Comparative Validation of Polyp Detection Methods in Video Colonoscopy: Results From the MICCAI 2015 Endoscopic Vision Challenge.
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.
Jorge
J
Bernal
Nima
N
Tajkbaksh
Francisco Javier
FJ
Sanchez
Bogdan J
BJ
Matuszewski
Hao Chen
Lequan Yu
Quentin
Q
Angermann
Olivier
O
Romain
Bjorn
B
Rustad
Ilangko
I
Balasingham
Konstantin
K
Pogorelov
Sungbin Choi
Quentin
Q
Debard
Lena
L
Maier-Hein
Stefanie
S
Speidel
Danail
D
Stoyanov
Patrick
P
Brandao
Henry
H
Cordova
Cristina
C
Sanchez-Montes
Suryakanth R
SR
Gurudu
Gloria
G
Fernandez-Esparrach
Xavier
X
Dray
Jianming Liang
Aymeric
A
Histace
D003110:Colonic Neoplasms; D003111:Colonic Polyps; D003113:Colonoscopy; D055088:Early Detection of Cancer; D006801:Humans; D016571:Neural Networks, Computer