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NIS.ai is a dedicated module for NIS-Elements. The module utilizes convolutional neural networks to learn from small training datasets supplied by the user. The training results can then be easily applied to process and analyze large volumes of data, increasing throughput and expanding application limits. NIS.ai has four new functions: Enhance.ai, Convert.ai, Segment.ai and Denoise.ai.
Some fluorescent samples express a very low signal and it is difficult to visualize or extract details for segmentation. Enhance.ai 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.
By recognizing patterns present in two different imaging channels, Convert.ai can be trained to predict what the second channel would look like when only the first channel is acquired.
Some images are nearly impossible to segment by traditional intensity thresholding methods. 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.
A dedicated denoising model for confocal. With new fluorescent techniques pushing intensities lower and acquisition speeds increasing, Denoise.ai can recognize and remove the shot noise component of images, increasing clarity and allowing for shorter exposure times or more exposures of specimens while maintaining viability.