Nikon NIS.ai Artificial Intelligence Imaging Analysis Module

Model: Nikon NIS.ai

Booth No: M512

Characteristic

Nikon NIS.ai Artificial Intelligence Imaging Analysis Module
Nikon has unveiled the latest expansion to its NIS-Elements suite — the Ai module — propelling microscopic imaging and analysis to an advanced new frontier.
The Nikon NIS-Elements software, designed for microscope control, image acquisition, and analysis, now leverages deep learning-based artificial intelligence algorithms to identify specific patterns and structured features within target images. This enables precise sample classification, enhanced image clarity, noise reduction, and a significant improvement in analytical performance. The system incorporates a feedback mechanism that allows retraining and fine-tuning with new data, ultimately generating optimized insights.
In addition, Nikon's newly launched AX/AX R series fully motorized confocal microscopes are equipped with the latest AI-driven tools. One such tool, Autosignal.ai, can rapidly predict optimal hardware parameters, minimizing the need for manual trial-and-error processes that are time-consuming and potentially damaging to samples.
Key AI Tools Integrated in NIS.ai:
◇ Enhance.ai – An automated denoising module that allows for reduced exposure times, thereby minimizing phototoxic effects on live cells.
◇ Convert.ai – Enables non-invasive cell counting by generating synthetic fluorescence images from unstained samples, reducing cellular damage caused by traditional labeling techniques.
◇ Segment.ai – Traditional image segmentation relies on intensity or grayscale thresholding, which often fails to accurately segment complex structures. With AI and deep learning, previously unattainable segmentation precision becomes possible.
◇ Clarify.ai – A pre-trained AI module capable of removing out-of-focus blur from widefield fluorescence microscopy images, resulting in significantly enhanced image detail and clarity.
◇ Denoise.ai – Trained on thousands of confocal microscopy datasets, this deep learning-based module identifies and removes shot noise in real time, improving signal quality during live imaging sessions.