Utilize este identificador para referenciar este registo:
https://hdl.handle.net/1822/71191
Registo completo
Campo DC | Valor | Idioma |
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dc.contributor.author | Queirós, Sandro Filipe Monteiro | por |
dc.contributor.author | Morais, Pedro André Gonçalves | por |
dc.contributor.author | Fonseca, Jaime C. | por |
dc.contributor.author | D'hooge, Jan | por |
dc.contributor.author | Vilaça, João L. | por |
dc.date.accessioned | 2021-04-01T13:01:26Z | - |
dc.date.issued | 2019 | - |
dc.identifier.isbn | 9781510625457 | por |
dc.identifier.issn | 0277-786X | - |
dc.identifier.uri | https://hdl.handle.net/1822/71191 | - |
dc.description.abstract | Accurate preoperative sizing of the aortic annulus (AoA) is crucial to determine the best fitting prosthesis to be implanted during transcatheter aortic valve (AV) implantation (TAVI). Although multidetector row computed tomography is currently the standard imaging modality for such assessment, 3D cardiac magnetic resonance (CMR) is a feasible radiation-free alternative. However, automatic AV segmentation and sizing in 3D CMR images is so far underexplored. In this sense, this study proposes a novel semi-automatic algorithm for AV tract segmentation and sizing in 3D CMR images using the recently presented shape-based B-spline Explicit Active Surfaces (BEAS) framework. Upon initializing the AV tract surface using two user-defined points, a dual-stage shape-based BEAS evolution is performed to segment the patient-specific AV wall. The obtained surface is then aligned with multiple reference AV tract surfaces to estimate the location of the aortic annulus, allowing to extract the relevant clinical measurements. The framework was validated in thirty datasets from a publicly available CMR benchmark, assessing the segmentation accuracy and the measurements' agreement against manual sizing. The automated segmentation showed an average absolute distance error of 0.54 mm against manually delineated surfaces, while demonstrating to be robust against the algorithm's parameters. In its turn, automated AoA area-derived diameters showed an excellent agreement against manual-based ones (-0.30 +/- 0.77 mm), being comparable to the interobserver agreement. Overall, the proposed framework proved to be accurate, robust and computationally efficient (around 1 sec) for AV tract segmentation and sizing in 3D CMR images, thus showing its potential for preoperative TAVI planning. | por |
dc.description.sponsorship | This work was funded by the project NORTE-01-0145-FEDER-000013, supported by Northern Portugal Regional Operational Programme (Norte2020), under the Portugal 2020 Partnership Agreement, through the European Regional Development Fund (FEDER), and has also been funded by FEDER funds, through Competitiveness Factors Operational Programme (COMPETE), and by national funds, through the FCT - Fundacao para a Ciencia e Tecnologia, under the scope of the project POCI-01-0145-FEDER-007038. The authors acknowledge support by FCT and the European Social Fund, through Programa Operacional Capital Humano (POCH), in the scope of the PhD grant SFRH/BD/93443/2013. S. Queiros would also like to acknowledge the kind support of the Fundação Luso-Americana para o Desenvolvimento (FLAD), which has funded the travel costs for participation at SPIE Medical Imaging 2019. | por |
dc.language.iso | eng | por |
dc.publisher | Society of Photo-optical Instrumentation Engineers (SPIE) | por |
dc.relation | NORTE-01-0145-FEDER-000013 | por |
dc.relation | POCI-01-0145-FEDER-007038 | por |
dc.relation | SFRH/BD/93443/2013 | por |
dc.rights | restrictedAccess | por |
dc.subject | Aortic valve segmentation | por |
dc.subject | Cardiac magnetic resonance | por |
dc.subject | TAVI | por |
dc.subject | Semi-automatic analysis | por |
dc.title | Semi-automatic aortic valve tract segmentation in 3D cardiac magnetic resonance images using shape-based B-spline Explicit Active Surfaces | por |
dc.type | conferencePaper | por |
dc.peerreviewed | yes | por |
dc.relation.publisherversion | https://www.spiedigitallibrary.org/conference-proceedings-of-spie/10949/2512777/Semi-automatic-aortic-valve-tract-segmentation-in-3D-cardiac-magnetic/10.1117/12.2512777.short | por |
oaire.citationConferenceDate | 16 - 21 Febr. 2019 | por |
sdum.event.title | SPIE Medical Imaging 2019 | por |
sdum.event.type | conference | por |
oaire.citationStartPage | 1094918-1 | por |
oaire.citationEndPage | 1094918-8 | por |
oaire.citationConferencePlace | San Diego, California, United States | por |
oaire.citationVolume | 10949 | - |
dc.date.updated | 2021-04-01T11:47:44Z | - |
dc.identifier.doi | 10.1117/12.2512777 | por |
dc.date.embargo | 10000-01-01 | - |
dc.subject.wos | Science & Technology | - |
sdum.export.identifier | 10301 | - |
sdum.journal | Proceedings of SPIE | - |
sdum.conferencePublication | Medical Imaging 2019: Image Processing | por |
sdum.bookTitle | MEDICAL IMAGING 2019: IMAGE PROCESSING | - |
Aparece nas coleções: | DEI - Artigos em atas de congressos internacionais |
Ficheiros deste registo:
Ficheiro | Descrição | Tamanho | Formato | |
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SQueiros_SPIE_2019.pdf Acesso restrito! | 602,05 kB | Adobe PDF | Ver/Abrir |