Visualizing stable features in live cell nucleus for evaluation of the cell global motion compensation

Area of Science:

• Cell Biology
• Biophysics
• Microscopy Image Analysis

Background:

• Accurate analysis of subcellular foci motion in live cells requires compensating for cell movement and deformation.
• Image registration is commonly used for cell motion compensation, but evaluating its quality on real data is challenging due to the lack of stable reference features.
• Existing methods for cell image registration vary in models and properties, necessitating reliable evaluation strategies.

Purpose of the Study:

• To develop a methodology for generating live cell nuclei image sequences with artificially imposed stable features.
• To enable quantitative evaluation of cell image registration methods using these generated datasets.
• To introduce a novel image analysis technique and metric for assessing registration quality.

Main Methods:

• Imposing stable features by creating regions of fluorescence bleaching using UV laser exposure in live cell nuclei.
• Acquiring image sequences exhibiting diverse cell deformations.
• Developing an image analysis technique and a quantitative metric to evaluate registration performance.

Main Results:

• Successfully created image sequences with imposed stable features in live cell nuclei.
• Demonstrated the utility of these datasets for evaluating cell image registration methods.
• Established a quantitative metric for assessing the quality of registration approaches.

Conclusions:

• The proposed methodology provides a reliable way to generate ground truth datasets for testing cell image registration.
• This approach facilitates thorough and quantitative evaluation of image registration techniques in live cell imaging.
• Enables more accurate single-particle tracking analysis by improving cell motion compensation.