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Manual and automatic image analysis segmentation methods for blood flow studies in microchannels

2021Journal article Open access
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dc.contributor.authorCarvalho, Violeta Meneses
dc.contributor.authorGonçalves, Inês M.
dc.contributor.authorSouza, Andrews Victor Almeida
dc.contributor.authorSouza, Maria Sabrina
dc.contributor.authorBento, David
dc.contributor.authorRibeiro, J.E.
dc.contributor.authorLima, Rui A.
dc.contributor.authorPinho, Diana
dc.date.accessioned2022-01-20T10:32:12Z
dc.date.available2022-01-20T10:32:12Z
dc.date.issued2021
dc.description.abstractIn blood flow studies, image analysis plays an extremely important role to examine raw data obtained by high-speed video microscopy systems. This work shows different ways to process the images which contain various blood phenomena happening in microfluidic devices and in microcirculation. For this purpose, the current methods used for tracking red blood cells (RBCs) flowing through a glass capillary and techniques to measure the cell-free layer thickness in different kinds of microchannels will be presented. Most of the past blood flow experimental data have been collected and analysed by means of manual methods, that can be extremely reliable, but they are highly time-consuming, user-intensive, repetitive, and the results can be subjective to user-induced errors. For this reason, it is crucial to develop image analysis methods able to obtain the data automatically. Concerning automatic image analysis methods for individual RBCs tracking and to measure the well know microfluidic phenomena cell-free layer thickness, two developed methods are present and discuss in order to demonstrate their feasibility for accurate data acquisition in such studies Additionally, a comparison analysis between manual and automatic methods was performed.pt_PT
dc.description.sponsorshipThis project has been funded by Portuguese national funds of FCT/MCTES (PIDDAC) through the base funding from the following research units: UIDB/00532/2020 (Transport Phenomena Research Center—CEFT), UIDB/04077/2020 (Mechanical Engineering and Resource Sustainability Center—MEtRICs), UIDB/00690/2020 (CIMO). The authors are also grateful for the partial funding of FCT through the projects, NORTE-01-0145-FEDER-029394 (PTDC/EMD-EMD/29394/2017) and NORTE-01-0145-FEDER-030171 (PTDC/EMD-EMD/30171/2017) funded by COMPETE2020, NORTE2020, PORTUGAL2020 and FEDER. D. Bento acknowledges the PhD scholarship SFRH/BD/91192/2012 granted by FCT.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationCarvalho, Violeta; Gonçalves, Inês M.; Souza, Andrews; Souza, Maria S.; Bento, David; Ribeiro, J.E.; Lima, Rui; Pinho, Diana (2021). Manual and automatic image analysis segmentation methods for blood flow studies in microchannels. Micromachines. ISSN 2072-666X. 12:3, p. 1-20pt_PT
dc.identifier.doi10.3390/mi12030317pt_PT
dc.identifier.issn2072-666X
dc.identifier.urihttp://hdl.handle.net/10198/24780
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.relationPTDC/EMD-EMD/30171/2017pt_PT
dc.relationTransport Phenomena Research Center
dc.relationMechanical Engineering and Resource Sustainability Center
dc.relationMountain Research Center
dc.relationReal time Liver-on-a-chip platform with integrated micro(bio)sensors for preclinical validation of graphene-based magnetic nanocarriers towards cancer theranostics
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectBlood flowpt_PT
dc.subjectParticle trackingpt_PT
dc.subjectRed blood cellspt_PT
dc.subjectManual methodspt_PT
dc.subjectAutomatic methodspt_PT
dc.subjectImage analysispt_PT
dc.subjectBiomicrofluidicspt_PT
dc.titleManual and automatic image analysis segmentation methods for blood flow studies in microchannelspt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.awardTitleTransport Phenomena Research Center
oaire.awardTitleMechanical Engineering and Resource Sustainability Center
oaire.awardTitleMountain Research Center
oaire.awardTitleReal time Liver-on-a-chip platform with integrated micro(bio)sensors for preclinical validation of graphene-based magnetic nanocarriers towards cancer theranostics
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F00532%2F2020/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F04077%2F2020/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F00690%2F2020/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/9471 - RIDTI/PTDC%2FEMD-EMD%2F29394%2F2017/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/SFRH/SFRH%2FBD%2F91192%2F2012/PT
oaire.citation.issue3pt_PT
oaire.citation.startPage317pt_PT
oaire.citation.titleMicromachinespt_PT
oaire.citation.volume12pt_PT
oaire.fundingStream6817 - DCRRNI ID
oaire.fundingStream6817 - DCRRNI ID
oaire.fundingStream6817 - DCRRNI ID
oaire.fundingStream9471 - RIDTI
oaire.fundingStreamSFRH
person.familyNameRibeiro
person.familyNamePinho
person.givenNameJ.E.
person.givenNameDiana
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person.identifier.orcid0000-0002-3884-6496
person.identifier.ridG-3839-2018
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project.funder.identifierhttp://doi.org/10.13039/501100001871
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project.funder.nameFundação para a Ciência e a Tecnologia
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rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
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