Machine learning applications in cell image analysis.

Area of Science:

• Biomedical image analysis
• Cell biology
• Computational microscopy

Background:

• Machine learning (ML) offers automated pattern recognition for complex data.
• Biomedical image analysis, particularly in light microscopy, faces challenges in cell segmentation, tracking, and lineage reconstruction due to morphological variability and clutter.
• A review of ML applications is needed to address these challenges.

Purpose of the Study:

• To review machine learning applications in light microscopy image analysis.
• To cover key tasks including cell segmentation, cell tracking, and lineage tree modeling.
• To provide an overview of ML concepts and examples relevant to these tasks.

Main Methods:

• Review of existing literature on machine learning in biomedical image analysis.
• Description of a typical image analysis pipeline and associated challenges.
• Presentation of supervised and active learning methods for cell segmentation and tracking.

Main Results:

• Machine learning methods, including supervised learning, can improve cell segmentation accuracy.
• Active learning strategies are effective for enhancing cell tracking in challenging conditions.
• The review covers ML applications across various image processing stages.

Conclusions:

• Machine learning provides powerful tools for automating and improving cell image analysis in light microscopy.
• Further research is needed in parameter setting and exploring advanced ML techniques for complex biological questions.
• This review serves as a guide to ML applications for researchers in cell biology and image analysis.