Journal Article

Deep learning: Promises for 3D nuclear imaging. A guide for biologists


For a century, the nucleus has been the focus of extensive investigations in cell biology. However, many questions remain about how its shape and size are regulated during development, in different tissues or during disease and aging. To track these changes, microscopy has long been the tool of choice. Image analysis has revolutionized this field of research by providing computational tools, translating qualitative images into quantitative parameters. Many tools were designed to delimit objects in 2D and eventually in 3D, to define their shapes, their number or position in nuclear space. Today, the field is driven by deep-learning methods, most taking advantage of convolutional neural networks. These techniques are remarkably adapted to biomedical images when trained on large datasets and powerful computer graphics cards. To promote these innovative and promising methods to cell biologists, this Review summarizes the main concepts and terminologies of deep learning. Special emphasis is placed on their availability. We highlight why quality and characteristics of training image datasets are important and where to find them, as well as how to create, store and share image datasets. Finally, we describe deep-learning methods well-suited for 3D analysis of nuclei and classify them according to their level of usability for biologists. Out of more than 150 published methods, we identify less than twelve that a biologist can use and explain why. Based on this experience, we propose best practices to share deep learning methods with biologists.

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Mougeot, Guillaume
Dubos, Tristan
Chausse, Frédéric
Péry, Emilie
Graumann, Katja
Tatout, Christophe
Evans, David E.
Desset, Sophie

Oxford Brookes departments

Department of Biological and Medical Sciences


Year of publication: 2022
Date of RADAR deposit: 2022-02-16

Creative Commons License This work is licensed under a Creative Commons Attribution 4.0 International License

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