Comparative Validation of Polyp Detection Methods in Video Colonoscopy: Results from the MICCAI 2015 Endoscopic Vision Challenge

Colonoscopy is the gold standard for colon cancer screening though some polyps are still missed, thus preventing early disease detection and treatment. Several computational systems have been proposed to assist polyp detection during colonoscopy but so far without consistent evaluation. The lack of...

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Autores: Bernal, Jorge, Tajbakhsh, Nima, Sánchez, F. Javier, Matuszewski, Bogdan J., Chen, Hao, Yu, Lequan, Angermann, Quentin, Romain, Olivier, Rustad, Bjorn, Balasingham, Ilangko, Pogorelov, Konstantin, Choi, Sungbin, Debard, Quentin, Maier-Hein, Lena, Speidel, Stefanie, Stoyanov, Danail, Brandao, Patrick, Cordova, Henry, Sánchez Montes, Cristina, Gurudu, Suryakanth R., Fernández Esparrach, Glòria, Dray, Xavier, Liang, Jianming, Histace, Aymeric
Formato: artículo
Estado:Versión aceptada para publicación
Fecha de publicación:2017
País:España
Recursos:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
Repositorio:Recercat. Dipósit de la Recerca de Catalunya
OAI Identifier:oai:recercat.cat:2445/123294
Acesso em linha:https://hdl.handle.net/2445/123294
Access Level:acceso abierto
Palavra-chave:Colonoscòpia
Càncer colorectal
Endoscòpia
Colonoscopy
Colorectal cancer
Endoscopy
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spelling Comparative Validation of Polyp Detection Methods in Video Colonoscopy: Results from the MICCAI 2015 Endoscopic Vision ChallengeBernal, JorgeTajbakhsh, NimaSánchez, F. JavierMatuszewski, Bogdan J.Chen, HaoYu, LequanAngermann, QuentinRomain, OlivierRustad, BjornBalasingham, IlangkoPogorelov, KonstantinChoi, SungbinDebard, QuentinMaier-Hein, LenaSpeidel, StefanieStoyanov, DanailBrandao, PatrickCordova, HenrySánchez Montes, CristinaGurudu, Suryakanth R.Fernández Esparrach, GlòriaDray, XavierLiang, JianmingHistace, AymericColonoscòpiaCàncer colorectalEndoscòpiaColonoscopyColorectal cancerEndoscopyColonoscopy is the gold standard for colon cancer screening though some polyps are still missed, thus preventing early disease detection and treatment. Several computational systems have been proposed to assist polyp detection during colonoscopy but so far without consistent evaluation. The lack of publicly available annotated databases has made it difficult to compare methods and to assess if they achieve performance levels acceptable for clinical use. The Automatic Polyp Detection sub-challenge, conducted as part of the Endoscopic Vision Challenge (http://endovis.grand-challenge.org) at the international conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) in 2015, was an effort to address this need. In this paper, we report the results of this comparative evaluation of polyp detection methods, as well as describe additional experiments to further explore differences between methods. We define performance metrics and provide evaluation databases that allow comparison of multiple methodologies. Results show that convolutional neural networks are the state of the art. Nevertheless, it is also demonstrated that combining different methodologies can lead to an improved overall performance.Institute of Electrical and Electronics Engineers (IEEE)2018201820172018info:eu-repo/semantics/articleinfo:eu-repo/semantics/acceptedVersion18 p.application/pdfapplication/pdfhttps://hdl.handle.net/2445/123294Articles publicats en revistes (Medicina)reponame:Recercat. Dipósit de la Recerca de Catalunyainstname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)InglésVersió postprint del document publicat a: https://doi.org/10.1109/TMI.2017.2664042IEEE Transactions on Medical Imaging, 2017, vol. 36, num. 6, p. 1231-1249https://doi.org/10.1109/TMI.2017.2664042(c) Institute of Electrical and Electronics Engineers (IEEE), 2017info:eu-repo/semantics/openAccessoai:recercat.cat:2445/1232942026-05-29T05:05:01Z
dc.title.none.fl_str_mv Comparative Validation of Polyp Detection Methods in Video Colonoscopy: Results from the MICCAI 2015 Endoscopic Vision Challenge
title Comparative Validation of Polyp Detection Methods in Video Colonoscopy: Results from the MICCAI 2015 Endoscopic Vision Challenge
spellingShingle Comparative Validation of Polyp Detection Methods in Video Colonoscopy: Results from the MICCAI 2015 Endoscopic Vision Challenge
Bernal, Jorge
Colonoscòpia
Càncer colorectal
Endoscòpia
Colonoscopy
Colorectal cancer
Endoscopy
title_short Comparative Validation of Polyp Detection Methods in Video Colonoscopy: Results from the MICCAI 2015 Endoscopic Vision Challenge
title_full Comparative Validation of Polyp Detection Methods in Video Colonoscopy: Results from the MICCAI 2015 Endoscopic Vision Challenge
title_fullStr Comparative Validation of Polyp Detection Methods in Video Colonoscopy: Results from the MICCAI 2015 Endoscopic Vision Challenge
title_full_unstemmed Comparative Validation of Polyp Detection Methods in Video Colonoscopy: Results from the MICCAI 2015 Endoscopic Vision Challenge
title_sort Comparative Validation of Polyp Detection Methods in Video Colonoscopy: Results from the MICCAI 2015 Endoscopic Vision Challenge
dc.creator.none.fl_str_mv Bernal, Jorge
Tajbakhsh, Nima
Sánchez, F. Javier
Matuszewski, Bogdan J.
Chen, Hao
Yu, Lequan
Angermann, Quentin
Romain, Olivier
Rustad, Bjorn
Balasingham, Ilangko
Pogorelov, Konstantin
Choi, Sungbin
Debard, Quentin
Maier-Hein, Lena
Speidel, Stefanie
Stoyanov, Danail
Brandao, Patrick
Cordova, Henry
Sánchez Montes, Cristina
Gurudu, Suryakanth R.
Fernández Esparrach, Glòria
Dray, Xavier
Liang, Jianming
Histace, Aymeric
author Bernal, Jorge
author_facet Bernal, Jorge
Tajbakhsh, Nima
Sánchez, F. Javier
Matuszewski, Bogdan J.
Chen, Hao
Yu, Lequan
Angermann, Quentin
Romain, Olivier
Rustad, Bjorn
Balasingham, Ilangko
Pogorelov, Konstantin
Choi, Sungbin
Debard, Quentin
Maier-Hein, Lena
Speidel, Stefanie
Stoyanov, Danail
Brandao, Patrick
Cordova, Henry
Sánchez Montes, Cristina
Gurudu, Suryakanth R.
Fernández Esparrach, Glòria
Dray, Xavier
Liang, Jianming
Histace, Aymeric
author_role author
author2 Tajbakhsh, Nima
Sánchez, F. Javier
Matuszewski, Bogdan J.
Chen, Hao
Yu, Lequan
Angermann, Quentin
Romain, Olivier
Rustad, Bjorn
Balasingham, Ilangko
Pogorelov, Konstantin
Choi, Sungbin
Debard, Quentin
Maier-Hein, Lena
Speidel, Stefanie
Stoyanov, Danail
Brandao, Patrick
Cordova, Henry
Sánchez Montes, Cristina
Gurudu, Suryakanth R.
Fernández Esparrach, Glòria
Dray, Xavier
Liang, Jianming
Histace, Aymeric
author2_role author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
dc.subject.none.fl_str_mv Colonoscòpia
Càncer colorectal
Endoscòpia
Colonoscopy
Colorectal cancer
Endoscopy
topic Colonoscòpia
Càncer colorectal
Endoscòpia
Colonoscopy
Colorectal cancer
Endoscopy
description Colonoscopy is the gold standard for colon cancer screening though some polyps are still missed, thus preventing early disease detection and treatment. Several computational systems have been proposed to assist polyp detection during colonoscopy but so far without consistent evaluation. The lack of publicly available annotated databases has made it difficult to compare methods and to assess if they achieve performance levels acceptable for clinical use. The Automatic Polyp Detection sub-challenge, conducted as part of the Endoscopic Vision Challenge (http://endovis.grand-challenge.org) at the international conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) in 2015, was an effort to address this need. In this paper, we report the results of this comparative evaluation of polyp detection methods, as well as describe additional experiments to further explore differences between methods. We define performance metrics and provide evaluation databases that allow comparison of multiple methodologies. Results show that convolutional neural networks are the state of the art. Nevertheless, it is also demonstrated that combining different methodologies can lead to an improved overall performance.
publishDate 2017
dc.date.none.fl_str_mv 2017
2018
2018
2018
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/acceptedVersion
format article
status_str acceptedVersion
dc.identifier.none.fl_str_mv https://hdl.handle.net/2445/123294
url https://hdl.handle.net/2445/123294
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Versió postprint del document publicat a: https://doi.org/10.1109/TMI.2017.2664042
IEEE Transactions on Medical Imaging, 2017, vol. 36, num. 6, p. 1231-1249
https://doi.org/10.1109/TMI.2017.2664042
dc.rights.none.fl_str_mv (c) Institute of Electrical and Electronics Engineers (IEEE), 2017
info:eu-repo/semantics/openAccess
rights_invalid_str_mv (c) Institute of Electrical and Electronics Engineers (IEEE), 2017
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 18 p.
application/pdf
application/pdf
dc.publisher.none.fl_str_mv Institute of Electrical and Electronics Engineers (IEEE)
publisher.none.fl_str_mv Institute of Electrical and Electronics Engineers (IEEE)
dc.source.none.fl_str_mv Articles publicats en revistes (Medicina)
reponame:Recercat. Dipósit de la Recerca de Catalunya
instname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
instname_str Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
reponame_str Recercat. Dipósit de la Recerca de Catalunya
collection Recercat. Dipósit de la Recerca de Catalunya
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15.812429

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