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Nikon NIS-Elements AI Imaging Software

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Nikon NIS-Elements AI Imaging Software

Nikon NIS-Elements AI Imaging Software

Taking microscope imaging and analysis to the next level






Artificial Intelligence (AI) and deep earning methods are making seemingly impossible tasks now possible. Results only managed by challenging acquisition parameters or by painfully long or manual segmentation methods can now be automated thanks to AI.


The NIS-ai module expands the NIS-Elements platform by building in talior-made solutions for acquisition, visualisation and analysis. Read more on the Nikon website.


Conventional Thresholding                                 AI Segmentation

Intensity measurements were desired to be made along the nuclear envelope of cells. Conventional segmentation could not differentiate the cellular structures and misses several cells. AI-trained segmentation recognises and identifies the nulear envelope successfully.

By recognising 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.

Commonly, this can be used as a segmentation tool for label-free approaches, or imaging without harmful near-UV excitation. Once the neural network learns the pattern common to two channels, then in subsequent experiments the second channel is no longer needed to be acquired. Throughput of acquisition as well as specimen viability both increase as a result.

Some fluorescent samples express a very low signal and it is difficult to visualise or extract details for segmentation.

In addition, many of these samples are sensitive to light or photobleach very quickly and need to be imaged as fast as possible. can restore details by training the network 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.

All images contain shot noise, which is a Poisson-distributed noise related to discreetly sampling (acquiring images) of a continuous event. As signal levels decrease, the contribution of shot noise increases and noisy images result, following a square-root function. Such noise therefore is modeled in a neural network and doesn’t need to be further trained.

With new fluorescent techniques pushing intensities lower and acquisition speeds increasing, can recognise and remove the shot noise component of images, increasing clarity and allowing for shorter exposure times or more exposures of specimens while maintaining viability.


For further information please contact us or read more on the Nikon website.


NIS-Elements brochure                                                             Read more on Nikon's website