EllipTrack: A Global-Local Cell-Tracking Pipeline for 2D Fluorescence Time-Lapse Microscopy

Area of Science:

• Cell Biology
• Bioimaging
• Computational Biology

Background:

• Time-lapse microscopy enables single-cell dynamics monitoring.
• Long-term cell tracking is challenging in large-scale, multi-day movies with complex cellular behaviors (e.g., migration, density changes, drug effects).

Purpose of the Study:

• To develop and present EllipTrack, a robust cell-tracking pipeline.
• To overcome limitations of existing trackers in complex microscopy data.
• To enable accurate reconstruction of cell lineages from challenging time-lapse movies.

Main Methods:

• Developed EllipTrack, a global-local cell-tracking pipeline.
• Employs a global track-linking algorithm to maximize lineage probability.
• Incorporates a local track-correction module for systematic error identification and amendment.

Main Results:

• EllipTrack outperforms state-of-the-art cell trackers in benchmarking.
• Achieves nearly error-free cell lineage reconstruction for large-scale movies.
• Adapts to dynamic changes in cell migration and density, requiring minimal training data.

Conclusions:

• EllipTrack provides a highly accurate solution for long-term single-cell tracking.
• The pipeline effectively handles complex experimental conditions.
• Facilitates advanced research in cell dynamics and lineage tracing.