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Multi-Grained Random Fields for Mitosis Identification in Time-Lapse Phase Contrast Microscopy Image Sequences.

An-An Liu, Jinhui Tang, Weizhi Nie

    IEEE Transactions on Medical Imaging
    |March 31, 2017
    PubMed
    Summary
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    This study introduces a novel multi-grained random fields (MGRFs) model for accurate mitosis identification. The MGRFs model effectively captures complex temporal dynamics in cell division sequences, outperforming existing methods.

    Area of Science:

    • Computational Biology
    • Biomedical Image Analysis
    • Machine Learning

    Background:

    • Mitosis identification is crucial for cell cycle analysis and cancer research.
    • Modeling gradual visual changes and hidden states in mitosis sequences presents significant challenges.
    • Existing methods struggle with capturing diverse temporal dynamics in cell division processes.

    Purpose of the Study:

    • To propose a novel Multi-Grained Random Fields (MGRFs) model for improved mitosis identification.
    • To address the limitations in hidden state discovery and sequential structure modeling for mitosis sequences.
    • To develop a probabilistic model for joint temporal and feature learning in cell division analysis.

    Main Methods:

    • Designed a graphical structure to convert individual mitosis sequences into coarse-to-fine grained sequences.

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  • Developed a probabilistic model for joint temporal and feature learning.
  • Decomposed MGRF model training into layer-wise sequential learning and graph-based sequential grouping for iterative optimization.

    • Main Results:

    • The MGRFs model demonstrated superior performance on challenging mitosis datasets (C3H10T1/2 and C2C12 stem cells).
    • The proposed method effectively models diverse temporal dynamics and visual patterns in mitosis sequences.
    • Extensive experiments confirmed the model's advantage over state-of-the-art techniques.

    Conclusions:

    • The MGRFs model offers a robust and effective approach for mitosis identification.
    • The proposed method advances the state-of-the-art in computational biology and biomedical image analysis.
    • This work provides a valuable tool for analyzing cell division dynamics in stem cells.