AI + Macro Imaging
Clarify.ai is a new AI module that removes the blurred light contained in fluorescence images and generates high-contrast images. Clarify.ai, used with a stereomicroscope, can provide sharper fluorescence images than ever before; it enables not only macroscopic observation of model organisms, but also microscopic observation of fine structures.
Quantitative 3D Imaging of Living Organs-on-Chips with a High-Speed Point-Scanning Confocal System
Organs-on-chips more faithfully recapitulate the 3D architectural and functional complexity of native tissues compared to standard 2D tissue culture systems. Yet these advanced cell culture platforms present technical challenges for imaging-based applications. This Application Note demonstrates how the Nikon A1R HD25 confocal point-scanning system, CFI S Plan Fluor LWD 20XC objective and NIS-Elements software can enable rapid, deep, quantitative imaging of living cells in the Emulate Organ-Chip platform.
Highly accurate segmentation of cell areas based on DIC images using deep learning
Quantification of cell migration and cell confluency is important in biological and medical research on cellular functions. A scratch assay is used to quantitatively measure the speed of cells migrating to a cell-free area (gap) that is physically produced. This is a common technique for evaluating cell migration in cell development and differentiation, as well as in the invasion and metastasis of cancer cells. However, manually processing unstained sample images for quantification takes an immense amount of time. In addition, the Wound Healing function, a special application for scratch assays in the NIS-Elements imaging software, shows roughly correct results, but it has limited accuracy in terms of detailed detection.
In this Application Note, we introduce examples of quantification of a scratch assay using the NIS.ai module of NIS-Elements. These examples proved that NIS.ai can make more accurate inferences compared to the existing Wound Healing function based on a small number of training images.
Nikon NIS-Elements Denoise.ai Software: utilizing deep learning to denoise confocal data
Noise is a fundamental component of confocal images, a result of discreet digital sampling of continuously emitting photons from samples. The contribution of noise to image quality (signal-to-noise ratio) increases as the signal decreases as a square-root function. Using a trained neural network, we use artificial intelligence to remove the shot noise component from confocal image data, allowing an increase in image quality and the ability to acquire dimmer samples at faster rates. NIS-Elements software’s Denoise.ai deploys this trained network for live or post-acquisition processing.
Hardware Triggering: Maximizing Speed and Efficiency for Live Cell Imaging
Live cell imaging experiments now require higher speeds and more data throughput than ever before. Nikon Instruments has robust tools that enable hardware triggering of imaging devices in microscopy via direct signaling between hardware. This minimizes delays, synchronizes devices, and reduces the exposure of specimens to light. This Application Note explains how Nikon’s NIS-Elements hardwaretriggering workflow operates, and details its benefits for common time-lapse acquisition routines.