论文标题
AV-NET：光学相干层析成像造影术中全自动动脉静脉分类的深度学习
AV-Net: Deep learning for fully automated artery-vein classification in optical coherence tomography angiography
论文作者
论文摘要
这项研究是为了证明光学连贯性层析成像血管造影（OCTA）中自动动脉静脉（AV）分类的深度学习。基于修改的U形CNN体系结构的完全卷积网络（FCN）AV-NET结合了ENFACE OCT和OCTA，以区分动脉和静脉。对于多模式训练过程，ENFACE OCT可作为近红外的底面图像来提供血管强度曲线，八颗八晶体包含血流强度和血管几何特征。还集成了转移学习过程，以弥补八八的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％的平均准确性，AV-NET有望提供一个完全自动化的平台，以促进八八颗差异AV分析的临床部署。
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.
