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Applications, promises, and pitfalls of deep learning for fluorescence image reconstruction


Deep learning is becoming an increasingly important tool for image reconstruction in fluorescence microscopy. We review state-of-the-art applications such as image restoration and super-resolution imaging, and discuss how the latest deep learning research could be applied to other image reconstruction tasks. Despite its successes, deep learning also poses substantial challenges and has limits. We discuss key questions, including how to obtain training data, whether discovery of unknown structures is possible, and the danger of inferring unsubstantiated image details.

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Fig. 1: Review of current applications of deep learning to fluorescence microscopy.
Fig. 2: Potential applications of deep learning in fluorescence microscopy and key concepts.

Data availability

Source code for the experiment described in Box 5 can be found at


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We thank our colleagues at the Chan Zuckerberg Biohub: A. Krishnan, B. Chhun, M. Leonetti, S. Mehta, J. Batson, and R. Gomez Sjoberg for insightful discussions, feedback, and review of the manuscript. We thank J. Zou for advice; M. Weigert for reviewing the manuscript and for innumerable discussions on the topics of deep learning and microscopy; and W. Ouyang, R. Prevedel, F. Jug, and others for feedback on the first version of the preprint. We thank the Chan Zuckerberg Biohub and its donors for funding this work.

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L.A.R. conceived the piece. C.B. and L.A.R. wrote the manuscript and designed the figures.

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Belthangady, C., Royer, L.A. Applications, promises, and pitfalls of deep learning for fluorescence image reconstruction. Nat Methods 16, 1215–1225 (2019).

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