Cookies on this website

We use cookies to ensure that we give you the best experience on our website. If you click 'Accept all cookies' we'll assume that you are happy to receive all cookies and you won't see this message again. If you click 'Reject all non-essential cookies' only necessary cookies providing core functionality such as security, network management, and accessibility will be enabled. Click 'Find out more' for information on how to change your cookie settings.

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.

Original publication

DOI

10.1016/j.ymeth.2017.02.007

Type

Journal article

Journal

Methods (San Diego, Calif.)

Publication Date

02/2017

Volume

115

Pages

65 - 79

Addresses

Institute of Biomedical Engineering, Department of Engineering Science, Old Road Campus Research Building, University of Oxford, Headington, Oxford OX3 7DQ, United Kingdom.

Keywords

Eukaryotic Cells, Animals, Humans, Microscopy, Cell Culture Techniques, Biosensing Techniques, Image Processing, Computer-Assisted, Software, Molecular Imaging, Cell Tracking, Signal-To-Noise Ratio, Machine Learning