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    This study introduces a novel 3D cell tracking method for microscopy images, utilizing dynamic hierarchical data structures for accurate segmentation and tracking of mouse embryonic stem (mES) cells, even with sudden changes in colony dynamics.

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    Area of Science:

    • Biomedical Imaging
    • Cell Biology
    • Computational Biology

    Background:

    • Accurate cell tracking in 3D time-lapse microscopy is crucial for understanding dynamic biological processes.
    • Existing methods often struggle with sudden changes in cell colony size, shape, and number, common in stem cell cultures.

    Purpose of the Study:

    • To develop and validate a robust cell tracking method for 3D time-lapse confocal microscopy images.
    • To improve the accuracy of cell and colony segmentation and correspondence in dynamic cell populations.

    Main Methods:

    • A novel cell tracking approach employing dynamic hierarchical data structures for 3D image analysis.
    • Segmentation of cells and colonies, recording geometric data for each 3D image set.
    • Computation of colony and cell correspondences between adjacent time-lapse frames using recorded geometric data.

    Main Results:

    • The proposed method demonstrates high tracking accuracy for mouse embryonic stem (mES) cells in 3D time-lapse confocal microscopy.
    • Successfully handles dynamic changes in cell colonies, including merging, splitting, proliferation, and cell death.
    • Geometric data within hierarchical structures facilitates visualization and quantification of cell shape and motility.

    Conclusions:

    • The developed method offers a robust solution for tracking cells in complex, dynamic 3D biological systems.
    • Enables precise quantitative analysis of cell behavior and population dynamics from time-lapse microscopy data.
    • Provides a valuable tool for research involving self-renewing cell populations like mES cells.