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dc.contributor.authorBusto, L.
dc.contributor.authorVeiga Garcia, Cesar
dc.contributor.authorGonzález-Nóvoa, J.A.
dc.contributor.authorLoureiro-Ga, M.
dc.contributor.authorJiménez, V.
dc.contributor.authorBaz Alonso, José Antonio 
dc.contributor.authorIñiguez Romo, Andres 
dc.date.accessioned2025-02-24T11:15:51Z
dc.date.available2025-02-24T11:15:51Z
dc.date.issued2022
dc.identifier.issn2075-4418
dc.identifier.urihttp://hdl.handle.net/20.500.11940/19611
dc.description.abstractTranscatheter aortic valve implantation (TAVI) has become the treatment of choice for patients with severe aortic stenosis and high surgical risk. Angiography has been established as an essential tool in TAVI, as this modality provides real-time images required to support the intervention. The automatic interpretation and parameter extraction on such images can lead to significative improvements and new applications in the procedure that, in most cases, rely on a prior identification of the transcatheter heart valve (THV). In this paper, U-Net architecture is proposed for the automatic segmentation of THV on angiographies, studying the role of its hyperparameters in the quality of the segmentations. Several experiments have been conducted, testing the methodology using multiple configurations and evaluating the results on different types of frames captured during the procedure. The evaluation has been performed in terms of conventional classification metrics, complemented with two new metrics, specifically defined for this problem. Those new metrics provide a more appropriate assessment of the quality of the results, given the class imbalance in the dataset. From an analysis of the evaluation results, it can be concluded that the method provides appropriate segmentation results for this dataset.
dc.language.isoenes
dc.rightsAtribución 4.0 Internacional
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.titleAutomatic Identification of Bioprostheses on X-ray Angiographic Sequences of Transcatheter Aortic Valve Implantation Procedures Using Deep Learning
dc.typeJournal Articlees
dcterms.bibliographicCitationBusto L, Veiga C, González-Nóvoa JA, Loureiro-Ga M, Jiménez V, Baz JA, et al. Automatic Identification of Bioprostheses on X-ray Angiographic Sequences of Transcatheter Aortic Valve Implantation Procedures Using Deep Learning. Diagnostics. 2022;12(2).
dc.authorsophosBusto, A. L.;Veiga, C.;González-Nóvoa, J. A.;Loureiro-Ga, M.;Jiménez, V.;Baz, J. A.;Íñiguez
dc.identifier.doi10.3390/DIAGNOSTICS12020334
dc.identifier.sophos620cb24175b45172b87a5a43
dc.issue.number2
dc.journal.titleDiagnostics
dc.relation.publisherversionhttps://www.mdpi.com/2075-4418/12/2/334/pdf?version=1644560491es
dc.rights.accessRightsopenAccess
dc.subject.keywordIISGSes
dc.subject.keywordAS Vigoes
dc.subject.keywordCHUVIes
dc.volume.number12


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