Sadda Srinivas R, Joeres Sandra, Wu Ziqiang, Updike Paul, Romano Peggy, Collins Allyson T, Walsh Alexander C
Doheny Image Reading Center, Doheny Eye Institute, Keck School of Medicine of the University of Southern California, Los Angeles, California 90033, USA.
Invest Ophthalmol Vis Sci. 2007 Feb;48(2):839-48. doi: 10.1167/iovs.06-0554.
To demonstrate feature subanalysis and error correction of optical coherence tomography (OCT) data by using computer-assisted grading.
The raw exported StratusOCT (Carl Zeiss Meditec, Inc., Dublin, CA) scan data from 20 eyes of 20 patients were analyzed using custom software (termed OCTOR) designed to allow the user to define manually the retinal borders on each radial line scan. Measurements calculated by the software, including thickness of the nine standard macular subfields, foveal center point (FCP), and macular volume, were compared between two graders and with the automated Stratus analysis. Mean and range of differences for each parameter were calculated and assessed by Bland-Altman plots and Pearson correlation coefficients. Additional cases with clinically relevant subretinal findings were selected to demonstrate the capabilities of this system for quantitative feature subanalysis.
Retinal thickness measurements for the various subfields and the FCP showed a mean difference of 1.7 mum (maximum, 7 microm) between OCTOR graders and a mean difference of 2.3 microm (maximum of 8 microm) between the OCTOR and Stratus analysis methods. Volume measurements between Stratus and OCTOR methods differed by a mean of 0.06 mm(3) (in reference to a mean macular volume of 6.81 mm(3)). The differences were not statistically significant, and the thicknesses correlated highly (R(2) > or = 0.98 for all parameters).
Manual identification of the inner and outer retinal boundaries on OCT scans can produce retinal thickness measurements consistent with those derived from the automated StratusOCT analysis. Computer-assisted OCT grading may be useful for correcting thickness measurements in cases with errors of automated retinal boundary detection and may be useful for quantitative subanalysis of clinically relevant features, such as subretinal fluid volume or pigment epithelial detachment volume.
通过计算机辅助分级来演示光学相干断层扫描(OCT)数据的特征子分析和误差校正。
使用定制软件(称为OCTOR)对20例患者的20只眼睛的原始导出StratusOCT(卡尔蔡司医疗技术公司,加利福尼亚州都柏林)扫描数据进行分析,该软件旨在允许用户在每条径向线扫描上手动定义视网膜边界。比较了两名分级人员之间以及与Stratus自动分析之间由该软件计算出的测量值,包括九个标准黄斑子区域的厚度、黄斑中心点(FCP)和黄斑体积。计算每个参数的平均差异和差异范围,并通过Bland-Altman图和Pearson相关系数进行评估。选择具有临床相关视网膜下发现的其他病例来演示该系统进行定量特征子分析的能力。
不同子区域和FCP的视网膜厚度测量值在OCTOR分级人员之间的平均差异为1.7μm(最大7μm),在OCTOR和Stratus分析方法之间的平均差异为2.3μm(最大8μm)。Stratus和OCTOR方法之间的体积测量值平均相差0.06mm³(相对于平均黄斑体积6.81mm³)。差异无统计学意义,且厚度高度相关(所有参数的R²≥0.98)。
在OCT扫描上手动识别视网膜内、外边界可产生与自动StratusOCT分析得出的测量值一致的视网膜厚度测量值。计算机辅助OCT分级对于校正自动视网膜边界检测存在误差的病例中的厚度测量可能有用,并且对于临床相关特征(如视网膜下液体积或色素上皮脱离体积)的定量子分析可能有用。