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利用混合机器学习光学相干断层扫描图像分割技术揭示食蟹猴种内黄斑异质性。

Uncovering of intraspecies macular heterogeneity in cynomolgus monkeys using hybrid machine learning optical coherence tomography image segmentation.

机构信息

Department of Ophthalmology, University of Basel, 4031, Basel, Switzerland.

Institute of Molecular and Clinical Ophthalmology Basel (IOB), 4031, Basel, Switzerland.

出版信息

Sci Rep. 2021 Oct 19;11(1):20647. doi: 10.1038/s41598-021-99704-z.

DOI:10.1038/s41598-021-99704-z
PMID:34667265
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8526684/
Abstract

The fovea is a depression in the center of the macula and is the site of the highest visual acuity. Optical coherence tomography (OCT) has contributed considerably in elucidating the pathologic changes in the fovea and is now being considered as an accompanying imaging method in drug development, such as antivascular endothelial growth factor and its safety profiling. Because animal numbers are limited in preclinical studies and automatized image evaluation tools have not yet been routinely employed, essential reference data describing the morphologic variations in macular thickness in laboratory cynomolgus monkeys are sparse to nonexistent. A hybrid machine learning algorithm was applied for automated OCT image processing and measurements of central retina thickness and surface area values. Morphological variations and the effects of sex and geographical origin were determined. Based on our findings, the fovea parameters are specific to the geographic origin. Despite morphological similarities among cynomolgus monkeys, considerable variations in the foveolar contour, even within the same species but from different geographic origins, were found. The results of the reference database show that not only the entire retinal thickness, but also the macular subfields, should be considered when designing preclinical studies and in the interpretation of foveal data.

摘要

中央凹是黄斑中心的凹陷处,是视力最敏锐的部位。光学相干断层扫描(OCT)在阐明黄斑病变方面做出了重要贡献,目前被认为是药物开发的一种辅助成像方法,如抗血管内皮生长因子及其安全性分析。由于临床前研究中的动物数量有限,并且尚未常规使用自动化图像评估工具,因此描述实验性食蟹猴黄斑厚度形态变化的基本参考数据很少甚至不存在。本研究应用混合机器学习算法进行自动 OCT 图像处理和中央视网膜厚度及表面积值的测量,以确定形态变化以及性别的影响和地理起源的影响。基于我们的发现,黄斑参数与地理起源有关。尽管食蟹猴之间存在形态相似性,但即使在同一物种内,也会发现来自不同地理起源的黄斑窝轮廓存在相当大的差异。参考数据库的结果表明,在设计临床前研究和解释黄斑数据时,不仅应考虑整个视网膜厚度,还应考虑黄斑的各个亚区。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5fe9/8526684/31e02a5352b0/41598_2021_99704_Fig7_HTML.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5fe9/8526684/31e02a5352b0/41598_2021_99704_Fig7_HTML.jpg
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