UCL Discovery
UCL home » Library Services » Electronic resources » UCL Discovery

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

Bernal, J; Tajkbaksh, N; Sanchez, FJ; Matuszewski, BJ; Chen, H; Yu, L; Angermann, Q; ... Histace, A; + view all (2017) Comparative Validation of Polyp Detection Methods in Video Colonoscopy: Results From the MICCAI 2015 Endoscopic Vision Challenge. IEEE Transactions on Medical Imaging , 36 (6) pp. 1231-1249. 10.1109/TMI.2017.2664042. Green open access

[img]
Preview
Text
Stoyanov_17023 Final Version.pdf - Accepted version

Download (3MB) | Preview

Abstract

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.

Type: Article
Title: Comparative Validation of Polyp Detection Methods in Video Colonoscopy: Results From the MICCAI 2015 Endoscopic Vision Challenge
Open access status: An open access version is available from UCL Discovery
DOI: 10.1109/TMI.2017.2664042
Publisher version: http://dx.doi.org/10.1109/TMI.2017.2664042
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Science & Technology, Technology, Life Sciences & Biomedicine, Computer Science, Interdisciplinary Applications, Engineering, Biomedical, Engineering, Electrical & Electronic, Imaging Science & Photographic Technology, Radiology, Nuclear Medicine & Medical Imaging, Computer Science, Engineering, Endoscopic vision, polyp detection, handcrafted features, machine learning, validation framework, OPTICAL DIAGNOSIS, CAPSULE ENDOSCOPY, CT COLONOGRAPHY, MISS RATE, ADENOMAS, ACCURACY, LESIONS, SYSTEM, IMPACT, CANCER
UCL classification: UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
URI: https://discovery.ucl.ac.uk/id/eprint/1540838
Downloads since deposit
96Downloads
Download activity - last month
Download activity - last 12 months
Downloads by country - last 12 months

Archive Staff Only

View Item View Item