Alam Minhaj, Le David, Son Taeyoon, Lim Jennifer I, Yao Xincheng
Department of Bioengineering, University of Illinois at Chicago, Chicago, IL 60607, USA.
These authors contributed equally to this work.
Biomed Opt Express. 2020 Aug 25;11(9):5249-5257. doi: 10.1364/BOE.399514. eCollection 2020 Sep 1.
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.
本研究旨在展示深度学习在光学相干断层扫描血管造影（OCTA）中进行自动动静脉（AV）分类的应用。AV-Net是一种基于改进的U形卷积神经网络（CNN）架构的全卷积网络（FCN），它结合了正面OCT和OCTA来区分动脉和静脉。在多模态训练过程中，正面OCT作为近红外眼底图像，提供血管强度轮廓，而OCTA包含血流强度和血管几何特征。还集成了迁移学习过程，以弥补OCTA（一种相对较新的成像模式）可用数据集大小的限制。AV-Net的平均准确率为86.75%，有望提供一个全自动平台，以促进OCTA中动静脉差异分析的临床应用。
