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Shape-to-graph mapping method for efficient characterization and classification of complex geometries in biological images

With the ever-increasing quality and quantity of imaging data in biomedical research comes the demand for computational methodologies that enable efficient and reliable automated extraction of the quantitative information contained within these images. One of the challenges in providing such methodo...

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Autores principales: Pilcher, William, Yang, Xingyu, Zhurikhina, Anastasia, Chernaya, Olga, Xu, Yinghan, Qiu, Peng, Tsygankov, Denis
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7494120/
https://www.ncbi.nlm.nih.gov/pubmed/32881897
http://dx.doi.org/10.1371/journal.pcbi.1007758
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author Pilcher, William
Yang, Xingyu
Zhurikhina, Anastasia
Chernaya, Olga
Xu, Yinghan
Qiu, Peng
Tsygankov, Denis
author_facet Pilcher, William
Yang, Xingyu
Zhurikhina, Anastasia
Chernaya, Olga
Xu, Yinghan
Qiu, Peng
Tsygankov, Denis
author_sort Pilcher, William
collection PubMed
description With the ever-increasing quality and quantity of imaging data in biomedical research comes the demand for computational methodologies that enable efficient and reliable automated extraction of the quantitative information contained within these images. One of the challenges in providing such methodology is the need for tailoring algorithms to the specifics of the data, limiting their areas of application. Here we present a broadly applicable approach to quantification and classification of complex shapes and patterns in biological or other multi-component formations. This approach integrates the mapping of all shape boundaries within an image onto a global information-rich graph and machine learning on the multidimensional measures of the graph. We demonstrated the power of this method by (1) extracting subtle structural differences from visually indistinguishable images in our phenotype rescue experiments using the endothelial tube formations assay, (2) training the algorithm to identify biophysical parameters underlying the formation of different multicellular networks in our simulation model of collective cell behavior, and (3) analyzing the response of U2OS cell cultures to a broad array of small molecule perturbations.
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spelling pubmed-74941202020-09-24 Shape-to-graph mapping method for efficient characterization and classification of complex geometries in biological images Pilcher, William Yang, Xingyu Zhurikhina, Anastasia Chernaya, Olga Xu, Yinghan Qiu, Peng Tsygankov, Denis PLoS Comput Biol Research Article With the ever-increasing quality and quantity of imaging data in biomedical research comes the demand for computational methodologies that enable efficient and reliable automated extraction of the quantitative information contained within these images. One of the challenges in providing such methodology is the need for tailoring algorithms to the specifics of the data, limiting their areas of application. Here we present a broadly applicable approach to quantification and classification of complex shapes and patterns in biological or other multi-component formations. This approach integrates the mapping of all shape boundaries within an image onto a global information-rich graph and machine learning on the multidimensional measures of the graph. We demonstrated the power of this method by (1) extracting subtle structural differences from visually indistinguishable images in our phenotype rescue experiments using the endothelial tube formations assay, (2) training the algorithm to identify biophysical parameters underlying the formation of different multicellular networks in our simulation model of collective cell behavior, and (3) analyzing the response of U2OS cell cultures to a broad array of small molecule perturbations. Public Library of Science 2020-09-03 /pmc/articles/PMC7494120/ /pubmed/32881897 http://dx.doi.org/10.1371/journal.pcbi.1007758 Text en © 2020 Pilcher et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Pilcher, William
Yang, Xingyu
Zhurikhina, Anastasia
Chernaya, Olga
Xu, Yinghan
Qiu, Peng
Tsygankov, Denis
Shape-to-graph mapping method for efficient characterization and classification of complex geometries in biological images
title Shape-to-graph mapping method for efficient characterization and classification of complex geometries in biological images
title_full Shape-to-graph mapping method for efficient characterization and classification of complex geometries in biological images
title_fullStr Shape-to-graph mapping method for efficient characterization and classification of complex geometries in biological images
title_full_unstemmed Shape-to-graph mapping method for efficient characterization and classification of complex geometries in biological images
title_short Shape-to-graph mapping method for efficient characterization and classification of complex geometries in biological images
title_sort shape-to-graph mapping method for efficient characterization and classification of complex geometries in biological images
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7494120/
https://www.ncbi.nlm.nih.gov/pubmed/32881897
http://dx.doi.org/10.1371/journal.pcbi.1007758
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