Cell tracking using phase-adaptive shape prior.

Area of Science:

• Biomedical Engineering
• Cell Biology
• Computational Biology

Background:

• Accurate tracking of individual cells is essential for quantitative analysis of cell population dynamics.
• Existing methods often suffer from error propagation due to inaccuracies in individual tracking steps.
• Understanding cell-cycle behavior requires precise monitoring of cell populations over time.

Purpose of the Study:

• To develop a holistic, automated system for accurate cell population tracking and cell-cycle phase identification.
• To address subproblems in cell tracking, including segmentation and phase determination, to minimize error propagation.
• To provide a robust method for quantitative measurements of dynamic cell-cycle behavior.

Main Methods:

• A three-component system learning mean cell shape and temporal dynamics for shape prior estimation.
• Level set-based segmentation utilizing the estimated shape prior for precise cell outlining.
• Cell phase identification based on goodness-of-fit to the segmentation model, with feedback for refinement.

Main Results:

• Empirical evaluation demonstrates high accuracy in tracking individual cells from HeLa H2B-GFP populations.
• Validation confirms the method's robustness across various realistic scenarios.
• Each component of the integrated system was found essential for overall performance.

Conclusions:

• The proposed holistic system effectively automates cell population tracking and phase identification.
• The integrated approach minimizes error propagation, leading to more reliable quantitative measurements.
• This method offers a robust solution for studying dynamic cell-cycle behaviors in biological research.