Guo Yukun, Camino Acner, Zhang Miao, Wang Jie, Huang David, Hwang Thomas, Jia Yali
Casey Eye Institute, Oregon Health & Science University, Portland, OR 97239, USA.
Topcon Healthcare Solutions, Inc., Milpitas, CA 95035, USA.
Biomed Opt Express. 2018 Aug 24;9(9):4429-4442. doi: 10.1364/BOE.9.004429. eCollection 2018 Sep 1.
Advances in the retinal layer segmentation of structural optical coherence tomography (OCT) images have allowed the separation of capillary plexuses in OCT angiography (OCTA). With the increased scanning speeds of OCT devices and wider field images (≥10 mm on fast-axis), greater retinal curvature and anatomic variations have introduced new challenges. In this study, we developed a novel automated method to segment seven retinal layer boundaries and two retinal plexuses in wide-field OCTA images. The algorithm was initialized by a series of points forming a guidance point array that estimates the location of retinal layer boundaries. A guided bidirectional graph search method consisting of an improvement of our previous segmentation algorithm was used to search for the precise boundaries. We validated the method on normal and diseased eyes, demonstrating subpixel accuracy for all groups. By allowing independent visualization of the superficial and deep plexuses, this method shows potential for the detection of plexus-specific peripheral vascular abnormalities.
结构光学相干断层扫描（OCT）图像视网膜层分割技术的进步，使得在OCT血管造影（OCTA）中能够分离毛细血管丛。随着OCT设备扫描速度的提高以及视野更宽的图像（快轴上≥10毫米）的出现，更大的视网膜曲率和解剖变异带来了新的挑战。在本研究中，我们开发了一种新颖的自动化方法，用于在宽视野OCTA图像中分割七个视网膜层边界和两个视网膜丛。该算法由一系列形成引导点阵列的点初始化，该阵列估计视网膜层边界的位置。使用一种由我们之前的分割算法改进而来的引导双向图搜索方法来搜索精确边界。我们在正常和患病眼睛上验证了该方法，证明所有组均具有亚像素精度。通过允许独立可视化浅表和深部丛，该方法显示出检测丛特异性周边血管异常的潜力。
