TY  - JOUR
AU  - Jorge Bernal
AU  - Nima Tajkbaksh
AU  - F. Javier Sanchez
AU  - Bogdan J. Matuszewski
AU  - Hao Chen
AU  - Lequan Yu
AU  - Quentin Angermann
AU  - Olivier Romain
AU  - Bjorn Rustad
AU  - Ilangko Balasingham
AU  - Konstantin Pogorelov
AU  - Sungbin Choi
AU  - Quentin Debard
AU  - Lena Maier Hein
AU  - Stefanie Speidel
AU  - Danail Stoyanov
AU  - Patrick Brandao
AU  - Henry Cordova
AU  - Cristina Sanchez Montes
AU  - Suryakanth R. Gurudu
AU  - Gloria Fernandez Esparrach
AU  - Xavier Dray
AU  - Jianming Liang
AU  - Aymeric Histace
PY  - 2017//
TI  - Comparative Validation of Polyp Detection Methods in Video Colonoscopy: Results from the MICCAI 2015 Endoscopic Vision Challenge
T2  - TMI
JO  - IEEE Transactions on Medical Imaging
SP  - 1231
EP  - 1249
VL  - 36
IS  - 6
KW  - Endoscopic vision
KW  - Polyp Detection
KW  - Handcrafted features
KW  - Machine Learning
KW  - Validation Framework
N2  - 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 lackof 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 subchallenge, conducted as part of the Endoscopic Vision Challenge (http://endovis.grand-challenge.org) at the international conference on Medical Image Computing and Computer AssistedIntervention (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.
L1  - http://refbase.cvc.uab.es/files/BTS2017.pdf
UR  - http://dx.doi.org/10.1109/TMI.2017.2664042
N1  - MV; 600.096; 600.075
ID  - Jorge Bernal2017
ER  -