Handle URI:
http://hdl.handle.net/10754/599775
Title:
Structure-Based Algorithms for Microvessel Classification
Authors:
Smith, Amy F. ( 0000-0003-0964-1632 ) ; Secomb, Timothy W.; Pries, Axel R.; Smith, Nicolas P.; Shipley, Rebecca J.
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.
Citation:
Smith 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.
Publisher:
Wiley-Blackwell
Journal:
Microcirculation
KAUST Grant Number:
KUK-C1-013-04
Issue Date:
Feb-2015
DOI:
10.1111/micc.12181
PubMed ID:
25403335
PubMed Central ID:
PMC4329063
Type:
Article
ISSN:
1073-9688
Sponsors:
This 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.
Appears in Collections:
Publications Acknowledging KAUST Support

Full metadata record

DC FieldValue Language
dc.contributor.authorSmith, Amy F.en
dc.contributor.authorSecomb, Timothy W.en
dc.contributor.authorPries, Axel R.en
dc.contributor.authorSmith, Nicolas P.en
dc.contributor.authorShipley, Rebecca J.en
dc.date.accessioned2016-02-28T06:09:31Zen
dc.date.available2016-02-28T06:09:31Zen
dc.date.issued2015-02en
dc.identifier.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.en
dc.identifier.issn1073-9688en
dc.identifier.pmid25403335en
dc.identifier.doi10.1111/micc.12181en
dc.identifier.urihttp://hdl.handle.net/10754/599775en
dc.description.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.en
dc.description.sponsorshipThis 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.en
dc.publisherWiley-Blackwellen
dc.subjectDiscrete algorithmsen
dc.subjectMicrovascular networksen
dc.subjectVessel classificationen
dc.titleStructure-Based Algorithms for Microvessel Classificationen
dc.typeArticleen
dc.identifier.journalMicrocirculationen
dc.identifier.pmcidPMC4329063en
dc.contributor.institutionOxford Centre for Collaborative Applied Mathematics; Mathematical Institute; University of Oxford; Oxford UKen
dc.contributor.institutionDepartment of Physiology; University of Arizona; Tucson Arizona USAen
dc.contributor.institutionDepartment of Physiology; Charité-Universitätsmedizin Berlin; Berlin Germanyen
dc.contributor.institutionDepartment of Biomedical Engineering; King's College London; St. Thomas Hospital; London UKen
dc.contributor.institutionFaculty of Engineering; The University of Auckland; Auckland New Zealanden
dc.contributor.institutionDepartment of Mechanical Engineering; University College London; London UKen
kaust.grant.numberKUK-C1-013-04en

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