MICROSCOPY & IMAGING
AI MEETS LIVE CELL IMAGING O
lympus’ newly launched scanR 3.1 high-content screening (HCS) station fully embraces the capabilities of artificial intelligence (AI) to enable cutting-edge life science research. It combines the modularity and flexibility of a microscope-based set-up with the automation, speed, throughput and reproducibility of HCS applications. Using the ‘self-learning microscopy’ concept, scanR 3.1 makes it easy to gather data quickly from large live cell populations for reliable, well- supported experimental results. To minimise set-up time, the self-
learning approach makes use of a short,
one-time training phase in which the software uses a quickly acquired set of images to generate ‘ground truth’ data without requiring human annotations. It then uses convolutional neural networks to autonomously create robust algorithms that can rapidly analyse large sets of images.
One application that clearly shows
the power of AI in HCS is label-free quantification of live cells. The scanR HCS software can reliably derive nuclei positions in micro wells solely from brightfield transmission images with an accuracy that rivals fluorescence. Quantifying live cells from brightfield images instead of fluorescence shortens exposure times, avoids using genetic modifications or nucleus markers, and saves fluorescence channels for other markers. These benefits reduce phototoxicity and lead to simpler, faster image acquisition and better cell viability.
Reliable low light analysis has also become possible thanks to scanR’s AI-based imaging software. Olympus has shown that its software accurately detects DAPI- labelled cells at only 0.2% of the optimal light intensity. It can even distinguish different stages of the cell cycle, based on the intensity of the signal, providing better insights and improving reproducibility. Dr Manoel Veiga, application specialist at Olympus Soft Imaging Solutions, commented on the launch, saying: “This exciting new scanR system is good news for a whole range of life science applications. Low light and label-free analysis are only examples of how automated detection using an AI-based approach can improve the accuracy and reproducibility of HCS. Olympus’ self-learning microscopy approach for scanR 3.1 combines these benefits with a user-friendly workflow that requires little human interaction during the training stage. This makes it easy for life science researchers to harness the power of HCS data while benefitting from a fast, simple set-up.”
For more information visit
www.olympus-lifescience.com EXPANDED PORTFOLIO OF MICROSCOPE CAMERAS
eiss has introduced four new high-quality CMOS cameras for digital imaging in light microscopy. These cameras complete the portfolio of Axiocam models that stand for excellent performance in demanding microscopy applications. The new microscope cameras Axiocam 705 colour and 712 colour deliver the best possible image quality for histology, pathology or material research and analysis, thanks to excellent colour rendition and greatly improved dynamic range. Axiocam 705 mono and 712 mono are ideal for demanding fluorescence live- cell imaging with fast frame rates and high dynamic range. Also, their extended near-IR sensitivity allows for deeper insights into sample structures. Demanding microscopy applications
Z
call for a combination of excellent contrast, resolution, dynamic range, sensitivity and readout speed. In all these aspects, the
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performance of the four new Axiocam microscope
cameras benefits greatly from their new high-quality CMOS sensors. Small 3.45 µm pixels and low noise levels in combination with the fast USB 3.0 platform enable researchers to carry out extremely fast imaging experiments while maintaining excellent signal quality. Also, the new cameras’ global shutter architecture allows capturing dynamic samples without creating motion artifacts. The Axiocam 705 mono and Axiocam 705 color are 5-megapixel cameras
optimised for fast speed and high dynamic range. Both are ideal for the acquisition of large samples with 12 megapixels, thereby greatly reducing the need for stitching.
For more information visit
www.zeiss.com
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