This study is to demonstrate deep learning for automated artery-vein (AV) classification in optical coherence tomography angiography (OCTA). The AV-Net, a fully convolutional network (FCN) based on modified U-shaped CNN architecture, incorporates enface OCT and OCTA to differentiate arteries and veins. For the multi-modal training process, the enface OCT works as a near infrared fundus image to provide vessel intensity profiles, and the OCTA contains blood flow strength and vessel geometry features. A transfer learning process is also integrated to compensate for the limitation of available dataset size of OCTA, which is a relatively new imaging modality. By providing an average accuracy of 86.75%, the AV-Net promises a fully automated platform to foster clinical deployment of differential AV analysis in OCTA.

中文翻译：

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AV-Net：用于光学相干断层扫描血管造影的全自动动脉-静脉分类的深度学习

这项研究旨在证明在光学相干断层扫描血管造影（OCTA）中对自动脉络（AV）分类的深度学习。AV-Net是一种基于改进的U形CNN架构的全卷积网络（FCN），它结合了enct OCT和OCTA来区分动脉和静脉。对于多模式训练过程，enface OCT用作近红外眼底图像以提供血管强度分布图，而OCTA包含血流强度和血管几何特征。还集成了转移学习过程以补偿OCTA可用数据集大小的限制，这是一种相对较新的成像方式。通过提供86.75％的平均准确度，AV-Net有望提供一个完全自动化的平台，以促进OCTA中差动AV分析的临床部署。