TY - JOUR
T1 - Comparative Validation of Polyp Detection Methods in Video Colonoscopy
T2 - Results from the MICCAI 2015 Endoscopic Vision Challenge
AU - Bernal, Jorge
AU - Tajkbaksh, Nima
AU - Sanchez, Francisco Javier
AU - Matuszewski, Bogdan J.
AU - Chen, Hao
AU - Yu, Lequan
AU - Angermann, Quentin
AU - Romain, Olivier
AU - Rustad, Bjorn
AU - Balasingham, Ilangko
AU - Pogorelov, Konstantin
AU - Choi, Sungbin
AU - Debard, Quentin
AU - Maier-Hein, Lena
AU - Speidel, Stefanie
AU - Stoyanov, Danail
AU - Brandao, Patrick
AU - Cordova, Henry
AU - Sanchez-Montes, Cristina
AU - Gurudu, Suryakanth R.
AU - Fernandez-Esparrach, Gloria
AU - Dray, Xavier
AU - Liang, Jianming
AU - Histace, Aymeric
N1 - Funding Information:
Manuscript received October 30, 2016; revised January 27, 2017; accepted January 31, 2017. Date of publication February 2, 2017; date of current version June 1, 2017. This work was supported in part by ASU-Mayo Clinic partnerships, in part by the Spanish Government through the Funded Project iVENDIS under Project DPI2015-65286-R, in part by FSEED, in part by the Secretaria d’Universitats i Recerca de la Generalitat de Catalunya under Grant 2014-SGR-1470 and Grant 2014-SGR-135, in part by par SATT IdFInnov (France) through the Project Smart Videocolonoscopy under Grant 186, and in part by the European Union through the ERC starting grant COMBIOSCOPY under the New Horizon Framework Programme under Grant ERC-2015-StG-37960. (Jorge Bernal and Nima Tajbaksh share first co-authorship. Aymeric Histace and Jianming Liang share last co-authorship) Asterisk indicates corresponding author. J. Bernal and F. J. Sánchez are with the Computer Science Department, Universitat Autònoma de Barcelona, 08193 Bellaterra, Spain, and also with the Computer Vision Center, Universitat Autònoma de Barcelona, 08193 Bellaterra, Spain. N. Tajkbaksh and J. Liang are with Arizona State University, Tempe, AZ 85281 USA. B. J. Matuszewski is with the School of Engineering, University of Central Lancashire, Preston PR1 2HE, U.K. H. Chen and L. Yu are with the Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong. Q. Angermann and O. Romain are with ETIS, ENSEA, CNRS, University of Cergy-Pontoise, F95000 Cergy, France. B. Rustad is with Oslo University Hospital, 0379 Oslo, Norway, and also with OmniVision, University of Oslo, 0313 Oslo, Norway. I. Balasingham is with Oslo University Hospital, 0379 Oslo, Norway. K. Pogorelov is with the Media Performance Group, Simula Research Laboratory, and University of Oslo, 0313 Oslo, Norway. S. Choi is with Seoul National University, Seoul 08826, South Korea. Q. Debard is with the University of Nice-Sophia Antipolis, 06000 Nice, France. L. Maier-Hein is with the Junior Group Computer-assisted Interventions, German Cancer Research Center, 69120 Heidelberg, Germany. S. Speidel is with the Institute for Anthropomatics, Karlsruhe Institute of Technology, 76021 Karlsruhe, Germany. D. Stoyanov and P. Brandao are with the Centre for Medical Image Computing and Department of Computer Science, University College London, London WC1E 6BT, U.K. H. Córdova, C. Sánchez-Montes, and G. Fernández-Esparrach are
PY - 2017/6
Y1 - 2017/6
N2 - 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.
AB - 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.
KW - Endoscopic vision
KW - handcrafted features
KW - machine learning
KW - polyp detection
KW - validation framework
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U2 - 10.1109/TMI.2017.2664042
DO - 10.1109/TMI.2017.2664042
M3 - Article
C2 - 28182555
AN - SCOPUS:85021449496
VL - 36
SP - 1231
EP - 1249
JO - IEEE Transactions on Medical Imaging
JF - IEEE Transactions on Medical Imaging
SN - 0278-0062
IS - 6
M1 - 7840040
ER -