Please use this identifier to cite or link to this item: https://doi.org/10.15480/882.3036
DC FieldValueLanguage
dc.contributor.authorBengs, Marcel-
dc.contributor.authorGessert, Nils Thorben-
dc.contributor.authorSchlaefer, Alexander-
dc.date.accessioned2020-11-04T12:21:19Z-
dc.date.available2020-11-04T12:21:19Z-
dc.date.issued2020-09-17-
dc.identifier.citationCurrent Directions in Biomedical Engineering 1 (6): 20200001 (2020)de_DE
dc.identifier.issn2364-5504de_DE
dc.identifier.urihttp://hdl.handle.net/11420/7722-
dc.description.abstractTracking and localizing objects is a central problem in computer-assisted surgery. Optical coherence tomography (OCT) can be employed as an optical tracking system, due to its high spatial and temporal resolution. Recently, 3D convolutional neural networks (CNNs) have shown promising performance for pose estimation of a marker object using single volumetric OCT images. While this approach relied on spatial information only, OCT allows for a temporal stream of OCT image volumes capturing the motion of an object at high volumes rates. In this work, we systematically extend 3D CNNs to 4D spatio-temporal CNNs to evaluate the impact of additional temporal information for marker object tracking. Across various architectures, our results demonstrate that using a stream of OCT volumes and employing 4D spatio-temporal convolutions leads to a 30% lower mean absolute error compared to single volume processing with 3D CNNs.en
dc.language.isoende_DE
dc.publisherDe Gruyterde_DE
dc.relation.ispartofCurrent directions in biomedical engineeringde_DE
dc.rightsCC BY 4.0de_DE
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/de_DE
dc.subjectconvolutional neural networksde_DE
dc.subjectoptical coherence tomographyde_DE
dc.subjectposition estimationde_DE
dc.subjectspatio-temporal datade_DE
dc.subject.ddc004: Informatikde_DE
dc.subject.ddc600: Technikde_DE
dc.subject.ddc610: Medizinde_DE
dc.title4D spatio-temporal convolutional networks for object position estimation in OCT volumesde_DE
dc.typeArticlede_DE
dc.identifier.doi10.15480/882.3036-
dc.type.diniarticle-
dcterms.DCMITypeText-
tuhh.identifier.urnurn:nbn:de:gbv:830-882.0111133-
tuhh.oai.showtruede_DE
tuhh.abstract.englishTracking and localizing objects is a central problem in computer-assisted surgery. Optical coherence tomography (OCT) can be employed as an optical tracking system, due to its high spatial and temporal resolution. Recently, 3D convolutional neural networks (CNNs) have shown promising performance for pose estimation of a marker object using single volumetric OCT images. While this approach relied on spatial information only, OCT allows for a temporal stream of OCT image volumes capturing the motion of an object at high volumes rates. In this work, we systematically extend 3D CNNs to 4D spatio-temporal CNNs to evaluate the impact of additional temporal information for marker object tracking. Across various architectures, our results demonstrate that using a stream of OCT volumes and employing 4D spatio-temporal convolutions leads to a 30% lower mean absolute error compared to single volume processing with 3D CNNs.de_DE
tuhh.publisher.doi10.1515/cdbme-2020-0001-
tuhh.publication.instituteMedizintechnische Systeme E-1de_DE
tuhh.identifier.doi10.15480/882.3036-
tuhh.type.opus(wissenschaftlicher) Artikel-
dc.type.driverarticle-
dc.type.casraiJournal Article-
tuhh.container.issue1de_DE
tuhh.container.volume6de_DE
dc.rights.nationallicensefalsede_DE
dc.identifier.scopus2-s2.0-85093500434de_DE
tuhh.container.articlenumber20200001de_DE
local.status.inpressfalsede_DE
local.type.versionpublishedVersionde_DE
item.openairecristypehttp://purl.org/coar/resource_type/c_6501-
item.creatorOrcidBengs, Marcel-
item.creatorOrcidGessert, Nils Thorben-
item.creatorOrcidSchlaefer, Alexander-
item.cerifentitytypePublications-
item.mappedtypeArticle-
item.openairetypeArticle-
item.fulltextWith Fulltext-
item.grantfulltextopen-
item.creatorGNDBengs, Marcel-
item.creatorGNDGessert, Nils Thorben-
item.creatorGNDSchlaefer, Alexander-
item.languageiso639-1en-
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.orcid0000-0002-2229-9547-
crisitem.author.orcid0000-0001-6325-5092-
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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