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Artifical Intelligence for Predictive Imaging

Home News Artifical Intelligence for Predictive Imaging
Artifical Intelligence for Predictive Imaging
24 Mar

Artifical Intelligence for Predictive Imaging

Nikon Instruments has announced a powerful image analysis and processing module for NIS-Elements that leverages Deep learning and Artifical Intelligence. is a suite of new AI-based processing tools that utilises convolutional neural networks to learn from small training datasets supplied by the user. The training results can then be easily applied to process and analyse huge volumes of data, enabling researchers to increase throughput and expand their application limits. includes a suite of applications for predictive imaging, image segmentation and processing: learns related patterns present in two different imaging channels. After training, can then predict the pattern in the second channel even when presented with only the first channel. DAPI-based staining of nuclei is a common method for cell segmentation and counting. can be trained to predict where the DAPI label would be based on unstained DIC or phase-contrast images. This enables users to perform nuclei-based image analysis without ever having to stain samples with DAPI or acquire a fluorescent channel. enables complex structures to be easily identified and segmented. Neurites in phase-contrast images are traditionally difficult to define by classic thresholding. can be trained on a small subset of hand-traced neurites to automatically detect and segment neurites from thousands of untraced datasets. Imaging dim fluorescent samples or applications that require low-level light exposure typically result in compromised image quality with poor signal-to-noise ratio. learns what a high signal-to-noise image looks like by comparing under-exposed and optimally-exposed images. can then restore details in under-exposed or dim fluorescent images, enabling researchers to gain more insights from their low-signal imaging applications. Launched earlier this year, removes shot noise from resonant confocal images and can be performed in real-time. Applying to resonant confocal imaging enables users to acquire confocal images at ultra high speed without sacrificing image quality.


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