Article

Automatic measurement of quality metrics for colonoscopy videos

Authors:

Wallapak Tavanapong,

Piet C. de GroenAuthors Info & Claims

MULTIMEDIA '05: Proceedings of the 13th annual ACM international conference on Multimedia

Pages 912 - 921

https://doi.org/10.1145/1101149.1101343

Published: 06 November 2005 Publication History

Abstract

Colonoscopy is the accepted screening method for detection of colorectal cancer or its precursor lesions, colorectal polyps. Indeed, colonoscopy has contributed to a decline in the number of colorectal cancer related deaths. However, not all cancers or large polyps are detected at the time of colonoscopy, and methods to investigate why this occurs are needed. We present a new computer-based method that allows automated measurement of a number of metrics that likely reflect the quality of the colonoscopic procedure. The method is based on analysis of a digitized video file created during colonoscopy, and produces information regarding insertion time, withdrawal time, images at the time of maximal intubation, the time and ratio of clear versus blurred or non-informative images, and a first estimate of effort performed by the endoscopist. As these metrics can be obtained automatically, our method allows future quality control in the day-to-day medical practice setting on a large scale. In addition, our method can be adapted to other healthcare procedures. Last but not least, our method may be useful to assess progress during colonoscopy training, or as part of endoscopic skills assessment evaluations.

References

[1]

A. Akutsu, Y. Tonomura, H. Hashimoto, and Y. Ohba. Video indexing using motion vectors. In SPIE, pages 1522--1530, 1992.

[2]

Y. H. An, J. Oh, S. Hwang, W. Tavanapong, P. C. de Groen, and J. Wong. Informative-Frame Filtering in Endoscopy Videos. In SPIE International Symposium, Medical Imaging 2005, San Diego, CA, 2005.

[3]

X. Cao and P. N. Suganthan. Video Shot Motion Characterization based on Hierarchical Overlapped Growing Neural Gas Networks. In Multimedia Systems, pages 378--385, October 2003.

[4]

T. H. Cormen, C. E. Leiserson, R. L. Rivest, and C. Stein. Introduction to Algorithms (2nd ed). MIT Press, 2001.

Digital Library

[5]

A. Dante and M. Brookes. Precise Real-Time Outlier Removal from Motion Vector Fields For 3D Reconstruction. In IEEE ICIP, pages 393--396, Barcelona, 2003.

[6]

Y. Deng and B. S. Manjunath. Unsupervised segmentation of color-texture regions in images and video. IEEE Trans. on Pattern Analysis and Machine Intelligence, 2001.

Digital Library

[7]

D. L. Editorial. Quality and colonoscopy: a new imperative. Gastrointestinal endoscopy, 61(2), 2005.

[8]

J. Kim, H. Chang, J. Kim, and H. Kim. Efficient camera motion characterization for MPEG video indexing. In IEEE ICME, pages 1171--1174, 2000.

[9]

S. Kuwada. Colorectal Cancer 2000. Postgraduate Medicine, 107:96--107, March 2000.

[10]

B. P., G. M., and G. F. A unified approach to shot change detection and camera motion characterization. In IEEE Trans Circuits and Syst. for Video Technol., pages 1030--1044, 1999.

Digital Library

[11]

A. Pabby, R. E. Schoen, J. L. Weissfeld, R. Burt, J. W. Kikendall, P. Lance, E. Lanza, and A. Schatzkin. Analysis of colorectal cancer occurence during surveillance colonoscopy in the dietary prevention trial. Gastrointestinal Endoscopy, 61(3):385--391, 2005.

[12]

D. K. Rex, J. H. Bond, S. Winawer, T. R. Levin, R. W. Burt, and D. A. J. et al. Quality in the technical performance of colonoscopy and the continuous quality improvement process for colonoscopy: recommendations of the u.s. multi-society task force on colorectal cancer. American Journal Gastroenterol, 97(6):1296--1308, 2002.

[13]

Y.-P. Tan, D. Saur, S. Kulkarni, and P. Ramadge. Rapid Estimation of Camera Motion from Compressed Video with Application to Video Annotation. IEEE Trans. Circuits Syst. Video Technol, 10:133--146, Feburary 2000.

Digital Library

[14]

H. Tian, T. Srikanthan, and K. V. Asari. Automatic Segmentation Algorithm for the Extraction of Lumen Region and Boundary from Endoscopic Images. Medical and Biological Engineering and Computing, 39:8--14, 2001.

[15]

H. Yi, D. Rajan, and L.-T. Chia. Automatic Extraction of Motion Trajectories in Compressed Sports Videos. In ACM Multimedia 2004, New York, NY, October 2004.

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Cited By

van der Putten Jvan der Sommen F(2022)AIM in Barrett’s EsophagusArtificial Intelligence in Medicine10.1007/978-3-030-64573-1_166(951-966)Online publication date: 18-Feb-2022
https://doi.org/10.1007/978-3-030-64573-1_166
van der Putten Jvan der Sommen F(2021)AIM in Barrett’s EsophagusArtificial Intelligence in Medicine10.1007/978-3-030-58080-3_166-1(1-16)Online publication date: 12-Nov-2021
https://doi.org/10.1007/978-3-030-58080-3_166-1
Zhang CZhang NWang DCao YLiu B(2020)Artifact Detection in Endoscopic Video with Deep Convolutional Neural Networks2020 Second International Conference on Transdisciplinary AI (TransAI)10.1109/TransAI49837.2020.00007(1-8)Online publication date: Sep-2020
https://doi.org/10.1109/TransAI49837.2020.00007
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Index Terms

Automatic measurement of quality metrics for colonoscopy videos
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Image and video acquisition
        Motion capture
  2. Computer graphics
    1. Animation
      1. Motion capture
      2. Motion processing

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Published In

cover image ACM Conferences

MULTIMEDIA '05: Proceedings of the 13th annual ACM international conference on Multimedia

November 2005

1110 pages

ISBN:1595930442

DOI:10.1145/1101149

General Chairs:
Hongjiang Zhang
Microsoft Research Asia, China
,
Tat-Seng Chua
National University of Singapore, Singapore
,
Program Chairs:
Ralf Steinmetz
Technische Universitat Darmstadt, Germany
,
Mohan Kankanhalli
National University of Singapore, Singapore
,
Lynn Wilcox
FXPAL

Copyright © 2005 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 06 November 2005

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MM05

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MM05: 2005 13th Annual ACM International Conference on Multimedia

November 6 - 11, 2005

Hilton, Singapore

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MULTIMEDIA '05 Paper Acceptance Rate 49 of 312 submissions, 16%;

Overall Acceptance Rate 2,145 of 8,556 submissions, 25%

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Citations

Cited By

van der Putten Jvan der Sommen F(2022)AIM in Barrett’s EsophagusArtificial Intelligence in Medicine10.1007/978-3-030-64573-1_166(951-966)Online publication date: 18-Feb-2022
https://doi.org/10.1007/978-3-030-64573-1_166
van der Putten Jvan der Sommen F(2021)AIM in Barrett’s EsophagusArtificial Intelligence in Medicine10.1007/978-3-030-58080-3_166-1(1-16)Online publication date: 12-Nov-2021
https://doi.org/10.1007/978-3-030-58080-3_166-1
Zhang CZhang NWang DCao YLiu B(2020)Artifact Detection in Endoscopic Video with Deep Convolutional Neural Networks2020 Second International Conference on Transdisciplinary AI (TransAI)10.1109/TransAI49837.2020.00007(1-8)Online publication date: Sep-2020
https://doi.org/10.1109/TransAI49837.2020.00007
Münzer BSchoeffmann KBöszörmenyi L(2018)Content-based processing and analysis of endoscopic images and videosMultimedia Tools and Applications10.1007/s11042-016-4219-z77:1(1323-1362)Online publication date: 1-Jan-2018
https://dl.acm.org/doi/10.1007/s11042-016-4219-z
Ballesteros CTrujillo MMazo CChaves DHoyos J(2017)Automatic Classification of Non-informative Frames in Colonoscopy Videos Using Texture AnalysisProgress in Pattern Recognition, Image Analysis, Computer Vision, and Applications10.1007/978-3-319-52277-7_49(401-408)Online publication date: 16-Feb-2017
https://doi.org/10.1007/978-3-319-52277-7_49
Nawarathna ROh JMuthukudage JTavanapong WWong JDe Groen PTang S(2014)Abnormal image detection in endoscopy videos using a filter bank and local binary patternsNeurocomputing10.1016/j.neucom.2014.02.064144(70-91)Online publication date: 1-Nov-2014
https://dl.acm.org/doi/10.1016/j.neucom.2014.02.064
Muthukudage JOh JNawarathna RTavanapong WWong Jde Groen P(2013)Fast Object Detection Using Color Features for Colonoscopy Quality MeasurementsAbdomen and Thoracic Imaging10.1007/978-1-4614-8498-1_14(365-388)Online publication date: 9-Nov-2013
https://doi.org/10.1007/978-1-4614-8498-1_14
Yi Wang Tavanapong WWong JJungHwan Oh de Groen P(2010)Detection of Quality Visualization of Appendiceal Orifices Using Local Edge Cross-Section Profile Features and Near Pause DetectionIEEE Transactions on Biomedical Engineering10.1109/TBME.2009.203446657:3(685-695)Online publication date: Mar-2010
https://doi.org/10.1109/TBME.2009.2034466
Liu XTavanapong WWong JOh Jde Groen P(2010)Automated measurement of quality of mucosa inspection for colonoscopyProcedia Computer Science10.1016/j.procs.2010.04.1051:1(951-960)Online publication date: May-2010
https://doi.org/10.1016/j.procs.2010.04.105
Grega MLeszczuk MDuplaga MFraczek R(2010)Algorithms for Automatic Recognition of Non-informative Frames in Video Recordings of Bronchoscopic ProceduresInformation Technologies in Biomedicine10.1007/978-3-642-13105-9_53(535-545)Online publication date: 2010
https://doi.org/10.1007/978-3-642-13105-9_53
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