From pixels to insights: Machine learning and deep learning for bioimage analysis.

Area of Science:

• Computational Biology
• Biotechnology
• Image Processing

Background:

• Bioimage analysis is crucial for understanding cellular structures and functions.
• Traditional methods often lack automation, reproducibility, and scalability.
• Emerging computational techniques offer enhanced analytical capabilities.

Purpose of the Study:

• To provide an overview of machine learning (ML) and deep learning (DL) in bioimage analysis.
• To highlight the impact of ML and DL on various stages of the bioimage analysis workflow.
• To guide researchers in applying these advanced computational tools.

Main Methods:

• Review of historical development and core principles of ML and DL.
• Analysis of ML/DL integration in bioimage preprocessing, segmentation, feature extraction, object tracking, and classification.
• Examination of accessible software and tools for biologists.

Main Results:

• ML and DL significantly improve automation, reproducibility, and accuracy in bioimage analysis.
• These techniques enhance performance across all key bioimage analysis tasks.
• Numerous user-friendly tools are available for practical implementation.

Conclusions:

• ML and DL are transformative for bioimage analysis, enabling deeper biological insights.
• The accessibility of tools empowers biologists to adopt these powerful methods.
• Continued integration of ML/DL will drive advancements in biological research.