Zhivov A, Winter K, Peschel S, Guthoff R F, Stachs O, Harder V, Schober H-C, Koehler B
Augenklinik, Universität Rostock.
Klin Monbl Augenheilkd. 2011 Dec;228(12):1067-72. doi: 10.1055/s-0031-1281663. Epub 2011 Sep 7.
An analysis of the corneal subbasal nerve plexus (SNP) allows an evaluation of the peripheral neuropathy in cases of degenerative diseases. In order to study the SNP structures quantitatively the automatically calculated morphological and topological parameters are required.
In vivo confocal laser scanning microscopy (Heidelberg Retina Tomograph II/Rostock Cornea Module) was performed in healthy volunteers as well as patients with severe diabetic neuropathy. An adapted image processing algorithm was used to preprocess, segment and evaluate quantitatively the nerve fibers of the SNP. Data sets were analysed statistically.
The developed algorithm allows an automated detection of SNP structures. Furthermore, it allows the collection of data based on morphological and topological parameters. The main parameters that show significant differences between healthy cornea and cases of diabetic neuropathy are nerve fibre density and length, number of branching, tortuosity and number of terminal and crossing points. All parameters of the measurements can be used isolated, combined or weighted for quantification of the SNP networks.
The presented fully automated preprocessing eliminates a large number of motion-induced artefacts. The quality of the resulting pictures allows an automated quantification using characteristic measurements. This represents an in vivo, non-invasive technology analysing degenerative changes of SNP especially in the course of diabetes mellitus.
对角膜基底神经丛(SNP)进行分析有助于评估退行性疾病病例中的周围神经病变。为了定量研究SNP结构,需要自动计算形态学和拓扑学参数。
对健康志愿者以及重度糖尿病神经病变患者进行了体内共焦激光扫描显微镜检查(海德堡视网膜断层扫描仪II/罗斯托克角膜模块)。使用一种经过改进的图像处理算法对SNP的神经纤维进行预处理、分割和定量评估。对数据集进行了统计分析。
所开发的算法能够自动检测SNP结构。此外,它还能基于形态学和拓扑学参数收集数据。在健康角膜与糖尿病神经病变病例之间显示出显著差异的主要参数有神经纤维密度和长度、分支数量、弯曲度以及终末点和交叉点数量。测量的所有参数都可单独、组合或加权使用,以对SNP网络进行量化。
所呈现的全自动化预处理消除了大量运动引起的伪影。所得图像的质量使得能够使用特征测量进行自动量化。这代表了一种用于分析SNP退行性变化的体内非侵入性技术,尤其是在糖尿病病程中。