Nikon NIS-Elements AI
Artificial intelligence (AI) makes seemingly impossible tasks possible. Image analysis that was previously difficult, or impossible, can now be automated with NIS-Elements ai.
Nikon NIS-Elements AI
Artificial intelligence (AI) makes seemingly impossible tasks possible. Image analysis that was previously difficult, or impossible, can now be automated with NIS-Elements ai.
Artificial Intelligence (AI) and Deep Learning make seemingly impossible tasks easy to accomplish. From increasing the contrast and clarity of your image, to improving the signal-to-noise ratio, or to segmenting low-contrast objects that were previously almost impossible to segment, the NIS ai module makes this easy without user bias.
Tailor-made solutions for visualization and analysis
NIS-Elements NIS.ai consists of several functions that make it possible to implement visualization and analysis solutions tailored to your application. NIS ai is trained on your own samples, under your specific conditions and this only needs to be done once. A trained ai can be reused again and again, during the experiment or when the imaging is finished.
Clarify.ai removes blur in fluorescent images
The module is already trained to recognize fluorescence signals from out-of-focus planes. It removes these components from the image automatically, leaving the focused structures. This increases the clarity and sharpness of the image. Clarify ai can be used on any widefield 2D or 3D image, camera, detector or magnifier, without training. Clarify ai is part of the 2D Deconvolution module.
Denoise ai. removes noise in the image
This ai is included in NIS-Elements AR and is already trained. All cameras and detectors generate noise, so called shot noise. If you have a weak fluorescent signal and have to increase the exposure time, you also increase the shot noise, many times so much that the weak signal is lost in the noise. Denoise ai is trained to recognize and remove shot noise, while retaining the signal. This results in an image with a clear and bright fluorescence signal with a smooth background. Denoise ai is already trained and can be applied to all types of fluorescent images, on the live image or on already taken images.
Enhance ai amplifies fluorescent signals
Some fluorescent samples express a very low signal that can be difficult to visualize or segment. Increasing the laser intensity or exposure time is not always an option as many samples are sensitive to being exposed to bright light. For living cells, exposure to light must also be kept as short as possible.
Enhance.ai is trained on your samples as they should look when correctly exposed, that is, with a strong signal. When imaging with a short exposure time, the signal strength is restored to what it would have looked like at the correct exposure. This means that you can still get a strong enough signal to proceed with segmentation and analysis, even though the laser intensity and exposure time are kept short. The trained Enhance ai can be reused during experiments or on already acquired image sequences. Enhance ai is part of the NIS ai module.
Convert.ai for segmentation and analysis of unstained cells
By comparing patterns found in two different image channels, for example an image series with a fluorescent marker, and the same image series without a fluorescent marker, Convert.ai can be trained to label the fluorescent channel even if the marker is missing in your preparation. This can be used, for example, to count cells without nuclear staining; Convert ai is trained with a series of images where one channel is DAPI staining for nuclei and the other channel is DIC. Convert ai learns the pattern that all cells should also have a nuclear stain, and that channel is added to your experiment. Since the nucleus is now visible, the fluorescent marker is no longer needed and these cells can be segmented on the DIC channel only. This means that completely unstained cells can be segmented and counted without being exposed to harmful UV illumination. Convert ai can be trained on all types of fluorescent patterns.Convert ai is part of the NIS-Elements ai module.
Segment ai for automatic segmentation of difficult objects
Some images are almost impossible to segment based on intensity values. These may be objects without contrast, objects that exhibit many different intensity levels, or objects with large morphological variation. Segment ai can be trained by manually thresholding and classifying the structures of interest. By manually selecting objects of interest and training Segment ai on them, it can learn the structures, find them on similar images, and apply segmentation to the objects. Segment ai requires a manual segmentation/selection the first time but can then be used again and again on similar specimens. Segment ai is part of the NIS-Elements ai module.
Contact the product specialist:
Catherine Kitts |
Marie Andersson |
Artificial Intelligence (AI) and Deep Learning make seemingly impossible tasks easy to accomplish. From increasing the contrast and clarity of your image, to improving the signal-to-noise ratio, or to segmenting low-contrast objects that were previously almost impossible to segment, the NIS ai module makes this easy without user bias.
Tailor-made solutions for visualization and analysis
NIS-Elements NIS.ai consists of several functions that make it possible to implement visualization and analysis solutions tailored to your application. NIS ai is trained on your own samples, under your specific conditions and this only needs to be done once. A trained ai can be reused again and again, during the experiment or when the imaging is finished.
Clarify.ai removes blur in fluorescent images
The module is already trained to recognize fluorescence signals from out-of-focus planes. It removes these components from the image automatically, leaving the focused structures. This increases the clarity and sharpness of the image. Clarify ai can be used on any widefield 2D or 3D image, camera, detector or magnifier, without training. Clarify ai is part of the 2D Deconvolution module.
Denoise ai. removes noise in the image
This ai is included in NIS-Elements AR and is already trained. All cameras and detectors generate noise, so called shot noise. If you have a weak fluorescent signal and have to increase the exposure time, you also increase the shot noise, many times so much that the weak signal is lost in the noise. Denoise ai is trained to recognize and remove shot noise, while retaining the signal. This results in an image with a clear and bright fluorescence signal with a smooth background. Denoise ai is already trained and can be applied to all types of fluorescent images, on the live image or on already taken images.
Enhance ai amplifies fluorescent signals
Some fluorescent samples express a very low signal that can be difficult to visualize or segment. Increasing the laser intensity or exposure time is not always an option as many samples are sensitive to being exposed to bright light. For living cells, exposure to light must also be kept as short as possible.
Enhance.ai is trained on your samples as they should look when correctly exposed, that is, with a strong signal. When imaging with a short exposure time, the signal strength is restored to what it would have looked like at the correct exposure. This means that you can still get a strong enough signal to proceed with segmentation and analysis, even though the laser intensity and exposure time are kept short. The trained Enhance ai can be reused during experiments or on already acquired image sequences. Enhance ai is part of the NIS ai module.
Convert.ai for segmentation and analysis of unstained cells
By comparing patterns found in two different image channels, for example an image series with a fluorescent marker, and the same image series without a fluorescent marker, Convert.ai can be trained to label the fluorescent channel even if the marker is missing in your preparation. This can be used, for example, to count cells without nuclear staining; Convert ai is trained with a series of images where one channel is DAPI staining for nuclei and the other channel is DIC. Convert ai learns the pattern that all cells should also have a nuclear stain, and that channel is added to your experiment. Since the nucleus is now visible, the fluorescent marker is no longer needed and these cells can be segmented on the DIC channel only. This means that completely unstained cells can be segmented and counted without being exposed to harmful UV illumination. Convert ai can be trained on all types of fluorescent patterns.Convert ai is part of the NIS-Elements ai module.
Segment ai for automatic segmentation of difficult objects
Some images are almost impossible to segment based on intensity values. These may be objects without contrast, objects that exhibit many different intensity levels, or objects with large morphological variation. Segment ai can be trained by manually thresholding and classifying the structures of interest. By manually selecting objects of interest and training Segment ai on them, it can learn the structures, find them on similar images, and apply segmentation to the objects. Segment ai requires a manual segmentation/selection the first time but can then be used again and again on similar specimens. Segment ai is part of the NIS-Elements ai module.
Contact the product specialist:
|
Catherine Kitts |
Marie Andersson |
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