| Literature DB >> 28242295 |
Thomas A Nketia1, Heba Sailem1, Gustavo Rohde2, Raghu Machiraju3, Jens Rittscher4.
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.Entities:
Keywords: Biological image analysis; Cell segmentation; Cell tracking; Live cell imaging; Machine learning; Quantitative biological imaging
Mesh:
Year: 2017 PMID: 28242295 DOI: 10.1016/j.ymeth.2017.02.007
Source DB: PubMed Journal: Methods ISSN: 1046-2023 Impact factor: 3.608