Doheny Eye Institute, Los Angeles, California, United StatesbDoheny Eye Centers, Department of Ophthalmology, David Geffen School of Medicine at UCLA, Los Angeles, California, United States.
Shiley Eye Institute and Hamilton Glaucoma Center, Department of Ophthalmology University of California, San Diego, California, United States.
J Biomed Opt. 2017 Jun 1;22(6):66010. doi: 10.1117/1.JBO.22.6.066010.
The purpose was to create a three-dimensional (3-D) model of circumferential aqueous humor outflow (AHO) in a living human eye with an automated detection algorithm for Schlemm’s canal (SC) and first-order collector channels (CC) applied to spectral-domain optical coherence tomography (SD-OCT). Anterior segment SD-OCT scans from a subject were acquired circumferentially around the limbus. A Bayesian Ridge method was used to approximate the location of the SC on infrared confocal laser scanning ophthalmoscopic images with a cross multiplication tool developed to initiate SC/CC detection automated through a fuzzy hidden Markov Chain approach. Automatic segmentation of SC and initial CC’s was manually confirmed by two masked graders. Outflow pathways detected by the segmentation algorithm were reconstructed into a 3-D representation of AHO. Overall, only <1% of images (5114 total B-scans) were ungradable. Automatic segmentation algorithm performed well with SC detection 98.3% of the time and <0.1% false positive detection compared to expert grader consensus. CC was detected 84.2% of the time with 1.4% false positive detection. 3-D representation of AHO pathways demonstrated variably thicker and thinner SC with some clear CC roots. Circumferential (360 deg), automated, and validated AHO detection of angle structures in the living human eye with reconstruction was possible.
目的是利用一种自动检测算法为活体人眼中的圆周房水流出（AHO）创建一个三维（3-D）模型，该算法应用于频域光相干断层扫描（SD-OCT）来检测施莱姆管（SC）和一级收集管（CC）。从一个受试者的眼前节 SD-OCT 扫描沿角膜缘进行圆周扫描。采用贝叶斯岭回归方法，通过开发的十字乘法工具，在共焦激光扫描眼底图像上近似 SC 的位置，该工具可通过模糊隐马尔可夫链方法启动 SC/CC 自动检测。SC 和初始 CC 的自动分割由两名蒙面分级员手动确认。通过分割算法检测到的流出途径被重建为 AHO 的 3-D 表示。总体而言，只有<1%的图像（总 B 扫描 5114 个）无法分级。与专家分级员的共识相比，自动分割算法在 SC 检测方面表现出色，准确率为 98.3%，假阳性检测率<0.1%。CC 的检测准确率为 84.2%，假阳性检测率为 1.4%。AHO 途径的 3-D 表示显示了 SC 的厚度不同，有些 CC 根部清晰可见。使用重建的活体人眼角度结构的圆周（360 度）、自动和经过验证的 AHO 检测成为可能。
