Computer-aided detection of retroflexion in colonoscopy

Yi Wang, Wallapak Tavanapong, Johnny Wong, Junghwan Oh, Piet C. De Groen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

11 Citations (Scopus)

Abstract

Colonoscopy is the most popular screening tool for colorectal cancer. Recent studies reported that retroflexion during colonoscopy improved polyp yields. Retroflexion is an endoscope maneuver that enables visualization of internal mucosa along the shaft of the endoscope, enabling visualization of the mucosa area that is difficult to see with typical forward viewing. This paper describes our new method that detects endoscopic images showing retroflexion. This problem has not been investigated in the literature. We propose new region features that encapsulate important properties of endoscope appearance during retroflexion. Our experimental results on 25 colonoscopy videos show that trained Decision Tree classifiers can effectively identify retroflexion in the rectum at 92.0% accuracy and 94.4% precision.

Original languageEnglish (US)
Title of host publicationProceedings - IEEE Symposium on Computer-Based Medical Systems
DOIs
StatePublished - 2011
Event24th International Symposium on Computer-Based Medical Systems, CBMS 2011 - Bristol, United Kingdom
Duration: Jun 27 2011Jun 30 2011

Other

Other24th International Symposium on Computer-Based Medical Systems, CBMS 2011
CountryUnited Kingdom
CityBristol
Period6/27/116/30/11

Fingerprint

Endoscopy
Endoscopes
Colonoscopy
Mucous Membrane
Visualization
Decision Trees
Decision trees
Polyps
Rectum
Colorectal Neoplasms
Screening
Classifiers

ASJC Scopus subject areas

  • Computer Science Applications
  • Radiology Nuclear Medicine and imaging

Cite this

Wang, Y., Tavanapong, W., Wong, J., Oh, J., & De Groen, P. C. (2011). Computer-aided detection of retroflexion in colonoscopy. In Proceedings - IEEE Symposium on Computer-Based Medical Systems [5999137] https://doi.org/10.1109/CBMS.2011.5999137

Computer-aided detection of retroflexion in colonoscopy. / Wang, Yi; Tavanapong, Wallapak; Wong, Johnny; Oh, Junghwan; De Groen, Piet C.

Proceedings - IEEE Symposium on Computer-Based Medical Systems. 2011. 5999137.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Wang, Y, Tavanapong, W, Wong, J, Oh, J & De Groen, PC 2011, Computer-aided detection of retroflexion in colonoscopy. in Proceedings - IEEE Symposium on Computer-Based Medical Systems., 5999137, 24th International Symposium on Computer-Based Medical Systems, CBMS 2011, Bristol, United Kingdom, 6/27/11. https://doi.org/10.1109/CBMS.2011.5999137
Wang Y, Tavanapong W, Wong J, Oh J, De Groen PC. Computer-aided detection of retroflexion in colonoscopy. In Proceedings - IEEE Symposium on Computer-Based Medical Systems. 2011. 5999137 https://doi.org/10.1109/CBMS.2011.5999137
Wang, Yi ; Tavanapong, Wallapak ; Wong, Johnny ; Oh, Junghwan ; De Groen, Piet C. / Computer-aided detection of retroflexion in colonoscopy. Proceedings - IEEE Symposium on Computer-Based Medical Systems. 2011.
@inproceedings{db98860959a44a3bbe4cb06edd124e41,
title = "Computer-aided detection of retroflexion in colonoscopy",
abstract = "Colonoscopy is the most popular screening tool for colorectal cancer. Recent studies reported that retroflexion during colonoscopy improved polyp yields. Retroflexion is an endoscope maneuver that enables visualization of internal mucosa along the shaft of the endoscope, enabling visualization of the mucosa area that is difficult to see with typical forward viewing. This paper describes our new method that detects endoscopic images showing retroflexion. This problem has not been investigated in the literature. We propose new region features that encapsulate important properties of endoscope appearance during retroflexion. Our experimental results on 25 colonoscopy videos show that trained Decision Tree classifiers can effectively identify retroflexion in the rectum at 92.0{\%} accuracy and 94.4{\%} precision.",
author = "Yi Wang and Wallapak Tavanapong and Johnny Wong and Junghwan Oh and {De Groen}, {Piet C.}",
year = "2011",
doi = "10.1109/CBMS.2011.5999137",
language = "English (US)",
isbn = "9781457711909",
booktitle = "Proceedings - IEEE Symposium on Computer-Based Medical Systems",

}

TY - GEN

T1 - Computer-aided detection of retroflexion in colonoscopy

AU - Wang, Yi

AU - Tavanapong, Wallapak

AU - Wong, Johnny

AU - Oh, Junghwan

AU - De Groen, Piet C.

PY - 2011

Y1 - 2011

N2 - Colonoscopy is the most popular screening tool for colorectal cancer. Recent studies reported that retroflexion during colonoscopy improved polyp yields. Retroflexion is an endoscope maneuver that enables visualization of internal mucosa along the shaft of the endoscope, enabling visualization of the mucosa area that is difficult to see with typical forward viewing. This paper describes our new method that detects endoscopic images showing retroflexion. This problem has not been investigated in the literature. We propose new region features that encapsulate important properties of endoscope appearance during retroflexion. Our experimental results on 25 colonoscopy videos show that trained Decision Tree classifiers can effectively identify retroflexion in the rectum at 92.0% accuracy and 94.4% precision.

AB - Colonoscopy is the most popular screening tool for colorectal cancer. Recent studies reported that retroflexion during colonoscopy improved polyp yields. Retroflexion is an endoscope maneuver that enables visualization of internal mucosa along the shaft of the endoscope, enabling visualization of the mucosa area that is difficult to see with typical forward viewing. This paper describes our new method that detects endoscopic images showing retroflexion. This problem has not been investigated in the literature. We propose new region features that encapsulate important properties of endoscope appearance during retroflexion. Our experimental results on 25 colonoscopy videos show that trained Decision Tree classifiers can effectively identify retroflexion in the rectum at 92.0% accuracy and 94.4% precision.

UR - http://www.scopus.com/inward/record.url?scp=80053049285&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=80053049285&partnerID=8YFLogxK

U2 - 10.1109/CBMS.2011.5999137

DO - 10.1109/CBMS.2011.5999137

M3 - Conference contribution

AN - SCOPUS:80053049285

SN - 9781457711909

BT - Proceedings - IEEE Symposium on Computer-Based Medical Systems

ER -