Kao Chiu-Yen, Richdale Kathryn, Sinnott Loraine T, Grillott Lauren E, Bailey Melissa D
Department of Mathematics and Mathematical Biosciences Institute, College of Mathematics and Physical Sciences, The Ohio State University, Columbus, Ohio 43210, USA.
Optom Vis Sci. 2011 Feb;88(2):275-89. doi: 10.1097/OPX.0b013e3182044b94.
To develop and evaluate a semiautomatic algorithm for segmentation and morphological assessment of the dimensions of the ciliary muscle in Visante Anterior Segment Optical Coherence Tomography images.
Geometric distortions in Visante images analyzed as binary files were assessed by imaging an optical flat and human donor tissue. The appropriate pixel/mm conversion factor to use for air (n = 1) was estimated by imaging calibration spheres. A semiautomatic algorithm was developed to extract the dimensions of the ciliary muscle from Visante images. Measurements were also made manually using Visante software calipers. Interclass correlation coefficients and Bland-Altman analyses were used to compare the methods. A multilevel model was fitted to estimate the variance of algorithm measurements that was due to differences within- and between-examiners in scleral spur selection vs. biological variability.
The optical flat and the human donor tissue were imaged and appeared without geometric distortions in binary file format. Bland-Altman analyses revealed that caliper measurements tended to underestimate ciliary muscle thickness at 3 mm posterior to the scleral spur in subjects with the thickest ciliary muscles (t = 3.6, p < 0.001). The percent variance due to within- or between-examiner differences in scleral spur selection was found to be small (6%) when compared with the variance because of biological difference across subjects (80%). Using the mean of measurements from three images, achieved an estimated interclass correlation coefficient of 0.85.
The semiautomatic algorithm successfully segmented the ciliary muscle for further measurement. Using the algorithm to follow the scleral curvature to locate more posterior measurements is critical to avoid underestimating thickness measurements. This semiautomatic algorithm will allow for repeatable, efficient, and masked ciliary muscle measurements in large datasets.
开发并评估一种半自动算法，用于在Visante眼前节光学相干断层扫描图像中对睫状肌的尺寸进行分割和形态学评估。
通过对光学平板和人类供体组织进行成像，评估以二进制文件形式分析的Visante图像中的几何失真。通过对校准球体进行成像，估计用于空气（n = 1）的合适像素/毫米转换因子。开发了一种半自动算法，用于从Visante图像中提取睫状肌的尺寸。还使用Visante软件卡尺进行手动测量。使用组内相关系数和Bland-Altman分析来比较这些方法。拟合了一个多级模型，以估计由于检查者在巩膜突选择方面的内部和之间差异与生物变异性导致的算法测量方差。
对光学平板和人类供体组织进行了成像，并且以二进制文件格式显示没有几何失真。Bland-Altman分析显示，在睫状肌最厚的受试者中，卡尺测量往往低估了巩膜突后方3毫米处的睫状肌厚度（t = 3.6，p < 0.001）。与受试者之间的生物差异导致的方差（80%）相比，由于检查者在巩膜突选择方面的内部或之间差异导致的方差百分比很小（6%）。使用来自三张图像的测量平均值，估计组内相关系数为0.85。
半自动算法成功地分割了睫状肌以进行进一步测量。使用该算法遵循巩膜曲率来定位更靠后的测量对于避免低估厚度测量至关重要。这种半自动算法将允许在大型数据集中进行可重复、高效且盲法的睫状肌测量。
