An Earth Mover's Distance-Based Self-Supervised Framework for Cellular Dynamic Grading in Live-Cell Imaging

Area of Science:

• Computational biology
• Biomedical imaging
• Deep learning applications

Background:

• Cellular appearance dynamics are crucial for understanding live-cell physiology.
• Computational analysis of cell properties is vital in biological and biomedical research.
• Deep learning models for analyzing live-cell videos face data limitations.

Purpose of the Study:

• To develop a novel self-supervised framework for cellular dynamic grading (CDG).
• To overcome data collection and annotation challenges in CDG.
• To enhance the learning of spatiotemporal dynamics in live-cell videos.

Main Methods:

• A self-supervised learning framework incorporating a consistency constraint between cell grade and appearance change.
• Formulation of a probability transition matrix using Earth Mover's Distance.
• Imposing a loss constraint on the probability transition matrix elements.

Main Results:

• The proposed framework significantly enhances a model's ability to learn spatiotemporal dynamics.
• The self-supervised approach effectively addresses limitations posed by scarce annotated cellular video data.
• Experimental results show superior performance compared to existing methods on a cell video database.

Conclusions:

• The novel self-supervised framework offers a robust solution for cellular dynamic grading.
• This approach improves the accuracy and efficiency of analyzing cellular dynamics from microscopic videos.
• The method has the potential to advance live-cell imaging analysis in biomedical research.