Opportunities and challenges for deep learning in cell dynamics research

TL;DR

This paper reviews how deep learning enhances microscopy image analysis, addressing challenges and enabling advances in cell dynamics research, drug development, and personalized medicine through segmentation, classification, and tracking techniques.

Contribution

It provides a comprehensive survey of AI-based methods, datasets, and emerging research frontiers in deep learning applications for cell dynamics analysis.

Findings

Deep learning improves segmentation, classification, and tracking in microscopy images.

AI techniques support advances in drug development and personalized medicine.

Emerging research focuses on automation and innovative applications in cell biology.

Abstract

With the growth of artificial intelligence (AI), there has been an increase in the adoption of computer vision and deep learning (DL) techniques for the evaluation of microscopy images and movies. This adoption has not only addressed hurdles in quantitative analysis of dynamic cell biological processes, but it has also started supporting advances in drug development, precision medicine and genome-phenome mapping. Here we survey existing AI-based techniques and tools, and open-source datasets, with a specific focus on the computational tasks of segmentation, classification, and tracking of cellular and subcellular structures and dynamics. We summarise long-standing challenges in microscopy video analysis from the computational perspective and review emerging research frontiers and innovative applications for deep learning-guided automation for cell dynamics research.