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 overvi...
Phenotypic image analysis is the task of recognizing variations in cell properties using microscopic...
Thesis: Ph. D., Massachusetts Institute of Technology, Department of Chemical Engineering, 2018Catal...
These images are for testing with the self-supervised machine learning demo code posted to GitHub. ...
The use of fluorescence imaging methods, most recently based on fluorescent protein technology, and ...
Light microscopes are essential research tools in biology and medicine. Cell and tissue staining met...
With the contiguous shift of biology from a qualitative toward a quantitative field of research, digi...
Live cell imaging is an important biomedical research paradigm for studying dynamic cellular behavio...
Time-lapse live cell imaging has been increasingly employed by biological and biomedical researchers...
Many techniques have been developed during last 10 years for cell recognition in biomedical images. ...
Live cell imaging provides a powerful technique for the analysis of molecular dynamics within cells....
Recent advances in computer vision and machine learning underpin a collection of algorithms with an ...
Cell biology has the ability to yield an incredible amount of information regarding cell activity, a...
The systems-level analysis of complex biological processes requires methods that enable the quantifi...
Accompanying raw and processed data as well as analysis scripts for the publication Biophysics Rev. ...
AbstractLive-cell assays are used to study the dynamic functional cellular processes in High-Content...
Phenotypic image analysis is the task of recognizing variations in cell properties using microscopic...
Thesis: Ph. D., Massachusetts Institute of Technology, Department of Chemical Engineering, 2018Catal...
These images are for testing with the self-supervised machine learning demo code posted to GitHub. ...
The use of fluorescence imaging methods, most recently based on fluorescent protein technology, and ...
Light microscopes are essential research tools in biology and medicine. Cell and tissue staining met...
With the contiguous shift of biology from a qualitative toward a quantitative field of research, digi...
Live cell imaging is an important biomedical research paradigm for studying dynamic cellular behavio...
Time-lapse live cell imaging has been increasingly employed by biological and biomedical researchers...
Many techniques have been developed during last 10 years for cell recognition in biomedical images. ...
Live cell imaging provides a powerful technique for the analysis of molecular dynamics within cells....
Recent advances in computer vision and machine learning underpin a collection of algorithms with an ...
Cell biology has the ability to yield an incredible amount of information regarding cell activity, a...
The systems-level analysis of complex biological processes requires methods that enable the quantifi...
Accompanying raw and processed data as well as analysis scripts for the publication Biophysics Rev. ...
AbstractLive-cell assays are used to study the dynamic functional cellular processes in High-Content...
Phenotypic image analysis is the task of recognizing variations in cell properties using microscopic...
Thesis: Ph. D., Massachusetts Institute of Technology, Department of Chemical Engineering, 2018Catal...
These images are for testing with the self-supervised machine learning demo code posted to GitHub. ...