Xu Juan, Ishikawa Hiroshi, Wollstein Gadi, Bilonick Richard A, Sung Kyung R, Kagemann Larry, Townsend Kelly A, Schuman Joel S
UPMC Eye Center, Eye and Ear Institute, Ophthalmology and Visual Science Research Center, Department of Ophthalmology, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania 15213, USA.
Invest Ophthalmol Vis Sci. 2008 Jun;49(6):2512-7. doi: 10.1167/iovs.07-1229. Epub 2008 Mar 7.
To develop automated software for optic nerve head (ONH) quantitative assessment from stereoscopic disc photographs and to evaluate its performance in comparison with human expert assessment.
A fully automated system, including three-dimensional ONH modeling, disc margin detection, cup margin detection, and calculation of stereometric ONH parameters, was developed and tested. One eye each from 54 subjects (23 healthy, 17 suspected glaucoma, and 14 glaucoma) was enrolled. The majority opinion of three experts defined disc and cup margins on the disc photographs was used for comparison. Seven ONH parameters, disc area, rim area, rim volume, cup area, cup volume, cup-to-disc (C/D) area ratio, and vertical C/D ratio, were computed based on both machine- and expert-defined margins and compared between the methods.
All automated ONH measurements showed good correlation with the expert defined margins (Pearson r = 0.90, disc area; 0.56, rim area; 0.78, rim volume; 0.88, cup area; 0.93, cup volume; 0.69, C/D area ratio; and 0.67, vertical C/D ratio; all P <or= 0.0001). No statistically significant difference was found in the glaucoma-discriminating ability of all seven ONH parameters (P >or= 0.21). The mean or median of automatically defined disc and cup areas was significantly higher than the subjective assessment (disc area P = 0.0001, t-test; cup area P = 0.036, Wilcoxon signed ranks test), although they had high correlation coefficients. The software failed to detect the disc margin for all the disc photographs with peripapillary atrophy.
The automated ONH analysis method provides an objective and quantitative ONH evaluation using widely available stereo disc photographs.
开发用于从立体视盘照片进行视神经乳头(ONH)定量评估的自动化软件,并与人类专家评估相比较来评估其性能。
开发并测试了一个全自动系统,包括三维ONH建模、视盘边缘检测、杯盘边缘检测以及立体视盘参数计算。纳入了54名受试者各一只眼睛(23名健康者、17名疑似青光眼患者和14名青光眼患者)。以三位专家对视盘照片上视盘和杯盘边缘的多数意见作为比较标准。基于机器和专家定义的边缘计算了七个ONH参数,即视盘面积、盘沿面积、盘沿体积、杯盘面积、杯盘体积、杯盘面积比(C/D)和垂直C/D比,并对两种方法的结果进行比较。
所有自动化ONH测量结果与专家定义的边缘均显示出良好的相关性(Pearson相关系数r:视盘面积为0.90;盘沿面积为0.56;盘沿体积为0.78;杯盘面积为0.88;杯盘体积为0.93;C/D面积比为0.69;垂直C/D比为0.67;所有P≤0.0001)。七个ONH参数在青光眼鉴别能力方面均未发现统计学显著差异(P≥0.21)。自动定义的视盘和杯盘面积的均值或中位数显著高于主观评估(视盘面积P = 0.0001,t检验;杯盘面积P = 0.036,Wilcoxon符号秩检验),尽管它们具有较高的相关系数。该软件无法检测出所有存在视乳头周围萎缩的视盘照片的视盘边缘。
自动化ONH分析方法利用广泛可得的立体视盘照片提供了一种客观、定量的ONH评估。