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
Transforming Multiplexed 2D Data into Spatial Insights Guided by AI
Aivia 13 handles large 2D images and enables researchers to obtain deep insights into microenvironment surrounding their phenotypes with millions of detected objects and automatic clustering up to 30…
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自动化加速神经元图像分析
复杂神经投射的检测能力主要取决于大规模神经元网络的精确重建。神经科学研究中的大多数数据析取方法都非常耗时和易错,进而导致进度延误和错误。在本次研讨会中,Aivia将演示如何利用自动化技术提升图像分析工作流的效率
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![[Translate to chinese:] Dynamic Signal Enhancement powered by Aivia: Truly simultaneous multicolor imaging of live cells (U2OS) in 3D [Translate to chinese:] Dynamic Signal Enhancement powered by Aivia: Truly simultaneous multicolor imaging of live cells (U2OS) in 3D](/fileadmin/_processed_/b/1/csm_How_Artificial_Intelligence_Enhances_Confocal_Imaging_teaser_bba50f917e.jpg)
人工智能如何增强共聚焦成像
在本文中,我们将展示人工智能(AI)如何增强您的成像实验。即,由 Aivia 提供支持的动态信号增强如何在捕捉活细胞样本的时间动态的同时提高图像质量。
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
An Introduction to Computational Clearing
Many software packages include background subtraction algorithms to enhance the contrast of features in the image by reducing background noise. The most common methods used to remove background noise…
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清晰对比、无雾的 3D 样本实时图像
历史上,宽场显微镜并不适合对大样本/标本体积进行成像。图像背景(BG)主要来源于观察样本的失焦区域,显著降低了成像系统的对比度、有效动态范围和最大可能的信噪比(SNR)。记录的图像显示出典型的雾霭,并且在许多情况下,无法提供进一步分析所需的细节水平。处理厚三维样本的研究人员要么使用替代显微镜方法,要么尝试通过后处理一系列图像来减少雾霭。