Kidney segmentation in ultrasound, magnetic resonance and computed tomography images: A systematic review
Autor(a) principal: | |
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Data de Publicação: | 2018 |
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/11110/1413 |
Resumo: | Background and objective: Segmentation is an essential step in computer-aided diagnosis and treatment planning of kidney diseases. In recent years, several researchers proposed multiple techniques to segment the kidney in medical images from distinct imaging acquisition systems, namely ultrasound, magnetic resonance, and computed tomography. This article aims to present a systematic review of the different methodologies developed for kidney segmentation. Methods: With this work, it is intended to analyze and categorize the different kidney segmentation algo- rithms, establishing a comparison between them and discussing the most appropriate methods for each modality. For that, articles published between 2010 and 2016 were analyzed. The search was performed in Scopus and Web of Science using the expressions “kidney segmentation” and “renal segmentation”. Results: A total of 1528 articles were retrieved from the databases, and 95 articles were selected for this review. After analysis of the selected articles, the reviewed segmentation techniques were categorized according to their theoretical approach. Conclusions: Based on the performed analysis, it was possible to identify segmentation approaches based on distinct image processing classes that can be used to accurately segment the kidney in images of different imaging modalities. Nevertheless, further research on kidney segmentation must be conducted to overcome the current drawbacks of the state-of-the-art methods. Moreover, a standardization of the evaluation database and metrics is needed to allow a direct comparison between methods. |
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Kidney segmentation in ultrasound, magnetic resonance and computed tomography images: A systematic reviewComputed tomographyKidney segmentationMagnetic resonance imagingSystematic reviewUltrasound imagingBackground and objective: Segmentation is an essential step in computer-aided diagnosis and treatment planning of kidney diseases. In recent years, several researchers proposed multiple techniques to segment the kidney in medical images from distinct imaging acquisition systems, namely ultrasound, magnetic resonance, and computed tomography. This article aims to present a systematic review of the different methodologies developed for kidney segmentation. Methods: With this work, it is intended to analyze and categorize the different kidney segmentation algo- rithms, establishing a comparison between them and discussing the most appropriate methods for each modality. For that, articles published between 2010 and 2016 were analyzed. The search was performed in Scopus and Web of Science using the expressions “kidney segmentation” and “renal segmentation”. Results: A total of 1528 articles were retrieved from the databases, and 95 articles were selected for this review. After analysis of the selected articles, the reviewed segmentation techniques were categorized according to their theoretical approach. Conclusions: Based on the performed analysis, it was possible to identify segmentation approaches based on distinct image processing classes that can be used to accurately segment the kidney in images of different imaging modalities. Nevertheless, further research on kidney segmentation must be conducted to overcome the current drawbacks of the state-of-the-art methods. Moreover, a standardization of the evaluation database and metrics is needed to allow a direct comparison between methods.This work was funded by projects NORTE-01-0145-FEDER- 000013, and NORTE-01-0145-FEDER-024300, supported by North- ern Portugal Regional Operational Programme (Norte2020), un- der the Portugal 2020 Partnership Agreement, through the Euro- pean Regional Development Fund (FEDER), and also been funded by FEDER funds, through Competitiveness Factors Operational Pro- gramme (COMPETE), and by national funds, through the FCT—Fundação para a Ciência e Tecnologia, under the scope of the project POCI-01-0145-FEDER-007038. The authors acknowledge FCT—Fundação para a Ciência e a Tecnologia, Portugal, and the European Social Found, European Union, for funding support through the Programa Operacional Capital Humano (POCH) in the scope of the PhD grants SFRH/BD/93443/2013 (S. Queirós) and SFRH/BD/95438/2013 (P. Morais).Computer Methods and Programs in Biomedicine2018-09-12T13:45:36Z2018-01-10T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://hdl.handle.net/11110/1413oai:ciencipca.ipca.pt:11110/1413eng0169-2607https://doi.org/DOI: https://doi.org/10.1016/j.cmpb.2018.01.014http://hdl.handle.net/11110/1413metadata only accessinfo:eu-repo/semantics/openAccessTorres, HelenaQueirós, SandroMorais, PedroOliveira, BrunoFonseca, JaimeVilaça, Joãoreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2022-09-05T12:52:51Zoai:ciencipca.ipca.pt:11110/1413Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T15:01:47.203777Repositório 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 |
Kidney segmentation in ultrasound, magnetic resonance and computed tomography images: A systematic review |
title |
Kidney segmentation in ultrasound, magnetic resonance and computed tomography images: A systematic review |
spellingShingle |
Kidney segmentation in ultrasound, magnetic resonance and computed tomography images: A systematic review Torres, Helena Computed tomography Kidney segmentation Magnetic resonance imaging Systematic review Ultrasound imaging |
title_short |
Kidney segmentation in ultrasound, magnetic resonance and computed tomography images: A systematic review |
title_full |
Kidney segmentation in ultrasound, magnetic resonance and computed tomography images: A systematic review |
title_fullStr |
Kidney segmentation in ultrasound, magnetic resonance and computed tomography images: A systematic review |
title_full_unstemmed |
Kidney segmentation in ultrasound, magnetic resonance and computed tomography images: A systematic review |
title_sort |
Kidney segmentation in ultrasound, magnetic resonance and computed tomography images: A systematic review |
author |
Torres, Helena |
author_facet |
Torres, Helena Queirós, Sandro Morais, Pedro Oliveira, Bruno Fonseca, Jaime Vilaça, João |
author_role |
author |
author2 |
Queirós, Sandro Morais, Pedro Oliveira, Bruno Fonseca, Jaime Vilaça, João |
author2_role |
author author author author author |
dc.contributor.author.fl_str_mv |
Torres, Helena Queirós, Sandro Morais, Pedro Oliveira, Bruno Fonseca, Jaime Vilaça, João |
dc.subject.por.fl_str_mv |
Computed tomography Kidney segmentation Magnetic resonance imaging Systematic review Ultrasound imaging |
topic |
Computed tomography Kidney segmentation Magnetic resonance imaging Systematic review Ultrasound imaging |
description |
Background and objective: Segmentation is an essential step in computer-aided diagnosis and treatment planning of kidney diseases. In recent years, several researchers proposed multiple techniques to segment the kidney in medical images from distinct imaging acquisition systems, namely ultrasound, magnetic resonance, and computed tomography. This article aims to present a systematic review of the different methodologies developed for kidney segmentation. Methods: With this work, it is intended to analyze and categorize the different kidney segmentation algo- rithms, establishing a comparison between them and discussing the most appropriate methods for each modality. For that, articles published between 2010 and 2016 were analyzed. The search was performed in Scopus and Web of Science using the expressions “kidney segmentation” and “renal segmentation”. Results: A total of 1528 articles were retrieved from the databases, and 95 articles were selected for this review. After analysis of the selected articles, the reviewed segmentation techniques were categorized according to their theoretical approach. Conclusions: Based on the performed analysis, it was possible to identify segmentation approaches based on distinct image processing classes that can be used to accurately segment the kidney in images of different imaging modalities. Nevertheless, further research on kidney segmentation must be conducted to overcome the current drawbacks of the state-of-the-art methods. Moreover, a standardization of the evaluation database and metrics is needed to allow a direct comparison between methods. |
publishDate |
2018 |
dc.date.none.fl_str_mv |
2018-09-12T13:45:36Z 2018-01-10T00:00:00Z |
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/11110/1413 oai:ciencipca.ipca.pt:11110/1413 |
url |
http://hdl.handle.net/11110/1413 |
identifier_str_mv |
oai:ciencipca.ipca.pt:11110/1413 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
0169-2607 https://doi.org/DOI: https://doi.org/10.1016/j.cmpb.2018.01.014 http://hdl.handle.net/11110/1413 |
dc.rights.driver.fl_str_mv |
metadata only access info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
metadata only access |
eu_rights_str_mv |
openAccess |
dc.publisher.none.fl_str_mv |
Computer Methods and Programs in Biomedicine |
publisher.none.fl_str_mv |
Computer Methods and Programs in Biomedicine |
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 |
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Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
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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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1799129887464751104 |