Computer-aided detection of diagnostic and therapeutic operations in colonoscopy videos

Yu Cao; Danyu Liu; Wallapak Tavanapong; Johnny Wong; Jung Hwan Oh; Piet C. De Groen

doi:10.1109/TBME.2007.890734

Computer-aided detection of diagnostic and therapeutic operations in colonoscopy videos

Yu Cao, Danyu Liu, Wallapak Tavanapong, Johnny Wong, Jung Hwan Oh, Piet C. De Groen

Research output: Contribution to journal › Review article › peer-review

40 Scopus citations

Abstract

Colonoscopy is an endoscopic technique that allows a physician to inspect the inside of the human colon and to perform if deemed necessaryat the same time a number of diagnostic and therapeutic operations. In order to see the inside of the colon, a video signal of the internal mucosa of the colon is generated by a tiny video camera at the tip of the endoscope and displayed on a monitor for real-time analysis by the physician. We have captured and stored these videos in digital format and call these colonoscopy videos. Based on new algorithms for instrument detection and shot segmentation, we introduce new spatio-temporal analysis techniques to automatically identify an operation shota segment of visual data in a colonoscopy video that corresponds to a diagnostic or therapeutic operation. Our experiments on real colonoscopy videos demonstrate the effectiveness of the proposed approach. The proposed techniques and software are useful for 1) postprocedure review for causes of complications due to diagnostic or therapeutic operations; 2) establishment of an effective content-based retrieval system to facilitate endoscopic research and education; 3) development of a systematic approach to assess and improve the procedural skills of endoscopists.

Original language	English (US)
Pages (from-to)	1268-1279
Number of pages	12
Journal	IEEE Transactions on Biomedical Engineering
Volume	54
Issue number	7
DOIs	https://doi.org/10.1109/TBME.2007.890734
State	Published - Jul 2007
Externally published	Yes

Bibliographical note

Funding Information:
Manuscript received March 21, 2006; revised October 29, 2006. This work was supported in part by the National Science Foundation (NSF) under Grant IIS-0513777, Grant IIS-0513809, and Grant IIS-0513582 and in part by the Mayo Clinic. Asterisk indicates corresponding author. Y. Cao, D. Liu, and J. Wong are with the Department of Computer Science, Iowa State University, Ames, IA 50011-1040 USA. *W. Tavanapong is with the Department of Computer Science, Iowa State University, Ames, IA 50011-1040 USA (e-mail: [email protected]). J. Oh is with the Department of Computer Science and Engineering, University of North Texas, Denton, TX 76203 USA. P. C. de Groen is with the Mayo Clinic College of Medicine, Mayo Clinic, Rochester, MN 55905 USA. Color versions of one or more of the figures in this paper are available online at http://ieeexplore.ieee.org. Digital Object Identifier 10.1109/TBME.2007.890734

Keywords

Colonoscopy
Instrument detection
Medical video analysis
Video segmentation

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10.1109/TBME.2007.890734

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title = "Computer-aided detection of diagnostic and therapeutic operations in colonoscopy videos",

abstract = "Colonoscopy is an endoscopic technique that allows a physician to inspect the inside of the human colon and to perform if deemed necessaryat the same time a number of diagnostic and therapeutic operations. In order to see the inside of the colon, a video signal of the internal mucosa of the colon is generated by a tiny video camera at the tip of the endoscope and displayed on a monitor for real-time analysis by the physician. We have captured and stored these videos in digital format and call these colonoscopy videos. Based on new algorithms for instrument detection and shot segmentation, we introduce new spatio-temporal analysis techniques to automatically identify an operation shota segment of visual data in a colonoscopy video that corresponds to a diagnostic or therapeutic operation. Our experiments on real colonoscopy videos demonstrate the effectiveness of the proposed approach. The proposed techniques and software are useful for 1) postprocedure review for causes of complications due to diagnostic or therapeutic operations; 2) establishment of an effective content-based retrieval system to facilitate endoscopic research and education; 3) development of a systematic approach to assess and improve the procedural skills of endoscopists.",

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note = "Funding Information: Manuscript received March 21, 2006; revised October 29, 2006. This work was supported in part by the National Science Foundation (NSF) under Grant IIS-0513777, Grant IIS-0513809, and Grant IIS-0513582 and in part by the Mayo Clinic. Asterisk indicates corresponding author. Y. Cao, D. Liu, and J. Wong are with the Department of Computer Science, Iowa State University, Ames, IA 50011-1040 USA. *W. Tavanapong is with the Department of Computer Science, Iowa State University, Ames, IA 50011-1040 USA (e-mail: [email protected]). J. Oh is with the Department of Computer Science and Engineering, University of North Texas, Denton, TX 76203 USA. P. C. de Groen is with the Mayo Clinic College of Medicine, Mayo Clinic, Rochester, MN 55905 USA. Color versions of one or more of the figures in this paper are available online at http://ieeexplore.ieee.org. Digital Object Identifier 10.1109/TBME.2007.890734",

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AU - De Groen, Piet C.

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AB - Colonoscopy is an endoscopic technique that allows a physician to inspect the inside of the human colon and to perform if deemed necessaryat the same time a number of diagnostic and therapeutic operations. In order to see the inside of the colon, a video signal of the internal mucosa of the colon is generated by a tiny video camera at the tip of the endoscope and displayed on a monitor for real-time analysis by the physician. We have captured and stored these videos in digital format and call these colonoscopy videos. Based on new algorithms for instrument detection and shot segmentation, we introduce new spatio-temporal analysis techniques to automatically identify an operation shota segment of visual data in a colonoscopy video that corresponds to a diagnostic or therapeutic operation. Our experiments on real colonoscopy videos demonstrate the effectiveness of the proposed approach. The proposed techniques and software are useful for 1) postprocedure review for causes of complications due to diagnostic or therapeutic operations; 2) establishment of an effective content-based retrieval system to facilitate endoscopic research and education; 3) development of a systematic approach to assess and improve the procedural skills of endoscopists.

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Computer-aided detection of diagnostic and therapeutic operations in colonoscopy videos

Abstract

Bibliographical note

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