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

Jorge Bernal, Nima Tajkbaksh, Francisco Javier Sanchez, Bogdan J. Matuszewski, Hao Chen, Lequan Yu, Quentin Angermann, Olivier Romain, Bjorn Rustad, Ilangko Balasingham, Konstantin Pogorelov, Sungbin Choi, Quentin Debard, Lena Maier-Hein, Stefanie Speidel, Danail Stoyanov, Patrick Brandao, Henry Cordova, Cristina Sanchez-Montes, Suryakanth R. GuruduGloria Fernandez-Esparrach, Xavier Dray, Jianming Liang, Aymeric Histace

Research output: Contribution to journalArticle

102 Scopus citations

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.

Original languageEnglish (US)
Article number7840040
Pages (from-to)1231-1249
Number of pages19
JournalIEEE Transactions on Medical Imaging
Volume36
Issue number6
DOIs
StatePublished - Jun 2017

Keywords

  • Endoscopic vision
  • handcrafted features
  • machine learning
  • polyp detection
  • validation framework

ASJC Scopus subject areas

  • Software
  • Radiological and Ultrasound Technology
  • Computer Science Applications
  • Electrical and Electronic Engineering

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    Bernal, J., Tajkbaksh, N., Sanchez, F. J., Matuszewski, B. J., Chen, H., Yu, L., Angermann, Q., Romain, O., Rustad, B., Balasingham, I., Pogorelov, K., Choi, S., Debard, Q., Maier-Hein, L., Speidel, S., Stoyanov, D., Brandao, P., Cordova, H., Sanchez-Montes, C., ... Histace, A. (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), 1231-1249. [7840040]. https://doi.org/10.1109/TMI.2017.2664042