title: Analysis of live cell images: Methods, tools and opportunities.
abstract: Advances in optical microscopy, biosensors and cell culturing technologies have transformed live cell imaging. Thanks to these advances live cell imaging plays an increasingly important role in basic biology research as well as at all stages of drug development. Image analysis methods are needed to extract quantitative information from these vast and complex data sets. The aim of this review is to provide an overview of available image analysis methods for live cell imaging, in particular required preprocessing image segmentation, cell tracking and data visualisation methods. The potential opportunities recent advances in machine learning, especially deep learning, and computer vision provide are being discussed. This review includes overview of the different available software packages and toolkits.
authors.affiliation: Institute of Biomedical Engineering, Department of Engineering Science, Old Road Campus Research Building, University of Oxford, Headington, Oxford OX3 7DQ, United Kingdom.
authors.forename: Thomas A
authors.initials: TA
authors.lastname: Nketia
authors.affiliation: Institute of Biomedical Engineering, Department of Engineering Science, Old Road Campus Research Building, University of Oxford, Headington, Oxford OX3 7DQ, United Kingdom.
authors.forename: Heba
authors.initials: H
authors.lastname: Sailem
authors.affiliation: Department of Biomedical Engineering, University of Virginia, Charlottesville, VA 22908, United States.
authors.forename: Gustavo
authors.initials: G
authors.lastname: Rohde
authors.affiliation: Computer Science and Engineering, The Ohio State University, 2015 Neil Ave, Columbus, OH 43210, United States.
authors.forename: Raghu
authors.initials: R
authors.lastname: Machiraju
authors.affiliation: Institute of Biomedical Engineering, Department of Engineering Science, Old Road Campus Research Building, University of Oxford, Headington, Oxford OX3 7DQ, United Kingdom; Ludwig Institute for Cancer Research, University of Oxford, Nuffield Department of Medicine, Old Road Campus Research Building, Oxford OX3 7DQ, United Kingdom; Target Discovery Institute, NDM Research Building, University of Oxford, Old Road Campus, Headington OX3 7FZ, United Kingdom. Electronic address: jens.rittscher@eng.ox.ac.uk.
authors.forename: Jens
authors.initials: J
authors.lastname: Rittscher
mesh_terms: D000818:Animals; D015374:Biosensing Techniques / Q000295:instrumentation / Q000379:methods; D018929:Cell Culture Techniques; D058948:Cell Tracking / Q000295:instrumentation / Q000379:methods; D005057:Eukaryotic Cells / Q000378:metabolism / Q000648:ultrastructure; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods* / Q000706:statistics & numerical data; D000069550:Machine Learning; D008853:Microscopy / Q000295:instrumentation / Q000379:methods*; D057054:Molecular Imaging / Q000295:instrumentation / Q000379:methods*; D059629:Signal-To-Noise Ratio; D012984:Software
keywords: Biological image analysis; Cell segmentation; Cell tracking; Live cell imaging; Machine learning; Quantitative biological imaging