mean unsigned to signed border positioning
errors for the choroidal inner and outer surfaces are 3.39plusminus1.26microns
(mean plusminus SD) to minus1.52 plusminus 1.63microns and 16.09 plusminus
6.21microns to 4.73 plusminus 9.53microns, respectively. The mean unsigned to
signed choroidal thickness errors are 16.54 plusminus 6.47microns to 6.25
plusminus 9.91microns, and the mean DSC is 0.949 plusminus 0.025. The ICC (95%
CI), CV, RC values are 0.991 (0.977 to 0.997), 2.48%, 3.15microns for the
repeatability and 0.991 (0.977 to 0.997), 2.49%, 0.53microns for the
reproducibility studies, respectively. Comparison with Existing Method(s): The
proposed method outperformed our previous method using choroidal vessel
segmentation and inter-grader variability. Conclusions: This automated
segmentation method can reliably measure choroidal thickness using different
OCT platforms.
