White Matter, Gray Matter and Cerebrospinal Fluid Segmentation from Brain 3D MRI Using B-UNET
Autor(a) principal: | |
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Data de Publicação: | 2019 |
Outros Autores: | , , |
Tipo de documento: | Livro |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | https://hdl.handle.net/10216/124729 |
Resumo: | The accurate segmentation of brain tissues in Magnetic Resonance (MR) images is an important step for detection and treatment planning of brain diseases. Among other brain tissues, Gray Matter, White Matter and Cerebrospinal Fluid are commonly segmented for Alzheimer diagnosis purpose. Therefore, different algorithms for segmenting these tissues in MR image scans have been proposed over the years. Nowadays, with the trend of deep learning, many methods are trained to learn important features and extract information from the data leading to very promising segmentation results. In this work, we propose an effective approach to segment three tissues in 3D Brain MR images based on B-UNET. The method is implemented by using the Bitplane method in each convolution of the UNET model. We evaluated the proposed method using two public databases with very promising results. (c) Springer Nature Switzerland AG 2019. |
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White Matter, Gray Matter and Cerebrospinal Fluid Segmentation from Brain 3D MRI Using B-UNETCiências Tecnológicas, Ciências médicas e da saúdeTechnological sciences, Medical and Health sciencesThe accurate segmentation of brain tissues in Magnetic Resonance (MR) images is an important step for detection and treatment planning of brain diseases. Among other brain tissues, Gray Matter, White Matter and Cerebrospinal Fluid are commonly segmented for Alzheimer diagnosis purpose. Therefore, different algorithms for segmenting these tissues in MR image scans have been proposed over the years. Nowadays, with the trend of deep learning, many methods are trained to learn important features and extract information from the data leading to very promising segmentation results. In this work, we propose an effective approach to segment three tissues in 3D Brain MR images based on B-UNET. The method is implemented by using the Bitplane method in each convolution of the UNET model. We evaluated the proposed method using two public databases with very promising results. (c) Springer Nature Switzerland AG 2019.2019-102019-10-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/bookapplication/pdfhttps://hdl.handle.net/10216/124729eng10.1007/978-3-030-32040-9_20Tran Anh TuanPham The BaoJin Young KimJoão Manuel R. S. Tavaresinfo: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:RCAAP2023-11-29T13:49:07Zoai:repositorio-aberto.up.pt:10216/124729Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T23:48:28.255516Repositório 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 |
White Matter, Gray Matter and Cerebrospinal Fluid Segmentation from Brain 3D MRI Using B-UNET |
title |
White Matter, Gray Matter and Cerebrospinal Fluid Segmentation from Brain 3D MRI Using B-UNET |
spellingShingle |
White Matter, Gray Matter and Cerebrospinal Fluid Segmentation from Brain 3D MRI Using B-UNET Tran Anh Tuan Ciências Tecnológicas, Ciências médicas e da saúde Technological sciences, Medical and Health sciences |
title_short |
White Matter, Gray Matter and Cerebrospinal Fluid Segmentation from Brain 3D MRI Using B-UNET |
title_full |
White Matter, Gray Matter and Cerebrospinal Fluid Segmentation from Brain 3D MRI Using B-UNET |
title_fullStr |
White Matter, Gray Matter and Cerebrospinal Fluid Segmentation from Brain 3D MRI Using B-UNET |
title_full_unstemmed |
White Matter, Gray Matter and Cerebrospinal Fluid Segmentation from Brain 3D MRI Using B-UNET |
title_sort |
White Matter, Gray Matter and Cerebrospinal Fluid Segmentation from Brain 3D MRI Using B-UNET |
author |
Tran Anh Tuan |
author_facet |
Tran Anh Tuan Pham The Bao Jin Young Kim João Manuel R. S. Tavares |
author_role |
author |
author2 |
Pham The Bao Jin Young Kim João Manuel R. S. Tavares |
author2_role |
author author author |
dc.contributor.author.fl_str_mv |
Tran Anh Tuan Pham The Bao Jin Young Kim João Manuel R. S. Tavares |
dc.subject.por.fl_str_mv |
Ciências Tecnológicas, Ciências médicas e da saúde Technological sciences, Medical and Health sciences |
topic |
Ciências Tecnológicas, Ciências médicas e da saúde Technological sciences, Medical and Health sciences |
description |
The accurate segmentation of brain tissues in Magnetic Resonance (MR) images is an important step for detection and treatment planning of brain diseases. Among other brain tissues, Gray Matter, White Matter and Cerebrospinal Fluid are commonly segmented for Alzheimer diagnosis purpose. Therefore, different algorithms for segmenting these tissues in MR image scans have been proposed over the years. Nowadays, with the trend of deep learning, many methods are trained to learn important features and extract information from the data leading to very promising segmentation results. In this work, we propose an effective approach to segment three tissues in 3D Brain MR images based on B-UNET. The method is implemented by using the Bitplane method in each convolution of the UNET model. We evaluated the proposed method using two public databases with very promising results. (c) Springer Nature Switzerland AG 2019. |
publishDate |
2019 |
dc.date.none.fl_str_mv |
2019-10 2019-10-01T00:00:00Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/book |
format |
book |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
https://hdl.handle.net/10216/124729 |
url |
https://hdl.handle.net/10216/124729 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.1007/978-3-030-32040-9_20 |
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 |
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1799135803337605120 |