Vessel network extraction and analysis of mouse pulmonary vasculature via X-ray micro-computed tomographic imaging.

In this work, non-invasive high-spatial resolution three-dimensional (3D) X-ray micro-computed tomography (μCT) of healthy mouse lung vasculature is performed. Methodologies are presented for filtering, segmenting, and skeletonizing the collected 3D images. Novel methods for the removal of spurious...

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Autores principales:	Eric A Chadwick, Takaya Suzuki, Michael G George, David A Romero, Cristina Amon, Thomas K Waddell, Golnaz Karoubi, Aimy Bazylak
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/f01852db209f455aa0dc33ff7d3a84d8
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spelling	oai:doaj.org-article:f01852db209f455aa0dc33ff7d3a84d82021-12-02T19:57:52ZVessel network extraction and analysis of mouse pulmonary vasculature via X-ray micro-computed tomographic imaging.1553-734X1553-735810.1371/journal.pcbi.1008930https://doaj.org/article/f01852db209f455aa0dc33ff7d3a84d82021-04-01T00:00:00Zhttps://doi.org/10.1371/journal.pcbi.1008930https://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358In this work, non-invasive high-spatial resolution three-dimensional (3D) X-ray micro-computed tomography (μCT) of healthy mouse lung vasculature is performed. Methodologies are presented for filtering, segmenting, and skeletonizing the collected 3D images. Novel methods for the removal of spurious branch artefacts from the skeletonized 3D image are introduced, and these novel methods involve a combination of distance transform gradients, diameter-length ratios, and the fast marching method (FMM). These new techniques of spurious branch removal result in the consistent removal of spurious branches without compromising the connectivity of the pulmonary circuit. Analysis of the filtered, skeletonized, and segmented 3D images is performed using a newly developed Vessel Network Extraction algorithm to fully characterize the morphology of the mouse pulmonary circuit. The removal of spurious branches from the skeletonized image results in an accurate representation of the pulmonary circuit with significantly less variability in vessel diameter and vessel length in each generation. The branching morphology of a full pulmonary circuit is characterized by the mean diameter per generation and number of vessels per generation. The methods presented in this paper lead to a significant improvement in the characterization of 3D vasculature imaging, allow for automatic separation of arteries and veins, and for the characterization of generations containing capillaries and intrapulmonary arteriovenous anastomoses (IPAVA).Eric A ChadwickTakaya SuzukiMichael G GeorgeDavid A RomeroCristina AmonThomas K WaddellGolnaz KaroubiAimy BazylakPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 17, Iss 4, p e1008930 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Eric A Chadwick Takaya Suzuki Michael G George David A Romero Cristina Amon Thomas K Waddell Golnaz Karoubi Aimy Bazylak Vessel network extraction and analysis of mouse pulmonary vasculature via X-ray micro-computed tomographic imaging.
description	In this work, non-invasive high-spatial resolution three-dimensional (3D) X-ray micro-computed tomography (μCT) of healthy mouse lung vasculature is performed. Methodologies are presented for filtering, segmenting, and skeletonizing the collected 3D images. Novel methods for the removal of spurious branch artefacts from the skeletonized 3D image are introduced, and these novel methods involve a combination of distance transform gradients, diameter-length ratios, and the fast marching method (FMM). These new techniques of spurious branch removal result in the consistent removal of spurious branches without compromising the connectivity of the pulmonary circuit. Analysis of the filtered, skeletonized, and segmented 3D images is performed using a newly developed Vessel Network Extraction algorithm to fully characterize the morphology of the mouse pulmonary circuit. The removal of spurious branches from the skeletonized image results in an accurate representation of the pulmonary circuit with significantly less variability in vessel diameter and vessel length in each generation. The branching morphology of a full pulmonary circuit is characterized by the mean diameter per generation and number of vessels per generation. The methods presented in this paper lead to a significant improvement in the characterization of 3D vasculature imaging, allow for automatic separation of arteries and veins, and for the characterization of generations containing capillaries and intrapulmonary arteriovenous anastomoses (IPAVA).
format	article
author	Eric A Chadwick Takaya Suzuki Michael G George David A Romero Cristina Amon Thomas K Waddell Golnaz Karoubi Aimy Bazylak
author_facet	Eric A Chadwick Takaya Suzuki Michael G George David A Romero Cristina Amon Thomas K Waddell Golnaz Karoubi Aimy Bazylak
author_sort	Eric A Chadwick
title	Vessel network extraction and analysis of mouse pulmonary vasculature via X-ray micro-computed tomographic imaging.
title_short	Vessel network extraction and analysis of mouse pulmonary vasculature via X-ray micro-computed tomographic imaging.
title_full	Vessel network extraction and analysis of mouse pulmonary vasculature via X-ray micro-computed tomographic imaging.
title_fullStr	Vessel network extraction and analysis of mouse pulmonary vasculature via X-ray micro-computed tomographic imaging.
title_full_unstemmed	Vessel network extraction and analysis of mouse pulmonary vasculature via X-ray micro-computed tomographic imaging.
title_sort	vessel network extraction and analysis of mouse pulmonary vasculature via x-ray micro-computed tomographic imaging.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/f01852db209f455aa0dc33ff7d3a84d8
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Vessel network extraction and analysis of mouse pulmonary vasculature via X-ray micro-computed tomographic imaging.

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