MICROSCOPY & IMAGING
Mike Woerdemann explains how deep learning overcomes many common microscopy challenges
M AI MEETS WHEN
networks to extract layers of information from a raw input, it relies on training data to yield accurate and consistent results. Te best-known practical applications of deep learning include speech recognition platforms such as Google Assistant and Amazon Alexa. However, deep learning has also seen considerable uptake across many scientific disciplines, largely because it overcomes the widely held perception that AI-based technologies are both complex and time consuming to set up. One such discipline is the field of
D
microscopy, which has leveraged deep learning to efficiently analyse the vast amounts of data generated by high- content screening and to tackle many other common microscopy challenges. For example, using deep learning microscopy, researchers have been able to carry out quantitative analysis of
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eep learning is a form of artificial intelligence (AI) inspired by the structure of the human brain. Using algorithms called neural
fluorescently labelled cells at ultra-low light exposure and perform label-free analysis of cells in microwell plates.
CHALLENGES OF HIGH-CONTENT SCREENING Microscopy data often uses segmentation, whereby thresholds based on signal
FIG. 1. After an automated training phase, Olympus’ TruAI module was able to locate nuclei with high accuracy at 100% (a), 2% (b) and 0.2% (c) of optimal lighting – with nuclei shapes only starting to deviate at 0.05% (d)
ICROSCOPY
intensity or colour are applied to images and used to extract the analysis targets. Drawbacks of this approach are that it can be extremely time-consuming and can affect the sample condition; it is also highly prone to operator bias. Moreover, as microscopy platforms have evolved to support high-throughput screening, the amount of data generated can quickly create a bottleneck. A further challenge in live cell imaging is that many cell studies require the use of fluorescent labels. Not only can exposure to strong excitation light influence cell behaviour, but adverse experimental conditions can lead to photodamage or phototoxicity with an observable impact on cell viability. Although these effects can be reduced with lower light exposure, the resulting decrease in fluorescence signal diminishes the signal-to-noise ratio to make quantitative image analysis difficult. Te impact of fluorescent labels can
be avoided using brightfield microscopy, which has the added advantages of
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