Apr. 17, 2019
Applications

Label-Free Cell Nuclei Analysis with AI

  • Brightfield image of a microwell showing artefacts (scratches, condensation, meniscus effect) that can make automated quantification unreliable, and AI prediction overlay (blue).Brightfield image of a microwell showing artefacts (scratches, condensation, meniscus effect) that can make automated quantification unreliable, and AI prediction overlay (blue).

Automated analysis of cell populations in microwell plates, without the need for fluo-rescent labeling, offers researchers a number of significant benefits. For the process to be reliable, however, it is essential that proto-cols are both robust and easy to use. Meth-ods based on artificial intelligence (AI) are ideally suited to address this challenge. Using the right software, AI-based image analysis requires minimal human input and a short training phase. Here, we demonstrate as an example application for the new self-learn-ing microscopy approach how AI can reliably detect and analyze cell nuclei from unstained brightfield images with an accuracy that ex-ceeds fluorescence-based methods.

Read full application note here.

Contact

Olympus Europa SE & Co. KG
Amsinckstraße 63
20097 Hamburg
Germany
Phone: +49 40 23773 0
Telefax: +49 40 23376 5

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