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A Full Skin Defect Model to Evaluate Vascularization of Biomaterials In Vivo

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Published on: August 28, 2014

Quantifying Vascular Density in Tissue Engineered Constructs Using Machine Learning.

Hannah A Strobel¹, Alex Schultz², Sarah M Moss¹

¹Tissue Modeling, Advanced Solutions Life Sciences, Manchester, NH, United States.

Frontiers in Physiology

|May 14, 2021

Summary

This summary is machine-generated.

Researchers developed BioSegment, a machine learning tool for rapid, high-throughput vascular density measurements. This software aids in monitoring engineered tissue constructs and advancing biomedical applications.

Keywords:

angiogenesis machine learning neovessel growth vascularity vessel quantification

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Area of Science:

Biomedical Engineering
Computational Biology
Vascular Biology

Background:

Accurate measurement of vascular density is crucial for understanding tissue health and function.
The complex, dynamic, and heterogeneous nature of vasculature presents measurement challenges.
Existing methods may lack the throughput or automation needed for complex biomedical applications.

Purpose of the Study:

To develop a semi-automated, machine learning-based software for quantifying vascular metrics.
To enable high-throughput vascular density measurements from various imaging modalities.
To provide a tool for monitoring vascular growth in engineered tissue constructs.

Main Methods:

Development of a semi-automated software tool named BioSegment.
Leveraging machine learning algorithms for identification and quantification of vascular structures.
Application to angiogenesis models imaged using fluorescent and phase contrast modalities.

Main Results:

BioSegment enables rapid and high-throughput measurement of vascular density.
The software effectively quantifies vascular metrics in complex 3D architectures.
Demonstrated utility in an angiogenesis model across different imaging techniques.

Conclusions:

BioSegment offers an efficient solution for vascular density assessment in biomedical research.
The software's speed makes it suitable for integration into tissue manufacturing workflows.
Facilitates robust monitoring of vascular development in engineered tissues.