Segmentation and three-dimensional reconstruction of lesions using the automated breast volume scanner (ABVS)
Autor(a) principal: | |
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Data de Publicação: | 2017 |
Outros Autores: | , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://repositorio.inesctec.pt/handle/123456789/6633 http://dx.doi.org/10.1002/rcs.1767 |
Resumo: | BackgroundUltrasound is an effective tool for breast cancer diagnosis. However, its relatively low image quality makes small lesion analysis challenging. This promotes the development of tools to help clinicians in the diagnosis. MethodsWe propose a method for segmentation and three-dimensional (3D) reconstruction of lesions from ultrasound images acquired using the automated breast volume scanner (ABVS). Segmentation and reconstruction algorithms are applied to obtain the lesion's 3D geometry. A total of 140 artificial lesions with different sizes and shapes are reconstructed in gelatin-based phantoms and biological tissue. Dice similarity coefficient (DSC) is used to evaluate the reconstructed shapes. The algorithm is tested using a human breast phantom and clinical data from six patients. ResultsDSC values are 0.860.06 and 0.86 +/- 0.05 for gelatin-based phantoms and biological tissue, respectively. The results are validated by a specialized clinician. ConclusionsEvaluation metrics show that the algorithm accurately segments and reconstructs various lesions. Copyright (c) 2016 John Wiley & Sons, Ltd. |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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7160 |
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Segmentation and three-dimensional reconstruction of lesions using the automated breast volume scanner (ABVS)BackgroundUltrasound is an effective tool for breast cancer diagnosis. However, its relatively low image quality makes small lesion analysis challenging. This promotes the development of tools to help clinicians in the diagnosis. MethodsWe propose a method for segmentation and three-dimensional (3D) reconstruction of lesions from ultrasound images acquired using the automated breast volume scanner (ABVS). Segmentation and reconstruction algorithms are applied to obtain the lesion's 3D geometry. A total of 140 artificial lesions with different sizes and shapes are reconstructed in gelatin-based phantoms and biological tissue. Dice similarity coefficient (DSC) is used to evaluate the reconstructed shapes. The algorithm is tested using a human breast phantom and clinical data from six patients. ResultsDSC values are 0.860.06 and 0.86 +/- 0.05 for gelatin-based phantoms and biological tissue, respectively. The results are validated by a specialized clinician. ConclusionsEvaluation metrics show that the algorithm accurately segments and reconstructs various lesions. Copyright (c) 2016 John Wiley & Sons, Ltd.2018-01-17T11:00:49Z2017-01-01T00:00:00Z2017info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://repositorio.inesctec.pt/handle/123456789/6633http://dx.doi.org/10.1002/rcs.1767engTeresa Finisterra AraújoAbayazid,MRutten,MJCMMisra,Sinfo:eu-repo/semantics/embargoedAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-05-15T10:20:28Zoai:repositorio.inesctec.pt:123456789/6633Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T17:53:09.512122Repositório 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 |
Segmentation and three-dimensional reconstruction of lesions using the automated breast volume scanner (ABVS) |
title |
Segmentation and three-dimensional reconstruction of lesions using the automated breast volume scanner (ABVS) |
spellingShingle |
Segmentation and three-dimensional reconstruction of lesions using the automated breast volume scanner (ABVS) Teresa Finisterra Araújo |
title_short |
Segmentation and three-dimensional reconstruction of lesions using the automated breast volume scanner (ABVS) |
title_full |
Segmentation and three-dimensional reconstruction of lesions using the automated breast volume scanner (ABVS) |
title_fullStr |
Segmentation and three-dimensional reconstruction of lesions using the automated breast volume scanner (ABVS) |
title_full_unstemmed |
Segmentation and three-dimensional reconstruction of lesions using the automated breast volume scanner (ABVS) |
title_sort |
Segmentation and three-dimensional reconstruction of lesions using the automated breast volume scanner (ABVS) |
author |
Teresa Finisterra Araújo |
author_facet |
Teresa Finisterra Araújo Abayazid,M Rutten,MJCM Misra,S |
author_role |
author |
author2 |
Abayazid,M Rutten,MJCM Misra,S |
author2_role |
author author author |
dc.contributor.author.fl_str_mv |
Teresa Finisterra Araújo Abayazid,M Rutten,MJCM Misra,S |
description |
BackgroundUltrasound is an effective tool for breast cancer diagnosis. However, its relatively low image quality makes small lesion analysis challenging. This promotes the development of tools to help clinicians in the diagnosis. MethodsWe propose a method for segmentation and three-dimensional (3D) reconstruction of lesions from ultrasound images acquired using the automated breast volume scanner (ABVS). Segmentation and reconstruction algorithms are applied to obtain the lesion's 3D geometry. A total of 140 artificial lesions with different sizes and shapes are reconstructed in gelatin-based phantoms and biological tissue. Dice similarity coefficient (DSC) is used to evaluate the reconstructed shapes. The algorithm is tested using a human breast phantom and clinical data from six patients. ResultsDSC values are 0.860.06 and 0.86 +/- 0.05 for gelatin-based phantoms and biological tissue, respectively. The results are validated by a specialized clinician. ConclusionsEvaluation metrics show that the algorithm accurately segments and reconstructs various lesions. Copyright (c) 2016 John Wiley & Sons, Ltd. |
publishDate |
2017 |
dc.date.none.fl_str_mv |
2017-01-01T00:00:00Z 2017 2018-01-17T11:00:49Z |
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://repositorio.inesctec.pt/handle/123456789/6633 http://dx.doi.org/10.1002/rcs.1767 |
url |
http://repositorio.inesctec.pt/handle/123456789/6633 http://dx.doi.org/10.1002/rcs.1767 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/embargoedAccess |
eu_rights_str_mv |
embargoedAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.source.none.fl_str_mv |
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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) |
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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 |
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1799131606191964160 |