使用商业和原型OCT-A平台进行深度学习血管分割和中央凹无血管区量化
Deep learning vessel segmentation and quantification of the foveal avascular zone using commercial and prototype OCT-A platforms
摘要：
光学相干断层扫描血管造影（OCT-A）图像中中央凹周围血管密度的自动量化面临着各种挑战，例如图像内和图像间信噪比可变、来自外部脉管系统层的投影伪影和运动伪影。这项研究证明了深度神经网络在健康和糖尿病眼睛中自动量化OCT-A图像的中心凹无血管区（FAZ）参数和中心凹周围血管密度方面的实用性。使用三个OCT-A系统获取中央凹区域的OCT-A图像：1060 nm扫频源（SS）-OCT原型，RTVue XR Avanti（Optovue Inc.，Fremont，CA）和ZEISS Angioplex（Carl Zeiss Meditec，Dublin，CA）。然后使用深度神经网络进行自动分割。四个FAZ形态测量参数（面积，最小/最大直径和偏心率）和中央凹周围血管密度被用作结果测量。所有三种器械平台的DNN血管分割的准确性、灵敏度和特异性相当。对于任何系统上的任何结局指标，未发现自动和手动分割测量的平均值之间存在显著差异。所有测量的组内相关系数（ICC）也良好（> 0.51）。OCT-A的自动深度学习血管分割可能适用于商业和研究目的，以更好地量化视网膜循环。
英文摘要：
Automatic quantification of perifoveal vessel densities in optical coherence tomography angiography (OCT-A) images face challenges such as variable intra- and inter-image signal to noise ratios, projection artefacts from outer vasculature layers, and motion artefacts. This study demonstrates the utility of deep neural networks for automatic quantification of foveal avascular zone (FAZ) parameters and perifoveal vessel density of OCT-A images in healthy and diabetic eyes. OCT-A images of the foveal region were acquired using three OCT-A systems: a 1060nm Swept Source (SS)-OCT prototype, RTVue XR Avanti (Optovue Inc., Fremont, CA), and the ZEISS Angioplex (Carl Zeiss Meditec, Dublin, CA). Automated segmentation was then performed using a deep neural network. Four FAZ morphometric parameters (area, min/max diameter, and eccentricity) and perifoveal vessel density were used as outcome measures. The accuracy, sensitivity and specificity of the DNN vessel segmentations were comparable across all three device platforms. No significant difference between the means of the measurements from automated and manual segmentations were found for any of the outcome measures on any system. The intraclass correlation coefficient (ICC) was also good (> 0.51) for all measurements. Automated deep learning vessel segmentation of OCT-A may be suitable for both commercial and research purposes for better quantification of the retinal circulation.
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