Please use this identifier to cite or link to this item: https://doi.org/10.15480/882.3823
DC FieldValueLanguage
dc.contributor.authorMieling, Till Robin-
dc.contributor.authorSprenger, Johanna-
dc.contributor.authorLatus, Sarah-
dc.contributor.authorHolstein, Lennart-
dc.contributor.authorSchlaefer, Alexander-
dc.date.accessioned2021-10-14T04:55:59Z-
dc.date.available2021-10-14T04:55:59Z-
dc.date.issued2021-08-01-
dc.identifier.citationCurrent Directions in Biomedical Engineering 7 (1): 20211105, 21-25 (2021-08-01)de_DE
dc.identifier.issn2364-5504de_DE
dc.identifier.urihttp://hdl.handle.net/11420/10492-
dc.description.abstractThe distinction between malignant and benign tumors is essential to the treatment of cancer. The tissue's elasticity can be used as an indicator for the required tissue characterization. Optical coherence elastography (OCE) probes have been proposed for needle insertions but have so far lacked the necessary load sensing capabilities. We present a novel OCE needle probe that provides simultaneous optical coherence tomography (OCT) imaging and load sensing at the needle tip. We demonstrate the application of the needle probe in indentation experiments on gelatin phantoms with varying gelatin concentrations. We further implement two deep learning methods for the end-toend sample characterization from the acquired OCT data. We report the estimation of gelatin sample weight ratios [wt%] in unseen samples with a mean error of 1.21 ± 0.91 wt%. Both evaluated deep learning models successfully provide sample characterization with different advantages regarding the accuracy and inference time.en
dc.description.sponsorshipDeutsche Forschungsgemeinschaft (DFG)de_DE
dc.language.isoende_DE
dc.publisherDe Gruyterde_DE
dc.relation.ispartofCurrent directions in biomedical engineeringde_DE
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/de_DE
dc.subjectDeep Learningde_DE
dc.subjectElastographyde_DE
dc.subjectNeedle Probede_DE
dc.subjectOptical Coherence Tomographyde_DE
dc.subjectTissue Characterizationde_DE
dc.subject.ddc600: Technikde_DE
dc.subject.ddc610: Medizinde_DE
dc.titleA novel optical needle probe for deep learning-based tissue elasticity characterizationde_DE
dc.typeArticlede_DE
dc.identifier.doi10.15480/882.3823-
dc.type.diniarticle-
dcterms.DCMITypeText-
tuhh.identifier.urnurn:nbn:de:gbv:830-882.0147334-
tuhh.oai.showtruede_DE
tuhh.abstract.englishThe distinction between malignant and benign tumors is essential to the treatment of cancer. The tissue's elasticity can be used as an indicator for the required tissue characterization. Optical coherence elastography (OCE) probes have been proposed for needle insertions but have so far lacked the necessary load sensing capabilities. We present a novel OCE needle probe that provides simultaneous optical coherence tomography (OCT) imaging and load sensing at the needle tip. We demonstrate the application of the needle probe in indentation experiments on gelatin phantoms with varying gelatin concentrations. We further implement two deep learning methods for the end-toend sample characterization from the acquired OCT data. We report the estimation of gelatin sample weight ratios [wt%] in unseen samples with a mean error of 1.21 ± 0.91 wt%. Both evaluated deep learning models successfully provide sample characterization with different advantages regarding the accuracy and inference time.de_DE
tuhh.publisher.doi10.1515/cdbme-2021-1005-
tuhh.publication.instituteMedizintechnische und Intelligente Systeme E-1de_DE
tuhh.identifier.doi10.15480/882.3823-
tuhh.type.opus(wissenschaftlicher) Artikel-
dc.type.driverarticle-
dc.type.casraiJournal Article-
tuhh.container.issue1de_DE
tuhh.container.volume7de_DE
tuhh.container.startpage21de_DE
tuhh.container.endpage25de_DE
dc.relation.projectMechatronisch geführte Mikronavigation von Nadeln in Weichgewebede_DE
dc.rights.nationallicensefalsede_DE
dc.identifier.scopus2-s2.0-85114433823de_DE
tuhh.container.articlenumber20211105de_DE
local.status.inpressfalsede_DE
local.type.versionpublishedVersionde_DE
local.funding.infoThis work was partially funded by the TUHH i3 initiative.de_DE
item.openairecristypehttp://purl.org/coar/resource_type/c_6501-
item.creatorOrcidMieling, Till Robin-
item.creatorOrcidSprenger, Johanna-
item.creatorOrcidLatus, Sarah-
item.creatorOrcidHolstein, Lennart-
item.creatorOrcidSchlaefer, Alexander-
item.cerifentitytypePublications-
item.mappedtypeArticle-
item.openairetypeArticle-
item.fulltextWith Fulltext-
item.grantfulltextopen-
item.creatorGNDMieling, Till Robin-
item.creatorGNDSprenger, Johanna-
item.creatorGNDLatus, Sarah-
item.creatorGNDHolstein, Lennart-
item.creatorGNDSchlaefer, Alexander-
item.languageiso639-1en-
crisitem.project.funderDeutsche Forschungsgemeinschaft (DFG)-
crisitem.project.funderid501100001659-
crisitem.project.funderrorid018mejw64-
crisitem.project.grantnoSCHL 1844/2-2-
crisitem.funder.funderid501100001659-
crisitem.funder.funderrorid018mejw64-
crisitem.author.deptMedizintechnische und Intelligente Systeme E-1-
crisitem.author.deptMedizintechnische und Intelligente Systeme E-1-
crisitem.author.deptMedizintechnische und Intelligente Systeme E-1-
crisitem.author.deptMedizintechnische und Intelligente Systeme E-1-
crisitem.author.deptMedizintechnische und Intelligente Systeme E-1-
crisitem.author.orcid0000-0002-9796-7001-
crisitem.author.orcid0000-0003-0610-0347-
crisitem.author.parentorgStudiendekanat Elektrotechnik, Informatik und Mathematik-
crisitem.author.parentorgStudiendekanat Elektrotechnik, Informatik und Mathematik-
crisitem.author.parentorgStudiendekanat Elektrotechnik, Informatik und Mathematik-
crisitem.author.parentorgStudiendekanat Elektrotechnik, Informatik und Mathematik-
crisitem.author.parentorgStudiendekanat Elektrotechnik, Informatik und Mathematik-
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