DC FieldValueLanguage
dc.contributor.authorGessert, Nils-
dc.contributor.authorPriegnitz, Torben-
dc.contributor.authorSaathoff, Thore-
dc.contributor.authorAntoni, Sven-Thomas-
dc.contributor.authorMeyer, David-
dc.contributor.authorHamann, Moritz Franz-
dc.contributor.authorJünemann, Klaus Peter-
dc.contributor.authorOtte, Christoph-
dc.contributor.authorSchlaefer, Alexander-
dc.date.accessioned2019-06-14T11:54:47Z-
dc.date.available2019-06-14T11:54:47Z-
dc.date.issued2018-
dc.identifier.citationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (11073 LNCS): 222-229 (2018)de_DE
dc.identifier.isbn978-303000936-6de_DE
dc.identifier.issn0302-9743de_DE
dc.identifier.urihttp://hdl.handle.net/11420/2791-
dc.description.abstractNeedle insertion is common during minimally invasive interventions such as biopsy or brachytherapy. During soft tissue needle insertion, forces acting at the needle tip cause tissue deformation and needle deflection. Accurate needle tip force measurement provides information on needle-tissue interaction and helps detecting and compensating potential misplacement. For this purpose we introduce an image-based needle tip force estimation method using an optical fiber imaging the deformation of an epoxy layer below the needle tip over time. For calibration and force estimation, we introduce a novel deep learning-based fused convolutional GRU-CNN model which effectively exploits the spatio-temporal data structure. The needle is easy to manufacture and our model achieves a mean absolute error of 1.76±1.5 mN with a cross-correlation coefficient of 0.9996, clearly outperforming other methods. We test needles with different materials to demonstrate that the approach can be adapted for different sensitivities and force ranges. Furthermore, we validate our approach in an ex-vivo prostate needle insertion scenario.en
dc.language.isoende_DE
dc.relation.ispartofLecture notes in computer sciencede_DE
dc.titleNeedle Tip Force Estimation Using an OCT Fiber and a Fused convGRU-CNN Architecturede_DE
dc.typeinProceedingsde_DE
dc.type.dinicontributionToPeriodical-
dcterms.DCMITypeText-
tuhh.abstract.englishNeedle insertion is common during minimally invasive interventions such as biopsy or brachytherapy. During soft tissue needle insertion, forces acting at the needle tip cause tissue deformation and needle deflection. Accurate needle tip force measurement provides information on needle-tissue interaction and helps detecting and compensating potential misplacement. For this purpose we introduce an image-based needle tip force estimation method using an optical fiber imaging the deformation of an epoxy layer below the needle tip over time. For calibration and force estimation, we introduce a novel deep learning-based fused convolutional GRU-CNN model which effectively exploits the spatio-temporal data structure. The needle is easy to manufacture and our model achieves a mean absolute error of 1.76±1.5 mN with a cross-correlation coefficient of 0.9996, clearly outperforming other methods. We test needles with different materials to demonstrate that the approach can be adapted for different sensitivities and force ranges. Furthermore, we validate our approach in an ex-vivo prostate needle insertion scenario.de_DE
tuhh.publisher.doi10.1007/978-3-030-00937-3_26-
tuhh.publication.instituteMedizintechnische Systeme E-1de_DE
tuhh.type.opusInProceedings (Aufsatz / Paper einer Konferenz etc.)-
tuhh.institute.germanMedizintechnische Systeme E-1de
tuhh.institute.englishMedizintechnische Systeme E-1de_DE
tuhh.gvk.hasppnfalse-
dc.type.drivercontributionToPeriodical-
dc.type.casraiConference Paper-
tuhh.container.startpage222de_DE
tuhh.container.endpage229de_DE
item.fulltextNo Fulltext-
item.languageiso639-1en-
item.creatorGNDGessert, Nils-
item.creatorGNDPriegnitz, Torben-
item.creatorGNDSaathoff, Thore-
item.creatorGNDAntoni, Sven-Thomas-
item.creatorGNDMeyer, David-
item.creatorGNDHamann, Moritz Franz-
item.creatorGNDJünemann, Klaus Peter-
item.creatorGNDOtte, Christoph-
item.creatorGNDSchlaefer, Alexander-
item.cerifentitytypePublications-
item.openairecristypehttp://purl.org/coar/resource_type/c_5794-
item.creatorOrcidGessert, Nils-
item.creatorOrcidPriegnitz, Torben-
item.creatorOrcidSaathoff, Thore-
item.creatorOrcidAntoni, Sven-Thomas-
item.creatorOrcidMeyer, David-
item.creatorOrcidHamann, Moritz Franz-
item.creatorOrcidJünemann, Klaus Peter-
item.creatorOrcidOtte, Christoph-
item.creatorOrcidSchlaefer, Alexander-
item.openairetypeinProceedings-
item.grantfulltextnone-
crisitem.author.deptMedizintechnische Systeme E-1-
crisitem.author.deptMedizintechnische Systeme E-1-
crisitem.author.deptMedizintechnische Systeme E-1-
crisitem.author.deptMedizintechnische Systeme E-1-
crisitem.author.deptMedizintechnische Systeme E-1-
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-
crisitem.author.parentorgStudiendekanat Elektrotechnik, Informatik und Mathematik-
crisitem.author.parentorgStudiendekanat Elektrotechnik, Informatik und Mathematik-
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