Opportunities and challenges for deep learning in cell dynamics research

Trends Cell Biol. 2023 Nov 21:S0962-8924(23)00228-3. doi: 10.1016/j.tcb.2023.10.010. Online ahead of print.ABSTRACTThe 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.PMID:38030542 | DOI:10.1016/j.tcb.2023.10.010
Source: Trends in Cell Biology - Category: Cytology Authors: Source Type: research