Selig Bettina, Vermeer Koenraad A, Rieger Bernd, Hillenaar Toine, Luengo Hendriks Cris L
Centre for Image Analysis, Uppsala University, Box 337, Uppsala, 75105, Sweden.
Rotterdam Ophthalmic Institute, Rotterdam, The Netherlands.
BMC Med Imaging. 2015 Apr 26;15:13. doi: 10.1186/s12880-015-0054-3.
Manual and semi-automatic analyses of images, acquired in vivo by confocal microscopy, are often used to determine the quality of corneal endothelium in the human eye. These procedures are highly time consuming. Here, we present two fully automatic methods to analyze and quantify corneal endothelium imaged by in vivo white light slit-scanning confocal microscopy.
In the first approach, endothelial cell density is estimated with the help of spatial frequency analysis. We evaluate published methods, and propose a new, parameter-free method. In the second approach, based on the stochastic watershed, cells are automatically segmented and the result is used to estimate cell density, polymegathism (cell size variability) and pleomorphism (cell shape variation). We show how to determine optimal values for the three parameters of this algorithm, and compare its results to a semi-automatic delineation by a trained observer.
The frequency analysis method proposed here is more precise than any published method. The segmentation method outperforms the fully automatic method in the NAVIS software (Nidek Technologies Srl, Padova, Italy), which significantly overestimates the number of cells for cell densities below approximately 1200 mm(-2), as well as previously published methods.
The methods presented here provide a significant improvement over the state of the art, and make in vivo, automated assessment of corneal endothelium more accessible. The segmentation method proposed paves the way to many possible new morphometric parameters, which can quickly and precisely be determined from the segmented image.
通过共聚焦显微镜在体内获取的图像的手动和半自动分析，常用于确定人眼角膜内皮的质量。这些程序非常耗时。在此，我们提出两种全自动方法来分析和量化通过体内白光裂隙扫描共聚焦显微镜成像的角膜内皮。
在第一种方法中，借助空间频率分析估计内皮细胞密度。我们评估已发表的方法，并提出一种新的、无参数的方法。在第二种方法中，基于随机分水岭算法，自动分割细胞，并将结果用于估计细胞密度、多形性（细胞大小变异性）和异形性（细胞形状变化）。我们展示了如何确定该算法三个参数的最佳值，并将其结果与训练有素的观察者的半自动描绘结果进行比较。
这里提出的频率分析方法比任何已发表的方法都更精确。分割方法优于NAVIS软件（意大利帕多瓦的Nidek Technologies Srl公司）中的全自动方法，该软件对于细胞密度低于约1200 mm⁻² 的情况会显著高估细胞数量，也优于先前发表的方法。
这里提出的方法相较于现有技术有显著改进，使体内角膜内皮的自动评估更容易实现。所提出的分割方法为许多可能的新形态测量参数铺平了道路，这些参数可以从分割图像中快速准确地确定。
