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开发无伪影成像系统以准确评估睑板腺反射率。

Development of Artefact-Free Imaging System for Accurate Meibomian Gland Reflectivity Assessment.

机构信息

Eurolens Research, Division of Pharmacy and Optometry, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, UK.

Johnson & Johnson Vision, Inc., Jacksonville, FL, USA.

出版信息

Transl Vis Sci Technol. 2023 Feb 1;12(2):9. doi: 10.1167/tvst.12.2.9.

DOI:10.1167/tvst.12.2.9
PMID:36749580
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9919613/
Abstract

PURPOSE

To develop and evaluate a custom imaging system to provide high-resolution, wide depth-of-field, reflection-free, multispectral infrared (IR) imaging of the Meibomian glands.

METHODS

Lower eyelids of 15 volunteers were everted to obtain multispectral images of the Meibomian glands with custom imaging setup. Photographs were captured at 10 different ISO settings (from underexposure to overexposure) and using nine IR imaging filters (ranging from 600 nm to 1000 nm in 50-nm steps). Meibomian gland contrast (simple and Michelson) was calculated for the images to choose an optimal wavelength for Meibomian gland imaging and to determine differences in contrast across individuals.

RESULTS

The overall linear regression model showed a significant effect of wavelength on Meibomian gland contrast (Simple contrast: F = 7.24, P < 0.0001; Michelson contrast: F = 7.19, P < 0.0001). There was a significant negative correlation between Meibomian gland contrast and Meibomian gland depth for 750-nm IR filter (ρs= -0.579; P = 0.026).

CONCLUSIONS

Meibomian gland contrast varies across individuals and depends on Meibomian gland depth. IR filter of 750 nm is the optimal choice for Meibomian gland imaging because it provides images of greatest contrast.

TRANSLATIONAL RELEVANCE

This study adds to our understanding of Meibomian gland imaging. It has successfully demonstrated that Meibomian glands that are deeper in the tarsal plate require longer wavelengths for imaging.

摘要

目的

开发和评估一种定制的成像系统,以提供高分辨率、大景深、无反射、多光谱近红外(IR)成像的睑板腺。

方法

将 15 名志愿者的下眼睑外翻,使用定制成像设备获取睑板腺的多光谱图像。在 10 个不同的 ISO 设置(从欠曝光到过曝光)和 9 个 IR 成像滤光片(以 50nm 的步长从 600nm 到 1000nm)下拍摄照片。计算了图像的睑板腺对比度(简单和Michelson),以选择用于睑板腺成像的最佳波长,并确定个体之间对比度的差异。

结果

总体线性回归模型显示,波长对睑板腺对比度有显著影响(简单对比度:F=7.24,P<0.0001;Michelson 对比度:F=7.19,P<0.0001)。对于 750nm IR 滤光片,睑板腺对比度与睑板腺深度之间存在显著负相关(ρs=-0.579;P=0.026)。

结论

睑板腺对比度在个体之间有所不同,取决于睑板腺的深度。750nm 的 IR 滤光片是睑板腺成像的最佳选择,因为它提供了对比度最大的图像。

翻译

张驰

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a3f5/9919613/96d8b79d5995/tvst-12-2-9-f008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a3f5/9919613/1b1e7a2b90b2/tvst-12-2-9-f001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a3f5/9919613/c6846ca1cd1e/tvst-12-2-9-f002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a3f5/9919613/e7ceacaf9249/tvst-12-2-9-f003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a3f5/9919613/0a904eb44b14/tvst-12-2-9-f004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a3f5/9919613/47d1f3aa81f3/tvst-12-2-9-f005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a3f5/9919613/a8059a959068/tvst-12-2-9-f006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a3f5/9919613/034bf371d23d/tvst-12-2-9-f007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a3f5/9919613/96d8b79d5995/tvst-12-2-9-f008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a3f5/9919613/1b1e7a2b90b2/tvst-12-2-9-f001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a3f5/9919613/c6846ca1cd1e/tvst-12-2-9-f002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a3f5/9919613/e7ceacaf9249/tvst-12-2-9-f003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a3f5/9919613/0a904eb44b14/tvst-12-2-9-f004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a3f5/9919613/47d1f3aa81f3/tvst-12-2-9-f005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a3f5/9919613/a8059a959068/tvst-12-2-9-f006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a3f5/9919613/034bf371d23d/tvst-12-2-9-f007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a3f5/9919613/96d8b79d5995/tvst-12-2-9-f008.jpg

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本文引用的文献

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Quantitative analysis of morphological and functional features in Meibography for Meibomian Gland Dysfunction: Diagnosis and Grading.睑板腺功能障碍的睑板造影术形态学和功能特征的定量分析:诊断与分级
EClinicalMedicine. 2021 Sep 11;40:101132. doi: 10.1016/j.eclinm.2021.101132. eCollection 2021 Oct.
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Quantifying Meibomian Gland Morphology Using Artificial Intelligence.使用人工智能定量测量睑板腺形态。
Optom Vis Sci. 2021 Sep 1;98(9):1094-1103. doi: 10.1097/OPX.0000000000001767.
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Diagnostic Capability of a New Objective Method to Assess Meibomian Gland Visibility.
一种评估睑板腺可视性的新客观方法的诊断能力。
Optom Vis Sci. 2021 Sep 1;98(9):1045-1055. doi: 10.1097/OPX.0000000000001764.
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Morphological variants of meibomian glands: correlation of meibography features with histopathology findings.睑板腺的形态学变异:睑板腺造影特征与组织病理学结果的相关性
Br J Ophthalmol. 2023 Feb;107(2):195-200. doi: 10.1136/bjophthalmol-2021-318876. Epub 2021 Aug 20.
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Longitudinal assessment of meibomian glands and tear film layer in systemic isotretinoin treatment.全身性异维A酸治疗中睑板腺和泪膜层的纵向评估
Eur J Ophthalmol. 2021 May 20:11206721211018361. doi: 10.1177/11206721211018361.
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Meibography Phenotyping and Classification From Unsupervised Discriminative Feature Learning.基于无监督判别特征学习的睑板腺形态学表型分析和分类。
Transl Vis Sci Technol. 2021 Feb 5;10(2):4. doi: 10.1167/tvst.10.2.4.
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Deep learning-based automatic meibomian gland segmentation and morphology assessment in infrared meibography.基于深度学习的红外睑板腺图像中睑板腺自动分割与形态评估。
Sci Rep. 2021 Apr 7;11(1):7649. doi: 10.1038/s41598-021-87314-8.
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An automated and multiparametric algorithm for objective analysis of meibography images.一种用于睑板腺图像客观分析的自动化多参数算法。
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BMJ Open Ophthalmol. 2021 Feb 12;6(1):e000436. doi: 10.1136/bmjophth-2020-000436. eCollection 2021.
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