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

Abstract : Colonoscopy is the gold standard for colon cancer screening though still some polyps are 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 (CNNs) are the state of the art. Nevertheless it is also demonstrated that combining different methodologies can lead to an improved overall performance.
Type de document :
Article dans une revue
IEEE Transactions on Medical Imaging, Institute of Electrical and Electronics Engineers, 2017, pp.1-18. <10.1109/TMI.2017.2664042>
Liste complète des métadonnées

https://hal.archives-ouvertes.fr/hal-01488652
Contributeur : Aymeric Histace <>
Soumis le : lundi 13 mars 2017 - 21:34:08
Dernière modification le : vendredi 17 mars 2017 - 08:43:59
Document(s) archivé(s) le : mercredi 14 juin 2017 - 15:41:24

Fichier

TMIAccepted.pdf
Fichiers produits par l'(les) auteur(s)

Identifiants

Collections

Citation

Jorge Bernal, Nima Tajkbaksh, F Sánchez, Bogdan Matuszewski, Hao Chen, et al.. Comparative Validation of Polyp Detection Methods in Video Colonoscopy: Results from the MICCAI 2015 Endoscopic Vision Challenge. IEEE Transactions on Medical Imaging, Institute of Electrical and Electronics Engineers, 2017, pp.1-18. <10.1109/TMI.2017.2664042>. <hal-01488652>

Partager

Métriques

Consultations de
la notice

164

Téléchargements du document

149