Automated segmentation of normal and diseased coronary arteries – The ASOCA challenge
Autor(a) principal: | |
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Data de Publicação: | 2022 |
Outros Autores: | , , , , , , , , , , , , , , , , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/11110/2546 |
Resumo: | Cardiovascular disease is a major cause of death worldwide. Computed Tomography Coronary Angiography (CTCA) is a non-invasive method used to evaluate coronary artery disease, as well as evaluating and reconstructing heart and coronary vessel structures. Reconstructed models have a wide array of for educational, training and research applications such as the study of diseased and non-diseased coronary anatomy, machine learning based disease risk prediction and in-silico and in-vitro testing of medical devices. However, coronary arteries are difficult to image due to their small size, location, and movement, causing poor resolution and artefacts. Segmentation of coronary arteries has traditionally focused on semi-automatic methods where a human expert guides the algorithm and corrects errors, which severely limits large-scale applications and integration within clinical systems. International challenges aiming to overcome this barrier have focussed on specific tasks such as centreline extraction, stenosis quantification, and segmentation of specific artery segments only. Here we present the results of the first challenge to develop fully automatic segmentation methods of full coronary artery trees and establish the first large standardized dataset of normal and diseased arteries. This forms a new automated segmentation benchmark allowing the automated processing of CTCAs directly relevant for large-scale and personalized clinical applications. |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Automated segmentation of normal and diseased coronary arteries – The ASOCA challengeCoronary arteriesImage segmentationMachine learningCardiovascular disease is a major cause of death worldwide. Computed Tomography Coronary Angiography (CTCA) is a non-invasive method used to evaluate coronary artery disease, as well as evaluating and reconstructing heart and coronary vessel structures. Reconstructed models have a wide array of for educational, training and research applications such as the study of diseased and non-diseased coronary anatomy, machine learning based disease risk prediction and in-silico and in-vitro testing of medical devices. However, coronary arteries are difficult to image due to their small size, location, and movement, causing poor resolution and artefacts. Segmentation of coronary arteries has traditionally focused on semi-automatic methods where a human expert guides the algorithm and corrects errors, which severely limits large-scale applications and integration within clinical systems. International challenges aiming to overcome this barrier have focussed on specific tasks such as centreline extraction, stenosis quantification, and segmentation of specific artery segments only. Here we present the results of the first challenge to develop fully automatic segmentation methods of full coronary artery trees and establish the first large standardized dataset of normal and diseased arteries. This forms a new automated segmentation benchmark allowing the automated processing of CTCAs directly relevant for large-scale and personalized clinical applications.Computerized Medical Image and Graphics2023-01-17T11:51:46Z2023-01-172022-02-18T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://hdl.handle.net/11110/2546http://hdl.handle.net/11110/2546engGharleghi, RamtinAdikari, DonaEllenberger, KatyOoi, Sze-YuanEllis, ChrisChen, Chung-MingGao Ruochen Gao, RuochenHe, YutingHussain, RaabidLee, Chia-YenLi, JunMa, JunNie, ZiweiOliveira, BrunoQi, YaoleiSkandarani, YoussefVilaça, João L.Wang, XiyueYang, SenSowmya, ArcotBeier, Susanninfo: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:RCAAP2023-01-19T04:16:06Zoai:ciencipca.ipca.pt:11110/2546Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T16:45:19.095508Repositório 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 |
Automated segmentation of normal and diseased coronary arteries – The ASOCA challenge |
title |
Automated segmentation of normal and diseased coronary arteries – The ASOCA challenge |
spellingShingle |
Automated segmentation of normal and diseased coronary arteries – The ASOCA challenge Gharleghi, Ramtin Coronary arteries Image segmentation Machine learning |
title_short |
Automated segmentation of normal and diseased coronary arteries – The ASOCA challenge |
title_full |
Automated segmentation of normal and diseased coronary arteries – The ASOCA challenge |
title_fullStr |
Automated segmentation of normal and diseased coronary arteries – The ASOCA challenge |
title_full_unstemmed |
Automated segmentation of normal and diseased coronary arteries – The ASOCA challenge |
title_sort |
Automated segmentation of normal and diseased coronary arteries – The ASOCA challenge |
author |
Gharleghi, Ramtin |
author_facet |
Gharleghi, Ramtin Adikari, Dona Ellenberger, Katy Ooi, Sze-Yuan Ellis, Chris Chen, Chung-Ming Gao Ruochen Gao, Ruochen He, Yuting Hussain, Raabid Lee, Chia-Yen Li, Jun Ma, Jun Nie, Ziwei Oliveira, Bruno Qi, Yaolei Skandarani, Youssef Vilaça, João L. Wang, Xiyue Yang, Sen Sowmya, Arcot Beier, Susann |
author_role |
author |
author2 |
Adikari, Dona Ellenberger, Katy Ooi, Sze-Yuan Ellis, Chris Chen, Chung-Ming Gao Ruochen Gao, Ruochen He, Yuting Hussain, Raabid Lee, Chia-Yen Li, Jun Ma, Jun Nie, Ziwei Oliveira, Bruno Qi, Yaolei Skandarani, Youssef Vilaça, João L. Wang, Xiyue Yang, Sen Sowmya, Arcot Beier, Susann |
author2_role |
author author author author author author author author author author author author author author author author author author author author |
dc.contributor.author.fl_str_mv |
Gharleghi, Ramtin Adikari, Dona Ellenberger, Katy Ooi, Sze-Yuan Ellis, Chris Chen, Chung-Ming Gao Ruochen Gao, Ruochen He, Yuting Hussain, Raabid Lee, Chia-Yen Li, Jun Ma, Jun Nie, Ziwei Oliveira, Bruno Qi, Yaolei Skandarani, Youssef Vilaça, João L. Wang, Xiyue Yang, Sen Sowmya, Arcot Beier, Susann |
dc.subject.por.fl_str_mv |
Coronary arteries Image segmentation Machine learning |
topic |
Coronary arteries Image segmentation Machine learning |
description |
Cardiovascular disease is a major cause of death worldwide. Computed Tomography Coronary Angiography (CTCA) is a non-invasive method used to evaluate coronary artery disease, as well as evaluating and reconstructing heart and coronary vessel structures. Reconstructed models have a wide array of for educational, training and research applications such as the study of diseased and non-diseased coronary anatomy, machine learning based disease risk prediction and in-silico and in-vitro testing of medical devices. However, coronary arteries are difficult to image due to their small size, location, and movement, causing poor resolution and artefacts. Segmentation of coronary arteries has traditionally focused on semi-automatic methods where a human expert guides the algorithm and corrects errors, which severely limits large-scale applications and integration within clinical systems. International challenges aiming to overcome this barrier have focussed on specific tasks such as centreline extraction, stenosis quantification, and segmentation of specific artery segments only. Here we present the results of the first challenge to develop fully automatic segmentation methods of full coronary artery trees and establish the first large standardized dataset of normal and diseased arteries. This forms a new automated segmentation benchmark allowing the automated processing of CTCAs directly relevant for large-scale and personalized clinical applications. |
publishDate |
2022 |
dc.date.none.fl_str_mv |
2022-02-18T00:00:00Z 2023-01-17T11:51:46Z 2023-01-17 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/11110/2546 http://hdl.handle.net/11110/2546 |
url |
http://hdl.handle.net/11110/2546 |
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.publisher.none.fl_str_mv |
Computerized Medical Image and Graphics |
publisher.none.fl_str_mv |
Computerized Medical Image and Graphics |
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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1799130924903825408 |