Synthesizing the degree of polarization uniformity from non-polarization-sensitive optical coherence tomography signals using a neural network

TL;DR

This paper introduces a neural network method to estimate polarization uniformity images from standard OCT scans, enabling detection of retinal abnormalities without specialized polarization-sensitive equipment.

Contribution

A novel neural network approach to synthesize DOPU images from conventional OCT data, reducing system complexity and expanding clinical utility.

Findings

High agreement between synthesized and ground truth DOPU in detecting RPE abnormalities

Recall of 0.869 and precision of 0.920 in retinal disease cases

No abnormalities detected in healthy volunteer cases

Abstract

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.