VESNA: an open-source tool for automated 3D vessel segmentation and network analysis.

Area of Science:

• Biomedical imaging
• Quantitative biology
• Vascular biology

Background:

• Vascular networks are crucial in tissues and organs, playing a role in various diseases.
• Current blood vessel image analysis software has limitations, including 2D data processing, lack of batch processing, and strict experimental condition requirements.
• There is a need for adaptable software capable of batch processing 3D image data with simple preparation.

Purpose of the Study:

• To develop an automated software tool for quantitative analysis of three-dimensional vascular networks.
• To create a flexible macro adaptable to diverse experimental conditions and tissue culturing methods.

Main Methods:

• Development of VESNA, a Fiji (ImageJ) macro for automated segmentation and skeletonization of 3D fluorescence images.
• Application of VESNA to various 3D image datasets, including organoids and hydrogel-based cultures.
• Demonstration of VESNA's adaptability across different experimental goals and tissue culturing techniques.

Main Results:

• VESNA enables automated, quantitative analysis of vascular networks from 3D fluorescence images.
• The macro successfully processed diverse datasets, including organoids of varying sizes and complexities, and hydrogel cultures.
• VESNA requires minimal experimental preparation, demonstrating high adaptability.

Conclusions:

• VESNA addresses the need for efficient processing of large 3D vascular image data.
• Its flexibility across experimental conditions makes it valuable for drug screening, tissue development research, and disease mechanism studies.
• VESNA serves as a versatile tool for diverse three-dimensional vasculature research applications.