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 subchallenge, 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 language | English (US) |
|---|---|
| Article number | 7840040 |
| Pages (from-to) | 1231-1249 |
| Number of pages | 19 |
| Journal | IEEE Transactions on Vehicular Technology |
| Volume | 36 |
| Issue number | 6 |
| DOIs | |
| State | Published - Jun 1 2017 |
Fingerprint
Keywords
- Endoscopic vision
- Handcrafted features
- Machine learning
- Polyp detection
- Validation framework
ASJC Scopus subject areas
- Automotive Engineering
- Aerospace Engineering
- Computer Networks and Communications
- Applied Mathematics
- Electrical and Electronic Engineering
Cite this
Comparative validation of polyp detection methods in video colonoscopy : Results from the MICCAI 2015 endoscopic vision challenge. / Bernal, Jorge; Tajkbaksh, Nima; Sánchez, Francisco Javier; Matuszewski, Bogdan J.; Chen, Hao; Yu, Lequan; Angermann, Quentin; Romain, Olivier; Rustad, Bjørn; Balasingham, Ilangko; Pogorelov, Konstantin; Choi, Sungbin; Debard, Quentin; Maier-Hein, Lena; Speidel, Stefanie; Stoyanov, Danail; Brandao, Patrick; Córdova, Henry; Sánchez-Montes, Cristina; Gurudu, Suryakanth R.; Fernández-Esparrach, Gloria; Dray, Xavier; Liang, Jianming; Histace, Aymeric.
In: IEEE Transactions on Vehicular Technology, Vol. 36, No. 6, 7840040, 01.06.2017, p. 1231-1249.Research output: Contribution to journal › Review article
}
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 - Sánchez, Francisco Javier
AU - Matuszewski, Bogdan J.
AU - Chen, Hao
AU - Yu, Lequan
AU - Angermann, Quentin
AU - Romain, Olivier
AU - Rustad, Bjørn
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 - Córdova, Henry
AU - Sánchez-Montes, Cristina
AU - Gurudu, Suryakanth R.
AU - Fernández-Esparrach, Gloria
AU - Dray, Xavier
AU - Liang, Jianming
AU - Histace, Aymeric
PY - 2017/6/1
Y1 - 2017/6/1
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 subchallenge, 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 subchallenge, 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
UR - http://www.scopus.com/inward/record.url?scp=85021449496&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85021449496&partnerID=8YFLogxK
U2 - 10.1109/TMI.2017.2664042
DO - 10.1109/TMI.2017.2664042
M3 - Review article
VL - 36
SP - 1231
EP - 1249
JO - IEEE Transactions on Vehicular Technology
JF - IEEE Transactions on Vehicular Technology
SN - 0018-9545
IS - 6
M1 - 7840040
ER -