Deep learning for bioimage analysis

TL;DR

This review discusses how deep learning revolutionizes bioimage analysis by enabling processing of complex datasets, highlighting open-source tools, and exploring future applications in cell and developmental biology.

Contribution

It provides an accessible introduction to deep learning concepts, reviews its impact on bioimage analysis, and discusses future directions and open resources.

Findings

Deep learning significantly enhances bioimage analysis capabilities.

Open-source tools facilitate integration of deep learning in research.

Future applications include multimodal data analysis in biology.

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 analyse 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.