A complete benchmark for polyp detection, segmentation and classification in colonoscopy images

Colorectal cancer (CRC) is one of the main causes of deaths worldwide. Early detection and diagnosis of its precursor lesion, the polyp, is key to reduce its mortality and to improve procedure efficiency. During the last two decades, several computational methods have been proposed to assist clinici...

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Autores: Tudela, Yael, Majó, Mireia, de la Fuente, Neil, Galdran, Adrian, Krenzer, Adrian, Puppe, Frank, Yamlahi, Amine, Tran, Thuy Nuong, Matuszewski, Bogdan J., Fitzgerald, Kerr, Bian, Cheng, Pan, Junwen, Liu, Shijle, Fernández-Esparrach, Gloria|||0000-0002-3378-3940, Histace, Aymeric|||0000-0002-3029-4412, Bernal del Nozal, Jorge|||0000-0001-8493-9514
Formato: artículo
Fecha de publicación:2024
País:España
Recursos:Universitat Autònoma de Barcelona
Repositorio:Dipòsit Digital de Documents de la UAB
Idioma:inglés
OAI Identifier:oai:ddd.uab.cat:319970
Acesso em linha:https://ddd.uab.cat/record/319970
https://dx.doi.org/urn:doi:10.3389/fonc.2024.1417862
Access Level:acceso abierto
Palavra-chave:Computer-aided diagnosis
Medical imaging
Polyp classification
Polyp detection
Polyp segmentation
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spelling A complete benchmark for polyp detection, segmentation and classification in colonoscopy imagesTudela, YaelMajó, Mireiade la Fuente, NeilGaldran, AdrianKrenzer, AdrianPuppe, FrankYamlahi, AmineTran, Thuy NuongMatuszewski, Bogdan J.Fitzgerald, KerrBian, ChengPan, JunwenLiu, ShijleFernández-Esparrach, Gloria|||0000-0002-3378-3940Histace, Aymeric|||0000-0002-3029-4412Bernal del Nozal, Jorge|||0000-0001-8493-9514Computer-aided diagnosisMedical imagingPolyp classificationPolyp detectionPolyp segmentationColorectal cancer (CRC) is one of the main causes of deaths worldwide. Early detection and diagnosis of its precursor lesion, the polyp, is key to reduce its mortality and to improve procedure efficiency. During the last two decades, several computational methods have been proposed to assist clinicians in detection, segmentation and classification tasks but the lack of a common public validation framework makes it difficult to determine which of them is ready to be deployed in the exploration room. This study presents a complete validation framework and we compare several methodologies for each of the polyp characterization tasks. Results show that the majority of the approaches are able to provide good performance for the detection and segmentation task, but that there is room for improvement regarding polyp classification. While studied show promising results in the assistance of polyp detection and segmentation tasks, further research should be done in classification task to obtain reliable results to assist the clinicians during the procedure. The presented framework provides a standarized method for evaluating and comparing different approaches, which could facilitate the identification of clinically prepared assisting methods. 22024-01-0120242024-01-01Articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfhttps://ddd.uab.cat/record/319970https://dx.doi.org/urn:doi:10.3389/fonc.2024.1417862reponame:Dipòsit Digital de Documents de la UABinstname:Universitat Autònoma de BarcelonaInglésengAgencia Estatal de Investigación https://doi.org/10.13039/501100011033 PID2020-120311RB-I00European Commission https://doi.org/10.13039/501100000780 892297Ministerio de Ciencia e Innovación https://doi.org/10.13039/501100004837 RED2022-134964-Topen accesshttp://purl.org/coar/access_right/c_abf2Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, la comunicació pública de l'obra i la creació d'obres derivades, fins i tot amb finalitats comercials, sempre i quan es reconegui l'autoria de l'obra original.https://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:ddd.uab.cat:3199702026-06-06T12:50:31Z
dc.title.none.fl_str_mv A complete benchmark for polyp detection, segmentation and classification in colonoscopy images
title A complete benchmark for polyp detection, segmentation and classification in colonoscopy images
spellingShingle A complete benchmark for polyp detection, segmentation and classification in colonoscopy images
Tudela, Yael
Computer-aided diagnosis
Medical imaging
Polyp classification
Polyp detection
Polyp segmentation
title_short A complete benchmark for polyp detection, segmentation and classification in colonoscopy images
title_full A complete benchmark for polyp detection, segmentation and classification in colonoscopy images
title_fullStr A complete benchmark for polyp detection, segmentation and classification in colonoscopy images
title_full_unstemmed A complete benchmark for polyp detection, segmentation and classification in colonoscopy images
title_sort A complete benchmark for polyp detection, segmentation and classification in colonoscopy images
dc.creator.none.fl_str_mv Tudela, Yael
Majó, Mireia
de la Fuente, Neil
Galdran, Adrian
Krenzer, Adrian
Puppe, Frank
Yamlahi, Amine
Tran, Thuy Nuong
Matuszewski, Bogdan J.
Fitzgerald, Kerr
Bian, Cheng
Pan, Junwen
Liu, Shijle
Fernández-Esparrach, Gloria|||0000-0002-3378-3940
Histace, Aymeric|||0000-0002-3029-4412
Bernal del Nozal, Jorge|||0000-0001-8493-9514
author Tudela, Yael
author_facet Tudela, Yael
Majó, Mireia
de la Fuente, Neil
Galdran, Adrian
Krenzer, Adrian
Puppe, Frank
Yamlahi, Amine
Tran, Thuy Nuong
Matuszewski, Bogdan J.
Fitzgerald, Kerr
Bian, Cheng
Pan, Junwen
Liu, Shijle
Fernández-Esparrach, Gloria|||0000-0002-3378-3940
Histace, Aymeric|||0000-0002-3029-4412
Bernal del Nozal, Jorge|||0000-0001-8493-9514
author_role author
author2 Majó, Mireia
de la Fuente, Neil
Galdran, Adrian
Krenzer, Adrian
Puppe, Frank
Yamlahi, Amine
Tran, Thuy Nuong
Matuszewski, Bogdan J.
Fitzgerald, Kerr
Bian, Cheng
Pan, Junwen
Liu, Shijle
Fernández-Esparrach, Gloria|||0000-0002-3378-3940
Histace, Aymeric|||0000-0002-3029-4412
Bernal del Nozal, Jorge|||0000-0001-8493-9514
author2_role author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
dc.subject.none.fl_str_mv Computer-aided diagnosis
Medical imaging
Polyp classification
Polyp detection
Polyp segmentation
topic Computer-aided diagnosis
Medical imaging
Polyp classification
Polyp detection
Polyp segmentation
description Colorectal cancer (CRC) is one of the main causes of deaths worldwide. Early detection and diagnosis of its precursor lesion, the polyp, is key to reduce its mortality and to improve procedure efficiency. During the last two decades, several computational methods have been proposed to assist clinicians in detection, segmentation and classification tasks but the lack of a common public validation framework makes it difficult to determine which of them is ready to be deployed in the exploration room. This study presents a complete validation framework and we compare several methodologies for each of the polyp characterization tasks. Results show that the majority of the approaches are able to provide good performance for the detection and segmentation task, but that there is room for improvement regarding polyp classification. While studied show promising results in the assistance of polyp detection and segmentation tasks, further research should be done in classification task to obtain reliable results to assist the clinicians during the procedure. The presented framework provides a standarized method for evaluating and comparing different approaches, which could facilitate the identification of clinically prepared assisting methods.
publishDate 2024
dc.date.none.fl_str_mv 2
2024-01-01
2024
2024-01-01
dc.type.none.fl_str_mv Article
http://purl.org/coar/resource_type/c_6501
VoR
http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://ddd.uab.cat/record/319970
https://dx.doi.org/urn:doi:10.3389/fonc.2024.1417862
url https://ddd.uab.cat/record/319970
https://dx.doi.org/urn:doi:10.3389/fonc.2024.1417862
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.relation.none.fl_str_mv Agencia Estatal de Investigación https://doi.org/10.13039/501100011033 PID2020-120311RB-I00
European Commission https://doi.org/10.13039/501100000780 892297
Ministerio de Ciencia e Innovación https://doi.org/10.13039/501100004837 RED2022-134964-T
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
https://creativecommons.org/licenses/by/4.0/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
https://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:Dipòsit Digital de Documents de la UAB
instname:Universitat Autònoma de Barcelona
instname_str Universitat Autònoma de Barcelona
reponame_str Dipòsit Digital de Documents de la UAB
collection Dipòsit Digital de Documents de la UAB
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,198674

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