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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Detalhes bibliográficos
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
Tipo de documento: artigo
Estado:Versión aceptada para publicación
Data de publicação:2017
País:España
Recursos:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
Repositório: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 aberto
Palavra-chave:Colonoscòpia
Càncer colorectal
Endoscòpia
Colonoscopy
Colorectal cancer
Endoscopy
Descrição
Resumo: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.

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