Deep learning tools for outcome prediction in a trial fibrilation from cardiac MRI

Detalhes bibliográficos
Autor(a) principal: Lourenço, Ana Catarina Feliciano
Data de Publicação: 2021
Tipo de documento: Dissertação
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10451/48465
Resumo: Tese de mestrado integrado em Engenharia Biomédica e Biofísica (Engenharia Clínica e Instrumentação Médica), Universidade de Lisboa, Faculdade de Ciências, 2021
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spelling Deep learning tools for outcome prediction in a trial fibrilation from cardiac MRIFibrilhação AuricularEnfarte do miocárdioRessonância Magnética CardíacaDeep learningClassificação/ SegmentaçãoTeses de mestrado - 2021Domínio/Área Científica::Engenharia e Tecnologia::Engenharia MédicaTese de mestrado integrado em Engenharia Biomédica e Biofísica (Engenharia Clínica e Instrumentação Médica), Universidade de Lisboa, Faculdade de Ciências, 2021Atrial fibrillation (AF), is the most frequent sustained cardiac arrhythmia, described by an irregular and rapid contraction of the two upper chambers of the heart (the atria). AF development is promoted and predisposed by atrial dilation, which is a consequence of atria adaptation to AF. However, it is not clear whether atrial dilation appears similarly over the cardiac cycle and how it affects ventricular volumes. Catheter ablation is arguably the AF gold standard treatment. In their current form, ablations are capable of directly terminating AF in selected patients but are only first-time effective in approximately 50% of the cases. In the first part of this work, volumetric functional markers of the left atrium (LA) and left ventricle (LV) of AF patients were studied. More precisely, a customised convolutional neural network (CNN) was proposed to segment, across the cardiac cycle, the LA from short axis CINE MRI images acquired with full cardiac coverage in AF patients. Using the proposed automatic LA segmentation, volumetric time curves were plotted and ejection fractions (EF) were automatically calculated for both chambers. The second part of the project was dedicated to developing classification models based on cardiac MR images. The EMIDEC STACOM 2020 challenge was used as an initial project and basis to create binary classifiers based on fully automatic classification neural networks (NNs), since it presented a relatively simple binary classification task (presence/absence of disease) and a large dataset. For the challenge, a deep learning NN was proposed to automatically classify myocardial disease from delayed enhancement cardiac MR (DE-CMR) and patient clinical information. The highest classification accuracy (100%) was achieved with Clinic-NET+, a NN that used information from images, segmentations and clinical annotations. For the final goal of this project, the previously referred NNs were re-trained to predict AF recurrence after catheter ablation (CA) in AF patients using pre-ablation LA short axis in CINE MRI images. In this task, the best overall performance was achieved by Clinic-NET+ with a test accuracy of 88%. This work shown the potential of NNs to interpret and extract clinical information from cardiac MRI. If more data is available, in the future, these methods can potentially be used to help and guide clinical AF prognosis and diagnosis.Caetano, Gina Maria CostaVarela, MartaRepositório da Universidade de LisboaLourenço, Ana Catarina Feliciano2021-06-11T15:02:52Z202120212021-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10451/48465TID:202933962enginfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-11-08T16:51:52Zoai:repositorio.ul.pt:10451/48465Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T22:00:21.022456Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Deep learning tools for outcome prediction in a trial fibrilation from cardiac MRI
title Deep learning tools for outcome prediction in a trial fibrilation from cardiac MRI
spellingShingle Deep learning tools for outcome prediction in a trial fibrilation from cardiac MRI
Lourenço, Ana Catarina Feliciano
Fibrilhação Auricular
Enfarte do miocárdio
Ressonância Magnética Cardíaca
Deep learning
Classificação/ Segmentação
Teses de mestrado - 2021
Domínio/Área Científica::Engenharia e Tecnologia::Engenharia Médica
title_short Deep learning tools for outcome prediction in a trial fibrilation from cardiac MRI
title_full Deep learning tools for outcome prediction in a trial fibrilation from cardiac MRI
title_fullStr Deep learning tools for outcome prediction in a trial fibrilation from cardiac MRI
title_full_unstemmed Deep learning tools for outcome prediction in a trial fibrilation from cardiac MRI
title_sort Deep learning tools for outcome prediction in a trial fibrilation from cardiac MRI
author Lourenço, Ana Catarina Feliciano
author_facet Lourenço, Ana Catarina Feliciano
author_role author
dc.contributor.none.fl_str_mv Caetano, Gina Maria Costa
Varela, Marta
Repositório da Universidade de Lisboa
dc.contributor.author.fl_str_mv Lourenço, Ana Catarina Feliciano
dc.subject.por.fl_str_mv Fibrilhação Auricular
Enfarte do miocárdio
Ressonância Magnética Cardíaca
Deep learning
Classificação/ Segmentação
Teses de mestrado - 2021
Domínio/Área Científica::Engenharia e Tecnologia::Engenharia Médica
topic Fibrilhação Auricular
Enfarte do miocárdio
Ressonância Magnética Cardíaca
Deep learning
Classificação/ Segmentação
Teses de mestrado - 2021
Domínio/Área Científica::Engenharia e Tecnologia::Engenharia Médica
description Tese de mestrado integrado em Engenharia Biomédica e Biofísica (Engenharia Clínica e Instrumentação Médica), Universidade de Lisboa, Faculdade de Ciências, 2021
publishDate 2021
dc.date.none.fl_str_mv 2021-06-11T15:02:52Z
2021
2021
2021-01-01T00:00:00Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/masterThesis
format masterThesis
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10451/48465
TID:202933962
url http://hdl.handle.net/10451/48465
identifier_str_mv TID:202933962
dc.language.iso.fl_str_mv eng
language eng
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron:RCAAP
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron_str RCAAP
institution RCAAP
reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository.name.fl_str_mv Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
repository.mail.fl_str_mv
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