KAUST Grant NumberKUK-C1-013-04
Online Publication Date2015-02-12
Print Publication Date2015-02
Permanent link to this recordhttp://hdl.handle.net/10754/599775
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Abstract© 2014 The Authors. Microcirculation published by John Wiley & Sons Ltd. Objective: Recent developments in high-resolution imaging techniques have enabled digital reconstruction of three-dimensional sections of microvascular networks down to the capillary scale. To better interpret these large data sets, our goal is to distinguish branching trees of arterioles and venules from capillaries. Methods: Two novel algorithms are presented for classifying vessels in microvascular anatomical data sets without requiring flow information. The algorithms are compared with a classification based on observed flow directions (considered the gold standard), and with an existing resistance-based method that relies only on structural data. Results: The first algorithm, developed for networks with one arteriolar and one venular tree, performs well in identifying arterioles and venules and is robust to parameter changes, but incorrectly labels a significant number of capillaries as arterioles or venules. The second algorithm, developed for networks with multiple inlets and outlets, correctly identifies more arterioles and venules, but is more sensitive to parameter changes. Conclusions: The algorithms presented here can be used to classify microvessels in large microvascular data sets lacking flow information. This provides a basis for analyzing the distinct geometrical properties and modelling the functional behavior of arterioles, capillaries, and venules.
CitationSmith AF, Secomb TW, Pries AR, Smith NP, Shipley RJ (2015) Structure-Based Algorithms for Microvessel Classification. Microcirculation 22: 99–108. Available: http://dx.doi.org/10.1111/micc.12181.
SponsorsThis study was supported by Award No. KUK-C1-013-04 made by King Abdullah University of Science and Technology (KAUST), NIH grant HL070657, and a travel grant from St Anne's College, Oxford.
PubMed Central IDPMC4329063
CollectionsPublications Acknowledging KAUST Support
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