Live-cell imaging in the deep learning era

Authors: Joanna W Pylvänäinen, Estibaliz Gómez-de-Mariscal, Ricardo Henriques, Guillaume Jacquemet
Technologies: BioImage Model Zoo, CARE, DeepBacs, Fast4DReg, NanoJ, NanoJ-eSRRF, NanoJ-Fluidics, NanoJ-SRRF and ZeroCostDL4Mic
Review published in Current Opinion in Cell Biology, January 2023
Publisher: Elsevier Current Trends

Live-cell imaging in the deep learning era
DOI: 10.1016/

The manuscript "Live-cell imaging in the deep learning era" by Pylvänäinen et al. discusses the challenges and computational solutions in live-cell imaging, a technique used to observe living organisms in real time with high sensitivity and specificity. The authors highlight the role of deep learning (DL) in addressing issues such as drift, phototoxicity, and large dataset sizes. They emphasize the importance of selecting appropriate DL methods for processing live imaging data, considering the type and dimensionality of the data. The manuscript also covers denoising and restoring live imaging data using DL algorithms, improving the spatiotemporal resolution of live imaging data, and segmentation and tracking for extracting biological information from videos. The text also mentions various tools and techniques for live cell imaging analysis, such as Fast4DReg, Napari, and Cell-ACDC. Overall, the manuscript explores the advancements and applications of deep learning in analyzing live-cell imaging data.