Methods to label, image, and analyze the complex structural architectures of microvascular networks

Area of Science:

• Physiology
• Biomedical Engineering
• Medical Imaging

Background:

• Microvascular networks are vital for tissue oxygen and nutrient supply, adapting structurally for development, healing, and disease.
• Characterizing microvascular architecture is challenged by imaging limitations and a lack of precise quantitative metrics.
• Current methods for microvessel analysis face low adoption due to validation, segmentation, and standardization issues.

Purpose of the Study:

• To review existing models, imaging techniques, and quantification metrics for microvascular networks.
• To identify limitations in current microvascular network analysis tools and their adoption.
• To propose strategies for enhancing the effectiveness, rigor, and reproducibility of microvascular quantification.

Main Methods:

• Survey of computational models for microvasculature studies.
• Review of techniques for microvessel labeling and imaging.
• Analysis of metrics and software packages for quantifying microvascular networks.

Main Results:

• Existing software provides valuable tools but suffers from low adoption rates.
• Key limitations include basic validation, segmentation performance, and nonstandard quantification metrics.
• The review identifies a need for improved and standardized approaches to microvascular network analysis.

Conclusions:

• Enhanced characterization of microvascular networks is essential for advancing biological and medical research.
• Addressing current limitations in quantification methods will improve research rigor and reproducibility.
• Future efforts should focus on developing validated, standardized, and user-friendly tools for microvascular network analysis.