Polyp Segmentation with Fully Convolutional Deep Neural Networks-Extended Evaluation Study

Analysis of colonoscopy images plays a significant role in early detection of colorectal cancer. Automated tissue segmentation can be useful for two of the most relevant clinical target applications-lesion detection and classification, thereby providing important means to make both processes more ac...

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Autores: Guo, Yunbo, Bernal del Nozal, Jorge|||0000-0001-8493-9514, J. Matuszewski, Bogdan
Tipo de recurso: artículo
Fecha de publicación:2020
País:España
Institución:Universitat Autònoma de Barcelona
Repositorio:Dipòsit Digital de Documents de la UAB
Idioma:inglés
OAI Identifier:oai:ddd.uab.cat:249102
Acceso en línea:https://ddd.uab.cat/record/249102
https://dx.doi.org/urn:doi:10.3390/jimaging6070069
Access Level:acceso abierto
Palabra clave:Fully convolutional dilation neural networks
Polyp segmentation
Data augmentation
Cross-validation
Ablation tests
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spelling Polyp Segmentation with Fully Convolutional Deep Neural Networks-Extended Evaluation StudyGuo, YunboBernal del Nozal, Jorge|||0000-0001-8493-9514J. Matuszewski, BogdanFully convolutional dilation neural networksPolyp segmentationData augmentationCross-validationAblation testsAnalysis of colonoscopy images plays a significant role in early detection of colorectal cancer. Automated tissue segmentation can be useful for two of the most relevant clinical target applications-lesion detection and classification, thereby providing important means to make both processes more accurate and robust. To automate video colonoscopy analysis, computer vision and machine learning methods have been utilized and shown to enhance polyp detectability and segmentation objectivity. This paper describes a polyp segmentation algorithm, developed based on fully convolutional network models, that was originally developed for the Endoscopic Vision Gastrointestinal Image Analysis (GIANA) polyp segmentation challenges. The key contribution of the paper is an extended evaluation of the proposed architecture, by comparing it against established image segmentation benchmarks utilizing several metrics with cross-validation on the GIANA training dataset. Different experiments are described, including examination of various network configurations, values of design parameters, data augmentation approaches, and polyp characteristics. The reported results demonstrate the significance of the data augmentation, and careful selection of the method's design parameters. The proposed method delivers state-of-the-art results with near real-time performance. The described solution was instrumental in securing the top spot for the polyp segmentation sub-challenge at the 2017 GIANA challenge and second place for the standard image resolution segmentation task at the 2018 GIANA challenge. 22020-01-0120202020-01-01Articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfhttps://ddd.uab.cat/record/249102https://dx.doi.org/urn:doi:10.3390/jimaging6070069reponame:Dipòsit Digital de Documents de la UABinstname:Universitat Autònoma de BarcelonaInglésengopen 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:2491022026-06-06T12:50:31Z
dc.title.none.fl_str_mv Polyp Segmentation with Fully Convolutional Deep Neural Networks-Extended Evaluation Study
title Polyp Segmentation with Fully Convolutional Deep Neural Networks-Extended Evaluation Study
spellingShingle Polyp Segmentation with Fully Convolutional Deep Neural Networks-Extended Evaluation Study
Guo, Yunbo
Fully convolutional dilation neural networks
Polyp segmentation
Data augmentation
Cross-validation
Ablation tests
title_short Polyp Segmentation with Fully Convolutional Deep Neural Networks-Extended Evaluation Study
title_full Polyp Segmentation with Fully Convolutional Deep Neural Networks-Extended Evaluation Study
title_fullStr Polyp Segmentation with Fully Convolutional Deep Neural Networks-Extended Evaluation Study
title_full_unstemmed Polyp Segmentation with Fully Convolutional Deep Neural Networks-Extended Evaluation Study
title_sort Polyp Segmentation with Fully Convolutional Deep Neural Networks-Extended Evaluation Study
dc.creator.none.fl_str_mv Guo, Yunbo
Bernal del Nozal, Jorge|||0000-0001-8493-9514
J. Matuszewski, Bogdan
author Guo, Yunbo
author_facet Guo, Yunbo
Bernal del Nozal, Jorge|||0000-0001-8493-9514
J. Matuszewski, Bogdan
author_role author
author2 Bernal del Nozal, Jorge|||0000-0001-8493-9514
J. Matuszewski, Bogdan
author2_role author
author
dc.subject.none.fl_str_mv Fully convolutional dilation neural networks
Polyp segmentation
Data augmentation
Cross-validation
Ablation tests
topic Fully convolutional dilation neural networks
Polyp segmentation
Data augmentation
Cross-validation
Ablation tests
description Analysis of colonoscopy images plays a significant role in early detection of colorectal cancer. Automated tissue segmentation can be useful for two of the most relevant clinical target applications-lesion detection and classification, thereby providing important means to make both processes more accurate and robust. To automate video colonoscopy analysis, computer vision and machine learning methods have been utilized and shown to enhance polyp detectability and segmentation objectivity. This paper describes a polyp segmentation algorithm, developed based on fully convolutional network models, that was originally developed for the Endoscopic Vision Gastrointestinal Image Analysis (GIANA) polyp segmentation challenges. The key contribution of the paper is an extended evaluation of the proposed architecture, by comparing it against established image segmentation benchmarks utilizing several metrics with cross-validation on the GIANA training dataset. Different experiments are described, including examination of various network configurations, values of design parameters, data augmentation approaches, and polyp characteristics. The reported results demonstrate the significance of the data augmentation, and careful selection of the method's design parameters. The proposed method delivers state-of-the-art results with near real-time performance. The described solution was instrumental in securing the top spot for the polyp segmentation sub-challenge at the 2017 GIANA challenge and second place for the standard image resolution segmentation task at the 2018 GIANA challenge.
publishDate 2020
dc.date.none.fl_str_mv 2
2020-01-01
2020
2020-01-01
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/249102
https://dx.doi.org/urn:doi:10.3390/jimaging6070069
url https://ddd.uab.cat/record/249102
https://dx.doi.org/urn:doi:10.3390/jimaging6070069
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
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
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