Deep learning for bioimage analysis in developmental biology

Author:

Hallou Adrien123ORCID,Yevick Hannah G.4ORCID,Dumitrascu Bianca5ORCID,Uhlmann Virginie6ORCID

Affiliation:

1. Cavendish Laboratory, Department of Physics, University of Cambridge, Cambridge, CB3 0HE, UK

2. Wellcome Trust/Cancer Research UK Gurdon Institute, University of Cambridge, Cambridge, CB2 1QN, UK

3. Wellcome Trust/Medical Research Council Stem Cell Institute, University of Cambridge, Cambridge, CB2 1QR, UK

4. Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, 02142, USA

5. Computer Laboratory, Cambridge, University of Cambridge, Cambridge, CB3 0FD, UK

6. European Bioinformatics Institute, European Molecular Biology Laboratory, Cambridge, CB10 1SD, UK

Abstract

ABSTRACT Deep learning has transformed the way large and complex image datasets can be processed, reshaping what is possible in bioimage analysis. As the complexity and size of bioimage data continues to grow, this new analysis paradigm is becoming increasingly ubiquitous. In this Review, we begin by introducing the concepts needed for beginners to understand deep learning. We then review how deep learning has impacted bioimage analysis and explore the open-source resources available to integrate it into a research project. Finally, we discuss the future of deep learning applied to cell and developmental biology. We analyze how state-of-the-art methodologies have the potential to transform our understanding of biological systems through new image-based analysis and modelling that integrate multimodal inputs in space and time.

Funder

Wellcome Trust

University of Cambridge

CRUK Gurdon Institute

National Institute of General Medical Sciences

European Molecular Biology Laboratory

Accelerate Programme for Scientific Discovery

Publisher

The Company of Biologists

Subject

Developmental Biology,Molecular Biology

Reference114 articles.

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