TY - JOUR
T1 - Vessel network extraction and analysis of mouse pulmonary vasculature via X-ray micro-computed tomographic imaging
AU - Chadwick, Eric A.
AU - Suzuki, Takaya
AU - George, Michael G.
AU - Romero, David A.
AU - Amon, Cristina
AU - Waddell, Thomas K.
AU - Karoubi, Golnaz
AU - Bazylak, Aimy
N1 - Funding Information:
Funding:Theauthorswouldliketogratefully acknowledgetheNaturalSciencesandEngineering ResearchCouncilofCanada(NSERC)Discovery Grant(AB),theNSERCCanadaResearchChairs Program(AB),andtheCanadaFirstResearch
Publisher Copyright:
© 2021 Chadwick et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
PY - 2021/4
Y1 - 2021/4
N2 - 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).
AB - 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).
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U2 - 10.1371/journal.pcbi.1008930
DO - 10.1371/journal.pcbi.1008930
M3 - Article
C2 - 33878108
AN - SCOPUS:85104836351
VL - 17
JO - PLoS Computational Biology
JF - PLoS Computational Biology
SN - 1553-734X
IS - 4
M1 - e1008930
ER -