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Mougeot G, Dubos T, Chausse F, Péry E, Graumann K, Tatout C, Evans DE, Desset S. Deep learning -- promises for 3D nuclear imaging: a guide for biologists. J Cell Sci 2022; 135:275041. [PMID: 35420128 PMCID: PMC9016621 DOI: 10.1242/jcs.258986] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Abstract
For the past 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 that can be used to translate qualitative images into quantitative parameters. Many tools have been designed to delimit objects in 2D and, eventually, in 3D in order to define their shapes, their number or their position in nuclear space. Today, the field is driven by deep-learning methods, most of which take advantage of convolutional neural networks. These techniques are remarkably adapted to biomedical images when trained using 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 the availability of these methods. We highlight why the 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 fewer than 12 that biologists can use, and we explain why this is the case. Based on this experience, we propose best practices to share deep-learning methods with biologists.
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Affiliation(s)
- Guillaume Mougeot
- Université Clermont Auvergne, CNRS, Inserm, GReD, F-63000 Clermont-Ferrand, France.,Department of Biological and Molecular Sciences, Faculty of Health and Life Sciences, Oxford Brookes University, Oxford OX3 0BP, UK
| | - Tristan Dubos
- Université Clermont Auvergne, CNRS, Inserm, GReD, F-63000 Clermont-Ferrand, France
| | - Frédéric Chausse
- Université Clermont Auvergne, Clermont Auvergne INP, CNRS, Institut Pascal, F-63000 Clermont-Ferrand, France
| | - Emilie Péry
- Université Clermont Auvergne, Clermont Auvergne INP, CNRS, Institut Pascal, F-63000 Clermont-Ferrand, France
| | - Katja Graumann
- Department of Biological and Molecular Sciences, Faculty of Health and Life Sciences, Oxford Brookes University, Oxford OX3 0BP, UK
| | - Christophe Tatout
- Université Clermont Auvergne, CNRS, Inserm, GReD, F-63000 Clermont-Ferrand, France
| | - David E Evans
- Department of Biological and Molecular Sciences, Faculty of Health and Life Sciences, Oxford Brookes University, Oxford OX3 0BP, UK
| | - Sophie Desset
- Université Clermont Auvergne, CNRS, Inserm, GReD, F-63000 Clermont-Ferrand, France
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