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DeepSeed Local Graph Matching for Densely Packed Cells Tracking.

Min Liu, Yalan Liu, Weili Qian

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    This study introduces DeepSeed, a novel method for robust plant cell tracking in microscopy images. DeepSeed improves cell tracking accuracy, especially in challenging unregistered or long-interval image sequences.

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    Area of Science:

    • Plant biology
    • Microscopy imaging
    • Computational biology

    Background:

    • Tracking densely packed plant cells in microscopy image sequences is challenging due to significant appearance changes over time.
    • Existing local graph matching algorithms for cell tracking rely heavily on robust seed cell pair identification, which is often unreliable in unregistered or large time-interval image sequences.

    Purpose of the Study:

    • To develop a robust method for identifying seed cell pairs in plant cell tracking.
    • To enhance the accuracy and reliability of plant cell tracking in challenging microscopy image sequences.

    Main Methods:

    • Proposed a DeepSeed local graph matching model combining local graph matching with CNN-based similarity learning.
    • Utilized spatial-temporal contextual information of cells and similarity information of cell pairs.
    • CNN-based similarity learning was employed to learn deep features and measure cell pair similarity.

    Main Results:

    • The DeepSeed local graph matching method demonstrated robust seed cell pair identification.
    • Experimental results show DeepSeed can track most cells in unregistered image sequences.
    • The DeepSeed tracking algorithm accurately tracks cells across image sequences with large time intervals.

    Conclusions:

    • DeepSeed significantly improves the robustness of seed cell pair finding in plant cell tracking.
    • The proposed method enhances cell tracking performance in challenging scenarios like unregistered images and large time gaps.
    • DeepSeed offers a more reliable approach for analyzing plant cell dynamics from microscopy data.