From images to understanding: Advances in deep learning for cellular dynamics analysis.

Area of Science:

• Bioimage analysis
• Cellular dynamics
• Deep learning applications

Background:

• Deep learning (DL) has significantly advanced bioimage analysis, offering new ways to study cellular dynamics.
• Quantifying cellular dynamics is crucial for understanding biological processes.
• Traditional methods often face limitations in complex biological systems.

Purpose of the Study:

• To provide an overview of state-of-the-art deep learning (DL) approaches for quantifying cellular dynamics from 2D microscopy images.
• To highlight the integration of DL with classical algorithms in bioimage analysis.
• To guide researchers in applying DL tools for specific biological questions.

Main Methods:

• Segmentation: Identifying objects in space using DL.
• Tracking: Connecting segmented objects through time.
• Trajectory analysis: Extracting measurements from object movement patterns.

Main Results:

• DL methods offer powerful tools for segmentation, tracking, and trajectory analysis in bioimage data.
• Innovations in DL complement and enhance established bioimage analysis algorithms.
• Emerging trends focus on making DL-powered analysis scientifically sound and accessible.

Conclusions:

• DL is transforming cellular dynamics quantification in bioimage analysis.
• This review bridges the gap between computational methods and biological applications.
• Researchers can leverage DL tools for deeper insights into cellular behavior.