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Confocal Fluorescence Microscopy

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Confocal microscopy is an advanced microscopic technique. The prime advantage of the confocal microscope over other microscopy techniques is its ability to block the out-of-focus light from the illuminated samples using pinholes. It is widely used with fluorescence optics to obtain high-resolution, sharp contrast images. Unlike optical microscopes, confocal microscopes use a focused beam of light laser to scan the entire sample surface at different z-planes. These microscopes are, therefore,...

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Updated: Jun 17, 2025

Longitudinal Morphological and Physiological Monitoring of Three-dimensional Tumor Spheroids Using Optical Coherence Tomography

Longitudinal Morphological and Physiological Monitoring of Three-dimensional Tumor Spheroids Using Optical Coherence Tomography

Published on: February 9, 2019

探索公开可访问的光学连贯断层扫描数据集:一个全面的概述

Anastasiia Rozhyna^1,2, Gábor Márk Somfai^3,4, Manfredo Atzori^1,5

¹Informatics Institute, University of Applied Sciences Western Switzerland (HES-SO), 3960 Sierre, Switzerland.

Diagnostics (Basel, Switzerland)

|August 10, 2024

概括

研究人员审查了公开可用的光学一致性断层扫描 (OCT) 数据集,以帮助眼科人工智能 (AI) 的发展. 该研究确定了23个数据集,强调需要更好的数据共享和标准化,以推进人工智能驱动的医学诊断.

关键词:

其他国家和地区.数据数据的数据数据的数据.数据分析数据分析数据分析分享数据的数据共享.数据集数据集数据集.开放数据是开放的数据.光学连贯性断层扫描 (optical coherence tomography) 是一种光学连贯性断层扫描技术.

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Multimodal Volumetric Retinal Imaging by Oblique Scanning Laser Ophthalmoscopy oSLO and Optical Coherence Tomography OCT

Multimodal Volumetric Retinal Imaging by Oblique Scanning Laser Ophthalmoscopy oSLO and Optical Coherence Tomography OCT

Published on: August 4, 2018

科学领域:

眼科医生眼科眼科
医疗成像医学成像
人工智能的人工智能

背景情况:

人工智能 (AI) 显著提高了医疗诊断,特别是分析医疗图像,如光学连贯断层扫描 (OCT).
人工智能算法需要广泛,高质量和注释的患者数据,以便在计算机辅助诊断中进行有效的培训和准确的预测.
由于隐私问题,有限的可用性和缺乏注释,研究人员经常在访问足够的医疗数据方面遇到挑战,这阻碍了眼科人工智能的发展.

研究的目的:

对公开可访问的视网膜OCT数据集进行全面审查.
编制可用的OCT数据集及其研究人员的特征的详细列表.
为了促进数据策划和改进AI应用在基于OCT的眼科诊断中.

主要方法:

在多个存储库中进行了系统搜索,包括Zenodo,Mendeley Data,MEDLINE和谷歌数据集搜索.
包含视网膜OCT图像的已识别的数据集被评估其属性,大小,范围和基本真相标签.
审查的重点是开放式访问数据集,以确保研究界的可访问性.

主要成果:

总共有23个开放访问的视网膜OCT数据集被确定.
这些数据集在大小,内容范围和基本真相标签的质量方面表现出相当大的差异.
这些发现强调了加强数据共享实践和在现场进行标准化文档的必要性.

结论:

提高视网膜OCT数据集的可用性和标准化对于推动眼科人工智能算法开发至关重要.
更好的数据资源将支持创建更强大的AI工具,以提高诊断能力.
本综述是指导研究人员利用和开发人工智能用于眼科医学图像分析的宝贵参考资料.