Opportunities and challenges for deep learning in cell dynamics research

• Published in: Trends in cell biology Vol. 34; no. 11; pp. 955 - 967
• Main Authors: Chai, Binghao, Efstathiou, Christoforos, Yue, Haoran, Draviam, Viji M.
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.11.2024
• Subjects: DL tools for organelle detection; neural networks for microscopy analysis; open-source image analysis tools and datasets; subcellular tracking challenges; subcellular tracking challenges; neural networks for microscopy analysis; DL tools for organelle detection; open-source image analysis tools and datasets
• ISSN: 0962-8924; 1879-3088
• DOI: 10.1016/j.tcb.2023.10.010

Artificial intelligence (AI)-guided methods are transforming the speed and scale with which image segmentation and classification tasks can be managed in cell biology.Deep learning (DL)-guided tools to segment and classify a variety of cells and subcellular structures are being rapidly developed, opening the need for standards and repositories.Despite DL-guided advances in still-image analysis, tracking objects in microscopy movies remains an area of open development owing to spatial and temporal discontinuities.DL methods offer new opportunities to significantly expand genotype–phenotype maps, genetic variant analysis, and drug development and discovery. The growth of artificial intelligence (AI) has led to 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 has also started to support advances in drug development, precision medicine, and genome–phenome mapping. We survey existing AI-based techniques and tools, as well as 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 a computational perspective and review emerging research frontiers and innovative applications for DL-guided automation in cell dynamics research. The growth of artificial intelligence (AI) has led to 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 has also started to support advances in drug development, precision medicine, and genome–phenome mapping. We survey existing AI-based techniques and tools, as well as 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 DL-guided automation in cell dynamics research.