Please use this identifier to cite or link to this item: https://doi.org/10.15480/882.3826
DC FieldValueLanguage
dc.contributor.authorBargsten, Lennart-
dc.contributor.authorKlisch, Daniel-
dc.contributor.authorRiedl, Katharina Alina-
dc.contributor.authorWissel, Tobias-
dc.contributor.authorBrunner, Fabian J.-
dc.contributor.authorSchaefers, Klaus-
dc.contributor.authorGraß, Michael-
dc.contributor.authorBlankenberg, Stefan-
dc.contributor.authorSeiffert, Moritz-
dc.contributor.authorSchlaefer, Alexander-
dc.date.accessioned2021-10-14T10:14:09Z-
dc.date.available2021-10-14T10:14:09Z-
dc.date.issued2021-08-01-
dc.identifier.citationCurrent Directions in Biomedical Engineering 7 (1): 20211125, 106-110 (2021-08-01)de_DE
dc.identifier.issn2364-5504de_DE
dc.identifier.urihttp://hdl.handle.net/11420/10495-
dc.description.abstractAlgorithms for automated analysis of intravascular ultrasound (IVUS) images can be disturbed by guidewires, which are often encountered when treating bifurcations in percutaneous coronary interventions. Detecting guidewires in advance can therefore help avoiding potential errors. This task is not trivial, since guidewires appear rather small compared to other relevant objects in IVUS images. We employed CNNs with additional multi-task learning as well as different guidewire-specific regularizations to enable and improve guidewire detection. In this context, we developed a network block which generates heatmaps that highlight guidewires without the need of localization annotations. The guidewire detection results reach values of 0.931 in terms of the F1-score and 0.996 in terms of area under curve (AUC). Comparing thresholded guidewire heatmaps with ground truth segmentation masks leads to a Dice score of 23.1 % and an average Hausdorff distance of 1.45 mm. Guidewire detection has proven to be a task that CNNs can handle quite well. Employing multi-task learning and guidewire-specific regularizations further improve detection results and enable generation of heatmaps that indicate the position of guidewires without actual labels.en
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.subjectCoronary arteryde_DE
dc.subjectHeatmapde_DE
dc.subjectMulti-task learningde_DE
dc.subjectRegularizationde_DE
dc.subjectSegmentationde_DE
dc.subjectVesselde_DE
dc.subject.ddc600: Technikde_DE
dc.subject.ddc610: Medizinde_DE
dc.titleDeep learning for guidewire detection in intravascular ultrasound imagesde_DE
dc.typeArticlede_DE
dc.identifier.doi10.15480/882.3826-
dc.type.diniarticle-
dcterms.DCMITypeText-
tuhh.identifier.urnurn:nbn:de:gbv:830-882.0147375-
tuhh.oai.showtruede_DE
tuhh.abstract.englishAlgorithms for automated analysis of intravascular ultrasound (IVUS) images can be disturbed by guidewires, which are often encountered when treating bifurcations in percutaneous coronary interventions. Detecting guidewires in advance can therefore help avoiding potential errors. This task is not trivial, since guidewires appear rather small compared to other relevant objects in IVUS images. We employed CNNs with additional multi-task learning as well as different guidewire-specific regularizations to enable and improve guidewire detection. In this context, we developed a network block which generates heatmaps that highlight guidewires without the need of localization annotations. The guidewire detection results reach values of 0.931 in terms of the F1-score and 0.996 in terms of area under curve (AUC). Comparing thresholded guidewire heatmaps with ground truth segmentation masks leads to a Dice score of 23.1 % and an average Hausdorff distance of 1.45 mm. Guidewire detection has proven to be a task that CNNs can handle quite well. Employing multi-task learning and guidewire-specific regularizations further improve detection results and enable generation of heatmaps that indicate the position of guidewires without actual labels.de_DE
tuhh.publisher.doi10.1515/cdbme-2021-1023-
tuhh.publication.instituteMedizintechnische und Intelligente Systeme E-1de_DE
tuhh.identifier.doi10.15480/882.3826-
tuhh.type.opus(wissenschaftlicher) Artikel-
dc.type.driverarticle-
dc.type.casraiJournal Article-
tuhh.container.issue1de_DE
tuhh.container.volume7de_DE
tuhh.container.startpage106de_DE
tuhh.container.endpage110de_DE
dc.relation.projectMALEKA: Maschinelle Lernverfahren für die kardiovaskuläre Bildgebung auf der Grundlage des Programms für Innovation (PROFI) - Modul PROFI Transfer Plusde_DE
dc.rights.nationallicensefalsede_DE
dc.identifier.scopus2-s2.0-85114408031de_DE
tuhh.container.articlenumber20211125de_DE
local.status.inpressfalsede_DE
local.type.versionpublishedVersionde_DE
item.grantfulltextopen-
item.languageiso639-1en-
item.creatorOrcidBargsten, Lennart-
item.creatorOrcidKlisch, Daniel-
item.creatorOrcidRiedl, Katharina Alina-
item.creatorOrcidWissel, Tobias-
item.creatorOrcidBrunner, Fabian J.-
item.creatorOrcidSchaefers, Klaus-
item.creatorOrcidGraß, Michael-
item.creatorOrcidBlankenberg, Stefan-
item.creatorOrcidSeiffert, Moritz-
item.creatorOrcidSchlaefer, Alexander-
item.cerifentitytypePublications-
item.openairetypeArticle-
item.creatorGNDBargsten, Lennart-
item.creatorGNDKlisch, Daniel-
item.creatorGNDRiedl, Katharina Alina-
item.creatorGNDWissel, Tobias-
item.creatorGNDBrunner, Fabian J.-
item.creatorGNDSchaefers, Klaus-
item.creatorGNDGraß, Michael-
item.creatorGNDBlankenberg, Stefan-
item.creatorGNDSeiffert, Moritz-
item.creatorGNDSchlaefer, Alexander-
item.mappedtypeArticle-
item.openairecristypehttp://purl.org/coar/resource_type/c_6501-
item.fulltextWith Fulltext-
crisitem.author.deptMedizintechnische und Intelligente Systeme E-1-
crisitem.author.deptMedizintechnische und Intelligente Systeme E-1-
crisitem.author.orcid0000-0003-0610-0347-
crisitem.author.parentorgStudiendekanat Elektrotechnik, Informatik und Mathematik-
crisitem.author.parentorgStudiendekanat Elektrotechnik, Informatik und Mathematik-
crisitem.project.funderHamburgische Investitions- und Förderbank-
crisitem.project.funderrorid00012xz55-
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