Abstract
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).
Highlights
Chronic pulmonary diseases are responsible for four million deaths per year [1]
We present the first quantitative identification of intrapulmonary arteriovenous anastomoses (IPAVA) in the pulmonary circuit via X-ray μCT
Additional angles of this IPAVA are shown in S4 Fig. In past literature, anastomoses may have been classified as noise [11,24,25], but with the new insights provided in our work, we provide a highly unique capability for researchers to identify anastomoses via the elucidation of connecting vessels in the absence of prohibitively destructive imaging that may otherwise obscure the features being imaged
Summary
Chronic pulmonary diseases are responsible for four million deaths per year [1]. Binary segmentation is the most straightforward approach to analyze this data; more sophisticated methods have been developed for the identification of vessel-like structures [16,17]. These methods of segmentation allow for identification of the vasculature but can experience limitations when used across a broad range of vessel sizes, are sensitive to signal noise in the data, and require further processing to achieve quantitative analysis
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