  Degree of polarization uniformity (DOPU) imaging obtained by
polarization-sensitive optical coherence tomography (PS-OCT) has the potential
to provide biomarkers for retinal diseases. It highlights abnormalities in the
retinal pigment epithelium that are not always clear in the OCT intensity
images. However, a PS-OCT system is more complicated than conventional OCT. We
present a neural-network-based approach to estimate the DOPU from standard OCT
images. DOPU images were used to train a neural network to synthesize the DOPU
from single-polarization-component OCT intensity images. DOPU images were then
synthesized by the neural network, and the clinical findings from ground truth
DOPU and synthesized DOPU were compared. There is a good agreement in the
findings for RPE abnormalities: recall was 0.869 and precision was 0.920 for 20
cases with retinal diseases. In five cases of healthy volunteers, no
abnormalities were found in either the synthesized or ground truth DOPU images.
The proposed neural-network-based DOPU synthesis method demonstrates the
potential of extending the features of retinal non-PS OCT.
