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Artificial Intelligence (AI) and deep learning methods are making seemingly impossible tasks now possible. From recovering contrast to improving signal-to-noise ratio, or for new approaches to managing challenging acquisition parameters or segmentation previously difficult or nearly impossible, these approaches can now be automated thanks to AI.

The NIS-Elements suite consists of various modules and functions which expand the NIS-Elements platform by building in tailor-made solutions for acquisition, visualization and analysis.


Uses artificial intelligence to automatically remove blur from fluorescence microscope images, leaving behind the in-focus structures, and can be used on any widefield 2D or 3D data set, detector, or magnification, without the need for AI training or introduction of bias from complicated user settings.


As signal levels decrease, the contribution of shot noise increases, and noisy images result. Pre-trained can be applied to confocal images to remove shot noise, increase clarity and allow for shorter exposure times or more exposures of specimens while maintaining viability.

By recognizing patterns present in two different imaging channels, can be trained to predict what the second channel would look like when only the first channel is acquired. can be used as a segmentation tool for label-free approaches, or imaging without harmful near-UV excitation.

Workflow for

Using for tracking

Some fluorescent samples express a very low signal and it is difficult to visualize or extract details for segmentation, as well as sensitive to light or photobleach very quickly and need to be imaged as fast as possible. can restore details by training the network on what properly-exposed images look like. Then this recipe can be applied to underexposed images to restore detail that can be used for further analysis.


Some images are nearly impossible to segment by traditional intensity thresholding methods. A neural network can be trained by human classification of structures of interest that cannot easily be defined by classic thresholding and image processing by using

By tracing features of interest and training these compared to the underlying image, the neural network can learn and apply segmentation to similar images, recognizing features previously only identifiable by painstaking manual tracing approaches.


*Photos and videos courtesy of Nikon official website

Related Information

Intelligent Acquisition System

With Nikon microscope and software NIS-Elements, our high content imaging streamlines high-speed imaging and simple operations.


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