论文标题
使用神经网络综合了非极化光学相干断层扫描信号的极化均匀度
Synthesizing the degree of polarization uniformity from non-polarization-sensitive optical coherence tomography signals using a neural network
论文作者
论文摘要
通过极化敏感的光学相干断层扫描（PS-OCT）获得的极化均匀度（DOPU）成像具有为视网膜疾病提供生物标志物的潜力。它突出了视网膜色素上皮的异常，在OCT强度图像中并不总是清楚的。但是，PS-OCT系统比常规OCT更为复杂。我们提出了一种基于神经网络的方法，可以从标准OCT图像中估算DOPU。 DOPU图像用于训练神经网络，以从单极化组分OCT强度图像中合成DOPU。然后通过神经网络合成DOPU图像，并比较了地面真实DOPU和合成DOPU的临床发现。 RPE异常的发现有一个很好的一致性：召回率为0.869，对于20例视网膜疾病病例，精度为0.920。在五个健康志愿者的情况下，在合成或地面真实图像中未发现异常。提出的基于神经网络的DOPU合成方法证明了扩展视网膜非PS OCT的特征的潜力。
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.
