Polyp Detection and Segmentation in Video Capsule Endoscopy - A Review

Abstract:

Video capsule endoscopy (VCE) is used widely nowadays for visualizing the gastrointestinal (GI) tract. Capsule endoscopy exams are prescribed usually as an additional monitoring mechanism and can help in identifying polyps, bleeding, etc. To analyze the large scale video data produced by VCE exams, automatic image processing, computer vision, and learning algorithms are required.  Recently, automatic polyp detection algorithms have been proposed with various degrees of success. Though polyp detection in CT colonoscopy and other traditional endoscopy procedure based images is becoming a mature field, due to its unique imaging characteristics detecting polyps automatically in VCE is a hard problem. We review different polyp detection approaches for VCE imagery and provide systematic analysis with challenges faced by standard image processing and computer vision methods.

Bibliography:

Since the topic is new it is possible to trace everything that has been published about it since the  start of video capsule endoscopy. To the best of our knowledge this bibliography represents all available publications on this topic to date, we will revise this document and bring it up to date once in six months.

Methods: detection, localization/segmentation, holistic.

2025 (??)

TBA

2024 (??)

TBA

2023 (??)

TBA

2022 (??)

TBA

2021 (??)

TBA

2020 (??)

TBA

2019 (??)

TBA

2018 (??)

TBA

2017 (??)

TBA

2016 (4?)

TBA

Mohua Zhang, Jianhua Peng, Xuejie Liu. Sparse coding with earth mover’s distance for multi-instance histogram representation. Neural Computing and Applications, 2016. (polyps, bleeding, ulcer)  - Not covered in the 2017 Review! 

Yixuan Yuan, Baopu Li, and Max Q-H Meng. WCE abnormality detection based on saliency and adaptive locality-constrained linear coding. IEEE Transactions on Automation Science and Engineering, 2016. (polyps, bleeding, ulcer)

Yuan, Y.; Meng, M.Q.H. A novel global and local saliency coding method for polyp recognition in WCE videos. Intelligent Robots and Systems (IROS), 2016 IEEE/RSJ International Conference on. IEEE, 2016, pp. 2394–2399.

Yuan, Y.; Li, B.; Meng, M.Q.H. Improved bag of feature for automatic polyp detection in wireless capsule endoscopy images. IEEE Transactions on Automation Science and Engineering 2016, 13, 529–535.

2015 (2)

Prasath, V.B.S.; Kawanaka, H. Vascularization features for polyp localization in capsule endoscopy. Bioinformatics and Biomedicine (BIBM), 2015 IEEE International Conference on. IEEE, 2015, pp. 1740–1742.

Gueye, L.; Yildirim-Yayilgan, S.; Cheikh, F.A.; Balasingham, I. Automatic detection of colonoscopic anomalies using capsule endoscopy. Image Processing (ICIP), 2015 IEEE International Conference on. IEEE, 2015, pp. 1061–1064.

2014 (9)

Meziou, L.; Histace, A.; Precioso, F.; Romain, O.; Dray, X.; Granado, B.; Matuszewski, B.J. Computer-assisted segmentation of videocapsule images using alpha-divergence-based active contour in the framework of intestinal pathologies detection. Journal of Biomedical Imaging 2014, 2014, 18.

Alizadeh, M.; Zadeh, H.S.; Maghsoudi, O.H. Segmentation of small bowel tumors in wireless capsule endoscopy using level set method. Computer-Based Medical Systems (CBMS), 2014 IEEE 27th International Symposium on. IEEE, 2014, pp. 562–563.

Zhou, M.; Bao, G.; Geng, Y.; Alkandari, B.; Li, X. Polyp detection and radius measurement in small intestine using video capsule endoscopy. Biomedical Engineering and Informatics (BMEI), 2014 7th International Conference on. IEEE, 2014, pp. 237–241.

Jia, J.; Sun, S.; Terrence, T.; Wang, P. Accurate and efficient polyp detection in wireless capsule endoscopy images, 2014. US Patent App. 14/471,143.

Nawarathna, R.; Oh, J.; Muthukudage, J.; Tavanapong, W.; Wong, J.; De Groen, P.C.; Tang, S.J. Abnormal image detection in endoscopy videos using a filter bank and local binary patterns. Neurocomputing 2014, 144, 70–91.

Silva, J.; Histace, A.; Romain, O.; Dray, X.; Granado, B. Toward embedded detection of polyps in WCE images for early diagnosis of colorectal cancer. International Journal of Computer Assisted Radiology and Surgery 2014, 9, 283–293.

Mamonov, A.V.; Figueiredo, I.N.; Figueiredo, P.N.; Tsai, Y.H.R. Automated polyp detection in colon capsule endoscopy. Medical Imaging, IEEE Transactions on 2014, 33, 1488–1502.

Yuan, Y.; Meng, M.Q.H. A novel feature for polyp detection in wireless capsule endoscopy images. Intelligent Robots and Systems (IROS 2014), 2014 IEEE/RSJ International Conference on. IEEE, 2014, pp. 5010–5015.

Yuan, Y.; Meng, M.Q.H. Polyp classification based on bag of features and saliency in wireless capsule endoscopy. Robotics and Automation (ICRA), 2014 IEEE International Conference on. IEEE, 2014, pp. 3930–3935.

2013 (4)

Silva, J.; Histace, A.; Romain, O.; Dray, X.; Granado, B.; Pinna, A. Towards real-time in situ polyp detection in WCE images using a boosting-based approach. Engineering in Medicine and Biology Society (EMBC), 2013 35th Annual International Conference of the IEEE. IEEE, 2013, pp. 5711–5714.

Romain, O.; Histace, A.; Silva, J.; Ayoub, J.; Granado, B.; Pinna, A.; Dray, X.; Marteau, P. Towards a multimodal wireless video capsule for detection of colonic polyps as prevention of colorectal cancer. Bioinformatics and Bioengineering (BIBE), 2013 IEEE 13th International Conference on. IEEE, 2013, pp. 1–6.  (Tested on videocolonoscopy dataset)

Figueiredo, I.N.; Kumar, S.; Figueiredo, P.N. An intelligent system for polyp detection in wireless capsule endoscopy images. Computational Vision and Medical Image Processing IV: VIPIMAGE 2013, 2013, 229–235.

David, E.; Boia, R.; Malaescu, A.; Carnu, M. Automatic colon polyp detection in endoscopic capsule images. International Symposium on Signals, Circuits and Systems (ISSCS), 2013, pp. 1–4.

2012 (5)

Meziou, L.; Histace, A.; Dray, X.; Romain, O.; Granado, B. Segmentation of video capsule endoscopic images using alpha-divergence based active contour method. Computer Assisted Radiology and Surgery, 2012, pp. S325–S326.

Eskandari, H.; Talebpour, A.; Alizadeh, M.; Soltanian-Zadeh, H. Polyp detection in wireless capsule endoscopy images by using region-based active contour model. Biomedical Engineering (ICBME), 2012 19th Iranian Conference of. IEEE, 2012, pp. 305–308.

Condessa, F.; Bioucas-Dias, J. Segmentation and detection of colorectal polyps using local polynomial approximation. In Image analysis and recognition; Springer, 2012; pp. 188–197.

Li, B.; Meng, M.Q.H. Automatic polyp detection for wireless capsule endoscopy images. Expert Systems with Applications 2012, 39, 10952–10958.

Zhao, Q.; Dassopoulos, T.; Mullin, G.E.; Meng, M.; Kumar, R. A decision fusion strategy for polyp detection in capsule endoscopy. In Studies in health technology and informatics; Westwood, J.; Westwood, S.; Fellander-Tsai, L.; Haluck, R.; Robb, R.; Senger, S.; Vosburgh, K., Eds.; IOS Press, 2012; Vol. 173, pp. 559–565. Proceedings of the Medicine Meets Virtual Reality conference (MMVR19), Newport Beach, CA, USA, February 2012.

2011 (6)

Hwang, S. Bag-of-visual-words approach to abnormal image detection in wireless capsule endoscopy videos. In Advances in Visual Computing; Springer, 2011; pp. 320–327.

Hwang, S. Bag-of-visual-words approach based on SURF features to polyp detection in wireless capsule endoscopy videos. World Congress in Computer Science, Computer Engineering, and Applied Computing, 2011.

Zhao, Q.; Dassopoulos, T.; Mullin, G.; Hager, G.; Meng, M.Q.; Kumar, R. Towards integrating temporal information in capsule endoscopy image analysis. Engineering in Medicine and Biology Society, EMBC, 2011 Annual International Conference of the IEEE. IEEE, 2011, pp. 6627–6630.

Zhao, Q.; Meng, M.Q.H. Polyp detection in wireless capsule endoscopy images using novel color texture features. Intelligent Control and Automation (WCICA), 2011 9th World Congress on. IEEE, 2011, pp. 948–952.

Karargyris, A.; Bourbakis, N. Detection of small bowel polyps and ulcers in wireless capsule endoscopy videos. Biomedical Engineering, IEEE Transactions on 2011, 58, 2777–2786.

Figueiredo, P.N.; Figueiredo, I.N.; Surya Prasath.; Tsai, R. Automatic polyp detection in Pillcam Colon 2  capsule images and videos: Preliminary feasibility report. Diagnostic and Therapeutic Endoscopy 2011, 2011, 7pp. Article ID 182435.

2010 (2)

Nawarathna, R.D.; Oh, J.; Yuan, X.; Lee, J.; Tang, S.J. Abnormal image detection using texton method in wireless capsule endoscopy videos. In Medical Biometrics; Springer, 2010; pp. 153–162.

Hwang, S.; Celebi, M.E. Polyp detection in wireless capsule endoscopy videos based on image segmentation and geometric feature. Acoustics Speech and Signal Processing (ICASSP), 2010 IEEE International Conference on. IEEE, 2010, pp. 678–681.

2009 (3)

Karargyris, A.; Bourbakis, N. Identification of polyps in wireless capsule endoscopy videos using log Gabor filters. IEEE/NIH Life Science Systems and Applications Workshop (LiSSA); , 2009; pp. 143–147.

Li, B.; Fan, Y.; Meng, M.Q.; Qi, L. Intestinal polyp recognition in capsule endoscopy images using color and shape features. Robotics and Biomimetics (ROBIO), 2009 IEEE International Conference on. IEEE, 2009, pp. 1490–1494.

Li, B.; Meng, M.Q.; Xu, L. A comparative study of shape features for polyp detection in wireless capsule endoscopy images. Engineering in Medicine and Biology Society, 2009. EMBC 2009. Annual International Conference of the IEEE, 2009, pp. 3731–3734.

2008 (0)

---

2007 (1)

Kodogiannis, V.; Boulougoura, M. An adaptive neurofuzzy approach for the diagnosis in wireless capsule endoscopy imaging. International Journal of Information Technology 2007, 13, 46–56.

Theses: (Theses PDFs are available online at their respective university websites - please email if you require a copy)

Vilariño, F. A machine learning approach for intestinal motility assessment with capsule endoscopy. PhD thesis, Universitat Autònoma de Barcelona, Spain, 2006.

Mackiewicz, M. Computer-assisted wireless capsule endoscopy video analysis. PhD thesis, University of East Anglia, UK, 2007.

Li, B., A study on computer aided diagnosis for wireless capsule endoscopy images. PhD thesis, the Chinese University of HongKong, September, 2008.

Karargyris, A. A novel synergistic diagnosis methodology for identifying abnormalities in wireless capsule endoscopy videos. PhD thesis, Wright State University, 2010.

Chen, Y. A computer-aided diagnostic system for wireless capsule endoscopy. PhD thesis, University of Bridgeport, USA, 2012.

Zhao, Q. Synopsis of video streams and its application to computer aided diagnosis for GI tract abnormalities based on wireless capsule endoscopy (CE) video. PhD thesis, The Chinese University of Hong Kong, 2012.

Filip, D. Self–stabilizing capsule endoscope for early detection and biopsy of colonic polyps. PhD thesis, University of Calgary, Canada, 2013.

Ruano Balseca, J.A. Estimation of gastrointestinal polyp size in video endoscopy. PhD thesis, Universidad Nacional de Colombia Sede Bogotá, Columbia 2013.

Nawarathna, R.D. Detection of temporal events and abnormal images for quality analysis in endoscopy videos. PhD thesis, University of North Texas, USA 2013.

Hadjilucas, L. Framework for the detection and classification of colorectal polyps. PhD thesis, Imperial College London, UK, 2013.

Drozdzal, M. Sequential image analysis for computer-aided wireless endoscopy. PhD thesis, Universitat de Barcelona, Spain, 2014.

Albisser, Z. Computer-aided screening of capsule endoscopy videos. PhD thesis, University of Oslo, Norway, 2015.

Bjørnevik, A.S. Localization and tracking of intestinal paths for wireless capsule endoscopy. PhD thesis, Norwegian University of Science and Technology (NTNU), Norway, 2015.

Available downloads (from 2017 review):

1. PDF of the review paper version 1 (2017): Published paper at Journal of Imaging.

Preprint version @ arXiv:1609.01915

2. Bibliography: BibTex (Please email me if you require a copy)

3. Each reference in PDF - Zip file ~1.5 Gb (Too big to attach here, please email me if you require a copy, we can send a Google Drive link for the whole data-set or send a particular paper's PDF via email)

4. Supplementary -  Download at Journal of Imaging

Complete Bibliography - Compiled and Continuously Updated (as of June 2017 Reference [1]) - New Update in Progress (as of February 2023 Reference [2])

This page is continuously updated with relevant references/links.

Last updated: June 2017 ~ New Update in Progress February 2023

 Please cite the main Reference [1] (published in March 2017) below if you use any of the materials here.

Reference:

[1] V. B. S. Prasath. Polyp detection and segmentation from video capsule endoscopy: A review. Journal of Imaging, 3(1), March 2017. doi:10.3390/jimaging3010001. Preliminary version at arXiv:1609.01915.

[2] V. B. S. Prasath. Polyp detection and segmentation from video capsule endoscopy: An updated review. Can AI help solve 'finding a needle in a haystack' problem?. In preparation, 2025. Preliminary version at arXiv:25xx.abcde.

Notes:

This page (at:https://www.prasathlab.com/research/endoscopy/ce-polyps) is a, chronologically ordered, bibliography of scientific publications on the polyp detection and segmentation algorithms for video capsule endoscopy, compiled and continuously updated by Surya Prasath. If you know of a related work in any form (preprint, reprint, journal publication, conference proceedings, technical report, abstract or poster, book chapter, thesis, patent, unpublished report, etc.) that should be included here please write to us on: prasatsa at uc dot edu (or at surya.iit at gmail dot com) with full bibliographic details, a DOI if available, and a PDF copy of the work if possible. If any publication in this area is missed/overlooked please let us know via email and our sincere apologies for missing it.

Copyright notice: Downloads are supplied for personal academic use only. A download is considered equivalent to a preprint or reprint request. Use is granted consistent with fair-use of a preprint or reprint. By downloading any of the materials here you are agreeing to these terms.

Other Useful Links:

1. Polyp detection in Colonoscopy: polyp.grand-challenge.org - Challenges - ASU - Mayo Clinic

2. CVC colon DB - http://mv.cvc.uab.es/projects/colon-qa/cvccolondb - Colonoscopy polyps image dataset - Machine Vision - Computer Vision Center - Universitat Autònoma de Barcelona (UAB)

Back Endoscopy Projects Main Page. Back to Research Page.