应用笔记

AI + Macro Imaging

2021年3月

Clarify.ai is a new AI module that removes the blurred light contained in fluorescence images and generates high-contrast images. Clarify.ai, used with a stereomicroscope, can provide sharper fluorescence images than ever before; it enables not only macroscopic observation of model organisms, but also microscopic observation of fine structures.


Quantitative 3D Imaging of Living Organs-on-Chips with a High-Speed Point-Scanning Confocal System

2021年1月

Organs-on-chips more faithfully recapitulate the 3D architectural and functional complexity of native tissues compared to standard 2D tissue culture systems. Yet these advanced cell culture platforms present technical challenges for imaging-based applications. This Application Note demonstrates how the Nikon A1R HD25 confocal point-scanning system, CFI S Plan Fluor LWD 20XC objective and NIS-Elements software can enable rapid, deep, quantitative imaging of living cells in the Emulate Organ-Chip platform.


Highly accurate segmentation of cell areas based on DIC images using deep learning

2020年9月

Quantification of cell migration and cell confluency is important in biological and medical research on cellular functions. A scratch assay is used to quantitatively measure the speed of cells migrating to a cell-free area (gap) that is physically produced. This is a common technique for evaluating cell migration in cell development and differentiation, as well as in the invasion and metastasis of cancer cells. However, manually processing unstained sample images for quantification takes an immense amount of time. In addition, the Wound Healing function, a special application for scratch assays in the NIS-Elements imaging software, shows roughly correct results, but it has limited accuracy in terms of detailed detection.

In this Application Note, we introduce examples of quantification of a scratch assay using the NIS.ai module of NIS-Elements. These examples proved that NIS.ai can make more accurate inferences compared to the existing Wound Healing function based on a small number of training images.


尼康NIS-Elements Denoise.ai软件:利用深度学习对共聚焦数据进行去噪

2020年1月

噪声是共聚焦图像的基本组成部分,这是对连续采样的光子进行离散数字采样的结果。噪声对图像质量的贡献(信噪比)随着信号平方根函数的减小而增加。通过使用训练有素的神经网络,我们使用人工智能从共焦图像数据中去除了散粒噪声分量,从而提高了图像质量,并能够以更快的速度获取调光器样本。 NIS-Elements软件的Denoise.ai将该训练有素的网络部署到实时或采集后处理中。


硬件触发:最大化活细胞成像的速度和效率

2017年12月

现在,活细胞成像实验需要比以前更高的速度和更多的数据采集量。尼康仪器拥有强大的工具,可通过硬件之间的直接信号传导,在显微镜中实现成像设备的硬件触发。这样可以最大限度地减少延迟,使设备同步,并减少样品照射时间。本应用手册介绍了尼康 NIS-Elements 如何实现硬件触发的工作流程,并详细介绍了其相比常规时间序列采集方式的优势。