Machine learning applications in cell image analysis
- Author(s)
- Kan, A;
- Journal Title
- Immunol Cell Biol
- Publication Type
- Journal Article in press
- Abstract
- Machine learning (ML) refers to a set of automatic pattern recognition methods that have been successfully applied across various problem domains, including biomedical image analysis. This review focuses on ML applications for image analysis in light microscopy experiments with typical tasks of segmenting and tracking individual cells, and modelling of reconstructed lineage trees. After describing a typical image analysis pipeline and highlighting challenges of automatic analysis (for example, variability in cell morphology, tracking in presence of clutters) this review gives a brief historical outlook of ML, followed by basic concepts and definitions required for understanding examples. This article then presents several example applications at various image processing stages, including the use of supervised learning methods for improving cell segmentation, and the application of active learning for tracking. The review concludes with remarks on parameter setting and future directions.Immunology and Cell Biology advance online publication, 4 April 2017; doi:10.1038/icb.2017.16.
- Publisher
- Springer Nature
- Research Division(s)
- Immunology
- PubMed ID
- 28294138
- Publisher's Version
- https://doi.org/10.1038/icb.2017.16
- Terms of Use/Rights Notice
- Refer to copyright notice on published article.
Creation Date: 2017-04-12 10:42:08
Last Modified: 2017-04-12 10:52:48