Guo Yukun, Hormel Tristan T, Pi Shaohua, Wei Xiang, Gao Min, Morrison John C, Jia Yali
Casey Eye Institute, Oregon Health & Science University, Portland, OR 97239, USA.
Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR 97239, USA.
Biomed Opt Express. 2021 Jul 16;12(8):4889-4900. doi: 10.1364/BOE.431888. eCollection 2021 Aug 1.
The segmentation of retinal capillary angiograms from volumetric optical coherence tomographic angiography (OCTA) usually relies on retinal layer segmentation, which is time-consuming and error-prone. In this study, we developed a deep-learning-based method to segment vessels in the superficial vascular plexus (SVP), intermediate capillary plexus (ICP), and deep capillary plexus (DCP) directly from volumetric OCTA data. The method contains a three-dimensional convolutional neural network (CNN) for extracting distinct retinal layers, a custom projection module to generate three vascular plexuses from OCTA data, and three parallel CNNs to segment vasculature. Experimental results on OCTA data from rat eyes demonstrated the feasibility of the proposed method. This end-to-end network has the potential to simplify OCTA data processing on retinal vasculature segmentation. The main contribution of this study is that we propose a custom projection module to connect retinal layer segmentation and vasculature segmentation modules and automatically convert data from three to two dimensions, thus establishing an end-to-end method to segment three retinal capillary plexuses from volumetric OCTA without any human intervention.
从体积光学相干断层扫描血管造影（OCTA）中分割视网膜毛细血管血管造影通常依赖于视网膜层分割，这既耗时又容易出错。在本研究中，我们开发了一种基于深度学习的方法，可直接从体积OCTA数据中分割浅表血管丛（SVP）、中间毛细血管丛（ICP）和深部毛细血管丛（DCP）中的血管。该方法包含一个用于提取不同视网膜层的三维卷积神经网络（CNN）、一个用于从OCTA数据生成三个血管丛的自定义投影模块，以及三个用于分割脉管系统的并行CNN。对大鼠眼睛的OCTA数据进行的实验结果证明了该方法的可行性。这个端到端网络有潜力简化视网膜脉管系统分割中的OCTA数据处理。本研究的主要贡献在于，我们提出了一个自定义投影模块，以连接视网膜层分割和脉管系统分割模块，并自动将数据从三维转换为二维，从而建立一种无需任何人工干预即可从体积OCTA中分割三个视网膜毛细血管丛的端到端方法。
