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Quantifying Vascular Density in Tissue Engineered Constructs Using Machine Learning
Given the considerable research efforts in understanding and manipulating the vasculature in tissue health and function, making effective measurements of vascular density is critical for a variety of biomedical applications. However, because the vasculature is a heterogeneous collection of vessel se...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8110917/ https://www.ncbi.nlm.nih.gov/pubmed/33986691 http://dx.doi.org/10.3389/fphys.2021.650714 |
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author | Strobel, Hannah A. Schultz, Alex Moss, Sarah M. Eli, Rob Hoying, James B. |
author_facet | Strobel, Hannah A. Schultz, Alex Moss, Sarah M. Eli, Rob Hoying, James B. |
author_sort | Strobel, Hannah A. |
collection | PubMed |
description | Given the considerable research efforts in understanding and manipulating the vasculature in tissue health and function, making effective measurements of vascular density is critical for a variety of biomedical applications. However, because the vasculature is a heterogeneous collection of vessel segments, arranged in a complex three-dimensional architecture, which is dynamic in form and function, it is difficult to effectively measure. Here, we developed a semi-automated method that leverages machine learning to identify and quantify vascular metrics in an angiogenesis model imaged with different modalities. This software, BioSegment, is designed to make high throughput vascular density measurements of fluorescent or phase contrast images. Furthermore, the rapidity of assessments makes it an ideal tool for incorporation in tissue manufacturing workflows, where engineered tissue constructs may require frequent monitoring, to ensure that vascular growth benchmarks are met. |
format | Online Article Text |
id | pubmed-8110917 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-81109172021-05-12 Quantifying Vascular Density in Tissue Engineered Constructs Using Machine Learning Strobel, Hannah A. Schultz, Alex Moss, Sarah M. Eli, Rob Hoying, James B. Front Physiol Physiology Given the considerable research efforts in understanding and manipulating the vasculature in tissue health and function, making effective measurements of vascular density is critical for a variety of biomedical applications. However, because the vasculature is a heterogeneous collection of vessel segments, arranged in a complex three-dimensional architecture, which is dynamic in form and function, it is difficult to effectively measure. Here, we developed a semi-automated method that leverages machine learning to identify and quantify vascular metrics in an angiogenesis model imaged with different modalities. This software, BioSegment, is designed to make high throughput vascular density measurements of fluorescent or phase contrast images. Furthermore, the rapidity of assessments makes it an ideal tool for incorporation in tissue manufacturing workflows, where engineered tissue constructs may require frequent monitoring, to ensure that vascular growth benchmarks are met. Frontiers Media S.A. 2021-04-27 /pmc/articles/PMC8110917/ /pubmed/33986691 http://dx.doi.org/10.3389/fphys.2021.650714 Text en Copyright © 2021 Strobel, Schultz, Moss, Eli and Hoying. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Physiology Strobel, Hannah A. Schultz, Alex Moss, Sarah M. Eli, Rob Hoying, James B. Quantifying Vascular Density in Tissue Engineered Constructs Using Machine Learning |
title | Quantifying Vascular Density in Tissue Engineered Constructs Using Machine Learning |
title_full | Quantifying Vascular Density in Tissue Engineered Constructs Using Machine Learning |
title_fullStr | Quantifying Vascular Density in Tissue Engineered Constructs Using Machine Learning |
title_full_unstemmed | Quantifying Vascular Density in Tissue Engineered Constructs Using Machine Learning |
title_short | Quantifying Vascular Density in Tissue Engineered Constructs Using Machine Learning |
title_sort | quantifying vascular density in tissue engineered constructs using machine learning |
topic | Physiology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8110917/ https://www.ncbi.nlm.nih.gov/pubmed/33986691 http://dx.doi.org/10.3389/fphys.2021.650714 |
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