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.
Liste complète des métadonnées

https://hal.archives-ouvertes.fr/hal-01488652
Contributor : Aymeric Histace <>
Submitted on : Monday, March 13, 2017 - 9:34:08 PM
Last modification on : Friday, March 29, 2019 - 4:04:02 PM
Document(s) archivé(s) le : Wednesday, June 14, 2017 - 3:41:24 PM

File

TMIAccepted.pdf
Files produced by the author(s)

Identifiers

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, 36 (6), pp.1231 - 1249. ⟨10.1109/TMI.2017.2664042⟩. ⟨hal-01488652⟩

Share

Metrics

Record views

854

Files downloads

1307