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Self-learning microscopy opens new horizons

20th December 2019


Olympus has announced the US launch of the scanR high-content screening (HCS) station, a cell imaging solution that uses artificial intelligence (AI) to enable next-generation biological research. It combines the modularity and flexibility of a microscope-based setup with the automation, speed, throughput and reproducibility of an HCS station.

AI meets high-content screening

The scanR HCS station uses the power of AI to carry out groundbreaking analyses of cells that, until recently, seemed impossible to do. Olympus’ self-learning microscopy technology reduces photobleaching and improves acquisition speed, measurement sensitivity, and accuracy, facilitating longer observations with reduced influence on cell viability.

The power of deep learning

The system acquires pairs of images that are processed by the software to generate an image analysis model. Optimised deep-learning technology, which is based on a dedicated convolutional neural network architecture, provides powerful and flexible learned analysis protocols. No human data annotations are required, which allows large numbers of examples to be used, enabling the potential of the deep-learning technology to be fully exploited.

Self-learning microscopy

After a one-time training phase, scanR AI enables the system to automatically analyse new data by incorporating the learned analysis protocol into its assay-based workflow. Because the user has full control in designing the training experiment and many challenging analysis conditions can be covered during the training phase, the accuracy and robustness of the analysis results are improved.

Robust, fast and automated image acquisition and analysis

The scanR system performs fully automated image acquisition and analysis of multiwell plates, slides and custom-built arrays. Throughout the automated image acquisition, the system maintains the focus plane using a combination of software algorithms and hardware, including TruFocus Z-drift compensation. Images are automatically analysed during acquisition to minimise analysis time, and all units are precisely synchronised by a real-time controller to maximize the acquisition speed.
 




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