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    Biological cell tracking and lineage inference via random finite sets

    93296.pdf (662.4Kb)
    Access Status
    Open access
    Authors
    Nguyen, Tran Thien Dat
    Shim, Changbeom
    Kim, W.
    Date
    2021
    Type
    Conference Paper
    
    Metadata
    Show full item record
    Citation
    Nguyen, T.T.D. and Shim, C. and Kim, W. 2021. Biological cell tracking and lineage inference via random finite sets. In: 18th IEEE International Symposium on Biomedical Imaging (ISBI), 13-16 Apr 2021, Nice, France.
    Source Title
    Proceedings - International Symposium on Biomedical Imaging
    Source Conference
    18th IEEE International Symposium on Biomedical Imaging (ISBI)
    DOI
    10.1109/ISBI48211.2021.9433957
    ISBN
    9781665412469
    ISSN
    1945-7928
    Faculty
    Faculty of Science and Engineering
    School
    School of Elec Eng, Comp and Math Sci (EECMS)
    Funding and Sponsorship
    http://purl.org/au-research/grants/arc/DP160104662
    URI
    http://hdl.handle.net/20.500.11937/93471
    Collection
    • Curtin Research Publications
    Abstract

    Automatic cell tracking has long been a challenging problem due to the uncertainty of cell dynamic and observation process, where detection probability and clutter rate are unknown and time-varying. This is compounded when cell lineages are also to be inferred. In this paper, we propose a novel biological cell tracking method based on the Labeled Random Finite Set (RFS) approach to study cell migration patterns. Our method tracks cells with lineage by using a Generalised Label Multi-Bernoulli (GLMB) filter with objects spawning, and a robust Cardinalised Probability Hypothesis Density (CPHD) to address unknown and time-varying detection probability and clutter rate. The proposed method is capable of quantifying the certainty level of the tracking solutions. The capability of the algorithm on population dynamic inference is demonstrated on a migration sequence of breast cancer cells.

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