On quality assessment of corneal endothelium and its possibility to be used for surgical corneal transplantation

Francesc Tinena, P. Sobrevilla, E. Montseny
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引用次数: 5

Abstract

Transplantation of corneal tissue is a usual practice in hospitals. The analysis of microscopy images of donor corneal endothelium is routinely carried out at eye banks for the clinical assessment of cornea quality and suitability for transplantation. One of the main clinical parameters expressing the health of a cornea, and assessing their suitability as a human graft, is the cell density of its endothelium. Endothelium cell density is conventionally estimated by a long, tedious and error-prone manual counting procedure, carried out by experts who, according to a protocol, observe specimen images through an optical microscope. Besides a great subjectivity, this manual process causes a great disparity in the results derived from the protocol considered. Another additional and very important drawback is that images are often blurred and noisy, what makes very difficult the correct recognition of the cells. Taking into account aforementioned problems, this paper introduces a computer intelligence-based system for automatic segmentation of corneal endothelium images that in addition to facilitating technicians work of will reduce the disparity of results
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角膜内皮质量评价及其在角膜移植手术中的应用前景
角膜组织移植在医院是一种常见的做法。眼库常规对供体角膜内皮的显微图像进行分析,以评估角膜质量和移植的适宜性。表达角膜健康状况和评估其作为人类移植物的适用性的主要临床参数之一是其内皮细胞密度。传统上,内皮细胞密度的估计是通过一个漫长、繁琐且容易出错的人工计数程序进行的,由专家根据一个协议,通过光学显微镜观察标本图像。除了很大的主观性外,这种手工过程还会导致从所考虑的协议中得出的结果存在很大差异。另外一个非常重要的缺点是图像通常是模糊和嘈杂的,这使得正确识别细胞变得非常困难。针对上述问题,本文介绍了一种基于计算机智能的角膜内皮图像自动分割系统,该系统在方便技术人员工作的同时,减少了分割结果的差异
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