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角膜共焦活体显微镜图像的数字图像处理技术综述。

A Review On digital image processing techniques for in-Vivo confocal images of the cornea.

机构信息

Departamento de Bioinformática, Facultad de Ciencias y Tecnologías Computacionales, Universidad de las Ciencias Informáticas (UCI), Carretera a San Antonio de los Baños Km 2 1/2, Torrens, Boyeros, La Habana, Cuba; TELIN-IPI, Ghent University - imec, Belgium.

Centro de Investigaciones de la Informática, Universidad Central "Marta Abreu" de Las Villas (UCLV), Carretera a Camajuaní, km 5 1/2, Santa Clara, VC, CP 54830, Cuba.

出版信息

Med Image Anal. 2021 Oct;73:102188. doi: 10.1016/j.media.2021.102188. Epub 2021 Jul 23.

DOI:10.1016/j.media.2021.102188
PMID:34340102
Abstract

This work reviews the scientific literature regarding digital image processing for in vivo confocal microscopy images of the cornea. We present and discuss a selection of prominent techniques designed for semi- and automatic analysis of four areas of the cornea (epithelium, sub-basal nerve plexus, stroma and endothelium). The main context is image enhancement, detection of structures of interest, and quantification of clinical information. We have found that the preprocessing stage lacks of quantitative studies regarding the quality of the enhanced image, or its effects in subsequent steps of the image processing. Threshold values are widely used in the reviewed methods, although generally, they are selected empirically and manually. The image processing results are evaluated in many cases through comparison with gold standards not widely accepted. It is necessary to standardize values to be quantified in terms of sensitivity and specificity of methods. Most of the reviewed studies do not show an estimation of the computational cost of the image processing. We conclude that reliable, automatic, computer-assisted image analysis of the cornea is still an open issue, constituting an interesting and worthwhile area of research.

摘要

本文回顾了用于角膜共聚焦显微镜活体图像的数字图像处理的科学文献。我们展示和讨论了一些针对角膜四个区域(上皮、基底下神经丛、基质和内皮)的半自动和自动分析设计的突出技术。主要内容是图像增强、感兴趣结构的检测和临床信息的量化。我们发现预处理阶段缺乏关于增强图像质量或其对图像处理后续步骤影响的定量研究。阈值在综述方法中被广泛使用,尽管通常它们是经验性和手动选择的。在许多情况下,通过与未广泛接受的金标准进行比较来评估图像处理结果。有必要根据方法的灵敏度和特异性来标准化要量化的值。大多数综述研究都没有显示图像处理计算成本的估计。我们得出结论,可靠的、自动的、计算机辅助的角膜图像分析仍然是一个悬而未决的问题,构成了一个有趣和有价值的研究领域。

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Methods for evaluation of corneal nerve fibres in diabetes mellitus by in vivo confocal microscopy: a scoping review protocol.应用活体共聚焦显微镜评估糖尿病患者角膜神经纤维的方法:系统评价方案。
BMJ Open. 2023 Apr 12;13(4):e070017. doi: 10.1136/bmjopen-2022-070017.
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Segmentation and Classification Approaches of Clinically Relevant Curvilinear Structures: A Review.临床相关曲线结构的分割与分类方法综述。
J Med Syst. 2023 Mar 27;47(1):40. doi: 10.1007/s10916-023-01927-2.
3
DenseUNets with feedback non-local attention for the segmentation of specular microscopy images of the corneal endothelium with guttae.
带有反馈非局部注意力的密集型 UNets 用于分割带胶滴的角膜内皮反射显微镜图像
Sci Rep. 2022 Aug 18;12(1):14035. doi: 10.1038/s41598-022-18180-1.