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Automated Polyp Detection in Colonoscopy Videos Using Shape and Context Information.
IEEE Trans Med Imaging. 2016 Feb; 35(2):630-44.IT

Abstract

This paper presents the culmination of our research in designing a system for computer-aided detection (CAD) of polyps in colonoscopy videos. Our system is based on a hybrid context-shape approach, which utilizes context information to remove non-polyp structures and shape information to reliably localize polyps. Specifically, given a colonoscopy image, we first obtain a crude edge map. Second, we remove non-polyp edges from the edge map using our unique feature extraction and edge classification scheme. Third, we localize polyp candidates with probabilistic confidence scores in the refined edge maps using our novel voting scheme. The suggested CAD system has been tested using two public polyp databases, CVC-ColonDB, containing 300 colonoscopy images with a total of 300 polyp instances from 15 unique polyps, and ASU-Mayo database, which is our collection of colonoscopy videos containing 19,400 frames and a total of 5,200 polyp instances from 10 unique polyps. We have evaluated our system using free-response receiver operating characteristic (FROC) analysis. At 0.1 false positives per frame, our system achieves a sensitivity of 88.0% for CVC-ColonDB and a sensitivity of 48% for the ASU-Mayo database. In addition, we have evaluated our system using a new detection latency analysis where latency is defined as the time from the first appearance of a polyp in the colonoscopy video to the time of its first detection by our system. At 0.05 false positives per frame, our system yields a polyp detection latency of 0.3 seconds.

Authors

No affiliation info availableNo affiliation info availableNo affiliation info available

Pub Type(s)

Journal Article
Research Support, Non-U.S. Gov't

Language

eng

PubMed ID

26462083

Citation

Tajbakhsh, Nima, et al. "Automated Polyp Detection in Colonoscopy Videos Using Shape and Context Information." IEEE Transactions On Medical Imaging, vol. 35, no. 2, 2016, pp. 630-44.
Tajbakhsh N, Gurudu SR, Liang J. Automated Polyp Detection in Colonoscopy Videos Using Shape and Context Information. IEEE Trans Med Imaging. 2016;35(2):630-44.
Tajbakhsh, N., Gurudu, S. R., & Liang, J. (2016). Automated Polyp Detection in Colonoscopy Videos Using Shape and Context Information. IEEE Transactions On Medical Imaging, 35(2), 630-44. https://doi.org/10.1109/TMI.2015.2487997
Tajbakhsh N, Gurudu SR, Liang J. Automated Polyp Detection in Colonoscopy Videos Using Shape and Context Information. IEEE Trans Med Imaging. 2016;35(2):630-44. PubMed PMID: 26462083.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Automated Polyp Detection in Colonoscopy Videos Using Shape and Context Information. AU - Tajbakhsh,Nima, AU - Gurudu,Suryakanth R, AU - Liang,Jianming, Y1 - 2015/10/08/ PY - 2015/10/14/entrez PY - 2015/10/16/pubmed PY - 2016/12/15/medline SP - 630 EP - 44 JF - IEEE transactions on medical imaging JO - IEEE Trans Med Imaging VL - 35 IS - 2 N2 - This paper presents the culmination of our research in designing a system for computer-aided detection (CAD) of polyps in colonoscopy videos. Our system is based on a hybrid context-shape approach, which utilizes context information to remove non-polyp structures and shape information to reliably localize polyps. Specifically, given a colonoscopy image, we first obtain a crude edge map. Second, we remove non-polyp edges from the edge map using our unique feature extraction and edge classification scheme. Third, we localize polyp candidates with probabilistic confidence scores in the refined edge maps using our novel voting scheme. The suggested CAD system has been tested using two public polyp databases, CVC-ColonDB, containing 300 colonoscopy images with a total of 300 polyp instances from 15 unique polyps, and ASU-Mayo database, which is our collection of colonoscopy videos containing 19,400 frames and a total of 5,200 polyp instances from 10 unique polyps. We have evaluated our system using free-response receiver operating characteristic (FROC) analysis. At 0.1 false positives per frame, our system achieves a sensitivity of 88.0% for CVC-ColonDB and a sensitivity of 48% for the ASU-Mayo database. In addition, we have evaluated our system using a new detection latency analysis where latency is defined as the time from the first appearance of a polyp in the colonoscopy video to the time of its first detection by our system. At 0.05 false positives per frame, our system yields a polyp detection latency of 0.3 seconds. SN - 1558-254X UR - https://www.unboundmedicine.com/medline/citation/26462083/Automated_Polyp_Detection_in_Colonoscopy_Videos_Using_Shape_and_Context_Information_ L2 - https://dx.doi.org/10.1109/TMI.2015.2487997 DB - PRIME DP - Unbound Medicine ER -