Artificial intelligence (AI) is experiencing a surge of renewed interest in recent years. AI technology, based on concepts such as machine learning and deep learning, has seemingly limitless applications in fields ranging from self-driving vehicle design, to cybersecurity, to (of course) scientific research and health care. This technology has also greatly affected how we approach optical microscopy imaging and analysis for biological research. However, given that the mainstream applications for new AI methods are still building momentum, it may not be clear to researchers the types of scenarios that are well suited to AI-based solutions. Our goal in this supplement is to help inform readers of the possibilities presented by deep learning–based imaging and analysis methods for microscopy. This e-book includes an educational primer on deep learning in microscopy by Florian Jug, a group leader at the MPI-CBG Center for Systems Biology in Dresden, Germany, and expert in quantitative bioimaging methods utilizing deep learning. Also included are a number of recent research articles highlighting the use of machine learning or deep learning for image analysis.
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