User-Accessible Machine Learning Approaches for Cell Segmentation and Analysis in Tissue.

Area of Science:

• Biomedical Image Analysis
• Computational Biology
• Machine Learning in Life Sciences

Background:

• Advanced image analysis using machine learning (ML) and deep learning (DL) enhances cell segmentation and classification.
• These techniques provide novel insights into biological mechanisms and cellular microenvironments in human diseases.
• High-quality, reproducible cell segmentation is crucial for analyzing diverse cell types across various imaging modalities.

Purpose of the Study:

• To highlight the advancements in cell segmentation and classification using ML and DL.
• To discuss the development of both modality-specific and multimodal solutions for cell segmentation.
• To emphasize the accessibility of these advanced methods through open-source tools for the broader biomedical research community.

Main Methods:

• Application of machine learning and deep learning algorithms for cell segmentation and classification.
• Development of imaging modality-specific and multimodal solutions.
• Creation of annotated cell libraries and strategies for leveraging annotations across modalities.

Main Results:

• Improved cell segmentation and classification leading to novel biological insights.
• Successful analysis of cells in situ and within tissues, revealing cellular microenvironment models.
• Development and accessibility of open-source software pipelines and ML model libraries for researchers.

Conclusions:

• Machine and deep learning are transformative for biomedical image analysis, particularly in cell segmentation and classification.
• Open-source tools are crucial for democratizing access to advanced computational methods in life sciences.
• Future directions include expanding ML models, enhancing classification capabilities, and developing novel DL strategies for biomedical research.