Computational software can enhance the diagnostic yield of video capsule endoscopy (VCE), both in terms of efficiency and accuracy
Despite increasing activity in information technology (IT) research worldwide, the translation of this information to clinical practice has been limited
The development of intelligent software systems requires close collaboration between medical and IT scientists at a laboratory level
Public sharing of anonymized and annotated VCE image and video data is essential
Video capsule endoscopy (VCE) has revolutionized the diagnostic work-up in the field of small bowel diseases. Furthermore, VCE has the potential to become the leading screening technique for the entire gastrointestinal tract. Computational methods that can be implemented in software can enhance the diagnostic yield of VCE both in terms of efficiency and diagnostic accuracy. Since the appearance of the first capsule endoscope in clinical practice in 2001, information technology (IT) research groups have proposed a variety of such methods, including algorithms for detecting haemorrhage and lesions, reducing the reviewing time, localizing the capsule or lesion, assessing intestinal motility, enhancing the video quality and managing the data. Even though research is prolific (as measured by publication activity), the progress made during the past 5 years can only be considered as marginal with respect to clinically significant outcomes. One thing is clear—parallel pathways of medical and IT scientists exist, each publishing in their own area, but where do these research pathways meet? Could the proposed IT plans have any clinical effect and do clinicians really understand the limitations of VCE software? In this Review, we present an in-depth critical analysis that aims to inspire and align the agendas of the two scientific groups.
This is a preview of subscription content, access via your institution
Subscribe to this journal
Receive 12 print issues and online access
$209.00 per year
only $17.42 per issue
Rent or buy this article
Prices vary by article type
Prices may be subject to local taxes which are calculated during checkout
Similar content being viewed by others
Wang, A. et al. Wireless capsule endoscopy. Gastrointest. Endosc. 78, 805–815 (2013).
Fisher, L. R. & Hasler, W. L. New vision in video capsule endoscopy: current status and future directions. Nat. Rev. Gastroenterol. Hepatol. 9, 392–405 (2012).
Ciuti, G., Menciassi, A. & Dario, P. Capsule endoscopy: from current achievements to open challenges. IEEE Rev. Biomed. Eng. 4, 59–72 (2011).
Koulaouzidis, A., Rondonotti, E. & Karargyris, A. Small-bowel capsule endoscopy: a ten-point contemporary review. World J. Gastroenterol. 19, 3726–3746 (2013).
Lo, S. K. How should we do capsule reading? Tech. Gastrointest. Endosc. 8, 146–148 (2006).
Eliakim, R. & Magro, F. Imaging techniques in IBD and their role in follow-up and surveillance. Nat. Rev. Gastroenterol. Hepatol. 11, 722–736 (2014).
Zheng, Y., Hawkins, L., Wolff, J., Goloubeva, O. & Goldberg E. Detection of lesions during capsule endoscopy: physician performance is disappointing. Am. J. Gastroenterol. 107, 554–560 (2012).
Rondonotti, E. et al. Can we improve the detection rate and interobserver agreement in capsule endoscopy? Dig. Liver Dis. 44, 1006–1011 (2012).
Lewis, B., Eisen, G. & Friedman, S. A pooled analysis to evaluate results of capsule endoscopy trials. Endoscopy 39, 303–308 (2005).
Karkanis, S. A., Iakovidis, D. K., Maroulis, D. E., Magoulas, G. D. & Theofanous, N. Tumor recognition in endoscopic video images using artificial neural network architectures. In Proc. 26th Euromicro Conference Vol. 2, 423–429 (2000).
Karkanis, S. A., Iakovidis, D. K., Maroulis, D. E., Karras, D. A. & Tzivras, M. Computer-aided tumor detection in endoscopic video using color wavelet features. IEEE Trans. Inf. Technol. Biomed. 7, 141–152 (2003).
Liedlgruber, M. & Uhl, A. Computer-aided decision support systems for endoscopy in the gastrointestinal tract: a review. IEEE Rev. Biomed. Eng. 4, 73–88 (2011).
Fisher, M. & Mackiewicz, M. in Color Medical Image Analysis Vol. 6 (eds Celebi, M. E. & Schaefer, G.) 129–144 (Springer, 2013).
Bovik, A. C. Handbook of Image and Video Processing (Academic Press, 2010).
Nixon, M., Nixon, M. S. & Aguado, A. S. Feature Extraction and Image Processing for Computer Vision (Academic Press, 2012).
Theodoridis, S. & Koutroumbas, K. Pattern Recognition (Academic Press, 2008).
Iakovidis, D. K. & Koulaouzidis, A. Automatic lesion detection in capsule endoscopy based on color saliency: closer to an essential adjunct for reviewing software. Gastrointest. Endosc. 80, 877–883 (2014).
Iakovidis, D. K. Software engineering applications in gastroenterology. Global J. Gastroenterol. Hepatol. 2, 11–18 (2014).
Rockey, D. C. Occult and obscure gastrointestinal bleeding: causes and clinical management. Nat. Rev. Gastroenterol. Hepatol. 7, 265–279 (2010).
Buscaglia, J. M. et al. Performance characteristics of the suspected blood indicator feature in capsule endoscopy according to indication for study. Clin. Gastroenterol. Hepatol. 6, 298–301 (2008).
Park, S. C. et al. Sensitivity of the suspected blood indicator: an experimental study. World J. Gastroenterol. 18, 4169–4174 (2012).
D'Halluin, P. N. et al. Does the “Suspected Blood Indicator” improve the detection of bleeding lesions by capsule endoscopy? Gastrointest. Endosc. 61, 243–249 (2005).
Boulougoura, M., Wadge, E, Kodogiannis, V. & Chowdrey, H. S. Intelligent systems for computer-assisted clinical endoscopic image analysis. In Proc. 2nd IASTED International Conference on Biomedical Engineering 405–408 (2004).
Lv, G., Yan, G. & Wang, Z. Bleeding detection in wireless capsule endoscopy images based on color invariants and spatial pyramids using support vector machines. In Engineering in Medicine and Biology Society, EMBC, Annual International Conference of the IEEE 6643–6646 (2011).
Sainju, S., Bui, F. M. & Wahid, K. A. Automated bleeding detection in capsule endoscopy videos using statistical features and region growing. J. Med. Syst. 38, 25 (2014).
Fu, Y., Zhang, W., Mandal, M. & Meng, M. Q. Computer-aided bleeding detection in WCE video. IEEE J. Biomed. Health Inform. 18, 636–642 (2014).
Hwang S., Oh, J., Cox, J., Tang, S. J. & Tibbals, H. F. Blood detection in wireless capsule endoscopy using expectation maximization clustering. In Proc. SPIE: Medical Imaging 61441P–61441P (2006).
Jung, Y. S. et al. Automatic patient-adaptive bleeding detection in a capsule endoscopy. In Proc. SPIE: Medical Imaging 72603T–72603T (2009).
Mäenpää, T. & Pietikäinen, M. Classification with color and texture: jointly or separately? Pattern Recognit. 37, 1629–1640 (2004).
Mackiewicz, M. W., Fisher, M. & Jamieson, C. Bleeding detection in wireless capsule endoscopy using adaptive colour histogram model and support vector classification. In Proc. SPIE Medical Imaging 69140R–69140R (2008).
Szczypinski, P., Klepaczko, A., Pazurek, M. & Daniel, P. Texture and color based image segmentation and pathology detection in capsule endoscopy videos. Comput. Methods Programs Biomed. 113, 396–411 (2014).
Pan G., Yan G., Qiu X. & Cui J. Bleeding detection in wireless capsule endoscopy based on probabilistic neural network. J. Med. Syst. 35, 1477–1484 (2011).
Figueiredo, I. N., Kumar, S., Leal, C. & Figueiredo, P. N. Computer-assisted bleeding detection in wireless capsule endoscopy images. Comput. Methods Biomechan. Biomed. Eng. Imaging Vis. 1, 198–210 (2013).
Fawcett, T. An introduction to ROC analysis. Pattern Recognit. Letters 27, 861–874 (2006).
Alotaibi, S., Qasim, S., Bchir, O. & Ismail, M. M. Empirical comparison of visual descriptors for multiple bleeding spots recognition in wireless capsule endoscopy video. Computer Analysis Images Patterns 8048, 402–407 (2013).
Karargyris, A. & Bourbakis, N. Detection of small bowel polyps and ulcers in wireless capsule endoscopy videos. IEEE Trans. Biomed. Eng. 58, 2777–2786 (2011).
Tanaka, M. et al. A new instrument for measurement of gastrointestinal mucosal color. Dig. Endosc. 8, 139–146 (1996).
Kudo, S. et al. Colonoscopic diagnosis and management of nonpolypoid early colorectal cancer. World J. Surg. 24, 1081–1090 (2000).
Maroulis, D. E., Iakovidis, D. K., Karkanis, S. A. & Karras, D. A. CoLD: a versatile detection system for colorectal lesions in endoscopy video-frames. Comput. Methods Programs Biomed. 70, 151–166 (2003).
Häfner, M. et al. Computer-assisted pit-pattern classification in different wavelet domains for supporting dignity assessment of colonic polyps. Pattern Recognit. 42, 1180–1191 (2009).
Cui, L. et al. Detection of lymphangiectasia disease from wireless capsule endoscopy images with adaptive threshold. In Proc. 8th World Congress on Intelligent Control and Automation 3088–3093 (2010).
Ciaccio, E. J., Tennyson, C. A., Bhagat, G., Lewis, S. K. & Green, P. H. Classification of videocapsule endoscopy image patterns: Comparative analysis between patients with celiac disease and normal individuals. Biomed. Eng. Online 9, 44 (2010).
Saurin, J. C. et al. Diagnostic value of endoscopic capsule in patients with obscure digestive bleeding: blinded comparison with video push-enteroscopy. Endoscopy 35, 576–584 (2003).
Romain, O. et al. Towards a multimodal wireless video capsule for detection of colonic polyps as prevention of colorectal cancer. In Proc. 13th International Conference on Bioinformatics and Bioengineering 1–6 (2013).
Li, B. P. & Meng, M. Q. Comparison of several texture features for tumor detection in CE images. J. Med. Syst. 36, 2463–2469 (2012).
Charisis, V. S., Hadjileontiadis, L. J., Liatsos, C. N., Mavrogiannis, C. C. & Sergiadis, G. D. Capsule endoscopy image analysis using texture information from various colour models. Comput. Methods Programs Biomed. 107, 61–74 (2012).
Chen, G., Bui, T. D., Krzyzak, A. & Krishnan, S. Small bowel image classification based on Fourier-Zernike moment features and canonical discriminant analysis. Pattern Recognit. Image Analysis 23, 211–216 (2013).
Li, B. & Meng, M. Q. Automatic polyp detection for wireless capsule endoscopy images. Expert Syst. Appl. 39, 10952–10958 (2012).
Li, B. & Meng, M. Q. Tumor recognition in wireless capsule endoscopy images using textural features and SVM-based feature selection. IEEE Trans. Inf. Technol. Biomed. 16, 323–329 (2012).
Li, B., Meng, M. Q. & Lau, J. Y. Computer-aided small bowel tumor detection for capsule endoscopy. Artif. Intell. Med. 52, 11–16 (2011).
Chen, H., Chen, J., Peng Q., Sun G. & Gan T. Automatic hookworm image detection for wireless capsule endoscopy using hybrid color gradient and contourlet transform. In Proc. 6th International Conference on Biomedical Engineering and Informatics 116–120 (2013).
Yu, L., Yuen, P. C. & Lai, J. Ulcer detection in wireless capsule endoscopy images. In Proc. 21st International Conference on Pattern Recognition 45–48 (2012).
Hwang, S. Bag-of-visual-words approach to abnormal image detection in wireless capsule endoscopy videos. Advances Visual Computing 6939, 320–327 (2011).
Chen, Y. & Lee, J. Ulcer detection in wireless capsule endoscopy video. In Proc. 20th ACM International Conference on Multimedia 1181–1184 (2012).
Sikora, T. The MPEG-7 visual standard for content description-an overview. IEEE Trans. Circuits Syst. Video Technol. 11, 696–702 (2001).
Kumar, R. et al. Assessment of Crohn's disease lesions in wireless capsule endoscopy images. IEEE Trans. Biomed. Eng. 59, 355–362 (2012).
David, E., Boia, R., Malaescu, A. & Carnu, M. Automatic colon polyp detection in endoscopic capsule images. In Proc. International Symposium on Signals, Circuits and Systems 1–4 (2013).
Mamonov, A. V., Figueiredo, I. N., Figueiredo, P. N. & Tsai, Y. H. Automated polyp detection in colon capsule endoscopy. IEEE Trans. Biomed. Eng. 33, 1488–1502 (2014).
Iakovidis, D., Tsevas, S., Maroulis D. & Polydorou, A. Unsupervised summarisation of capsule endoscopy video. In Proc. 4th International IEEE Conference Vol. 1, 3–15 (2008).
Iakovidis, D. K., Tsevas, S. & Polydorou, A. Reduction of capsule endoscopy reading times by unsupervised image mining. Comput. Med. Imaging Graph. 34, 471–478 (2010).
Zhao, Q. & Meng, M. H. A strategy to abstract WCE video clips based on LDA. In Proc. IEEE International Conference on Robotics and Automation 4145–4150 (2011).
Yuan, Y. & Meng, M. Q. Hierarchical key frames extraction for WCE video. In Proc. IEEE International Conference on Mechatronics and Automation 225–229 (2013).
Ismail, M., Bchir, O. & Emam, A. Z. Endoscopy video summarization based on unsupervised learning and feature discrimination. IEEE Xplore [online], (2013).
Fan, Y., Meng, M. H. & Li, B. A novel method for informative frame selection in wireless capsule endoscopy video. In Proc. Annual International Conference of the IEEE: Engineering in Medicine and Biology Society 4864–4867 (2011).
HajiMaghsoudi, O., Talebpour, A., Soltanian-Zadeh, H. & Soleimani, H. A. Automatic informative tissue's discriminators in WCE. In Proc. IEEE International Conference on Imaging Systems and Techniques 18–23 (2012).
Segui, S. et al. Categorization and segmentation of intestinal content frames for wireless capsule endoscopy. IEEE Trans. Inf. Technol. Biomed. 16, 1341–1352 (2012).
Sun, Z., Li, B., Zhou, R., Zheng, H. & Meng, M. Q. Removal of non-informative frames for wireless capsule endoscopy video segmentation. In Proc. IEEE International Conference on Automation and Logistics 294–299 (2012).
Fu, Y. et al. Key-frame selection in WCE video based on shot detection. In Proc. 10th World Congress on Intelligent Control and Automation 5030–5034 (2012).
Liu, H. et al. Wireless capsule endoscopy video reduction based on camera motion estimation. J. Digi. Imaging 26, 287–301 (2013).
Lee, H. G., Choi, M. K., Shin, B. S. & Lee, S. C. Reducing redundancy in wireless capsule endoscopy videos. Comput. Biol. Med. 43, 670–682 (2013).
Bay, H., Ess, A., Tuytelaars, T. & Van Gool, L. Speeded-up robust features (SURF). Comput. Vis. Image Underst. 110, 346–359 (2008).
Chen, Y., Lan, Y. & Ren, H. Trimming the wireless capsule endoscopic video by removing redundant frames. In Proc. 8th International Conference on Wireless Communications, Networking and Mobile Computing 1–4 (2012).
Mackiewicz, M., Berens, J. & Fisher, M. Wireless capsule endoscopy color video segmentation. IEEE Trans. Med. Imaging 27, 1769–1781 (2008).
Cunha, J. S., Coimbra, M., Campos, P. & Soares, J. M. Automated topographic segmentation and transit time estimation in endoscopic capsule exams. IEEE Trans. Med. Imaging 27, 19–27 (2008).
Gallo, G. & Granata, E. WCE video segmentation using textons. Proc. SPIE http://dx.doi.org/10.1117/12.840690.
Given Imaging Wireless capsule endoscopy software [online], (2014).
Koulaouzidis, A., Iakovidis, D. K., Karargyris, A. & Plevris, J. N. Optimizing lesion detection in small-bowel capsule endoscopy: from present problems to future solutions. Expert Rev. Gastroenterol. Hepatol. 9, 217–235 (2015).
Günther, U., Daum, S., Zeitz, M. & Bojarski, C. Capsule endoscopy: comparison of two different reading modes. Int. J. Colorectal Dis. 27, 521–525 (2012).
Koulaouzidis, A., Smirnidis, A., Douglas, S. & Plevris, J. N. QuickView in small-bowel capsule endoscopy is useful in certain clinical settings, but QuickView with Blue Mode is of no additional benefit. Eur. J. Gastroenterol. Hepatol. 24, 1099–1104 (2012).
Vu, H. et al. Controlling the display of capsule endoscopy video for diagnostic assistance. IEICE Trans. Inf. Syst. 92, 512–528 (2009).
Chu, X. et al. Epitomized summarization of wireless capsule endoscopic videos for efficient visualization. Med. Image Comput. Comput. Assist. Interv. 13, 522–529 (2010).
Iakovidis, D. K, Spyrou, E. & Diamantis, D. Efficient homography-based video visualization for wireless capsule endoscopy. In Proc. 13th International Conference on Bioinformatics and Bioengineering 1–4 (2013).
Szeliski, R. Image alignment and stitching: a tutorial. Foundations Trends Computer Graphics Vision. 2, 1–104 (2006).
Than, T. D., Alici, G., Zhou, H. & Li, W. A review of localization systems for robotic endoscopic capsules. IEEE Trans. Biomed. Eng. 59, 2387–2399 (2012).
Li, X., Chen, H., Dai, J., Gao, Y. & Ge, Z. Predictive role of capsule endoscopy on the insertion route of double-balloon enteroscopy. Endoscopy 41, 762–766 (2009).
Pedersen, P. B., Bar-Shalom, D., Baldursdottir, S., Vilmann, P. & Müllertz, A. Feasibility of capsule endoscopy for direct imaging of drug delivery systems in the fasted upper-gastrointestinal tract. Pharm. Res. 31, 1–10 (2014).
van der Stap N., van der Heijden, F. & Broeders, I. A. Towards automated visual flexible endoscope navigation. Surg. Endosc. 27, 3539–3547 (2013).
Marya, N., Karellas, A., Foley, A., Roychowdhury, A. & Cave, D. Computerized 3-dimensional localization of a video capsule in the abdominal cavity: validation by digital radiography. Gastrointest. Endosc. 79, 669–674 (2014).
Scaramuzza, D. & Fraundorfer, F. Visual odometry [tutorial]. IEEE Robotics & Automation Magazine 18, 80–92 (2011).
Berens, J., Mackiewicz, M. & Bell, D. Stomach, intestine, and colon tissue discriminators for wireless capsule endoscopy images. Proc. SPIE 5747, Medical Imaging Image Processing http://dx.doi.org/10.1117/12.594799.
Vu, H. et al. Color analysis for segmenting digestive organs in VCE. In Proc. 20th International Conference on Pattern Recognition (ICPR) 2468–2471 (2010).
Marques, N., Dias, E., Cunha, J. & Coimbra, M. Compressed domain topographic classification for capsule endoscopy. In Proc. Engineering in Medicine and Biology Society, EMBC, Annual International Conference of the IEEE 6631–6634 (2011).
Shen, Y., Guturu, P. & Buckles, B. P. Wireless capsule endoscopy video segmentation using an unsupervised learning approach based on probabilistic latent semantic analysis with scale invariant features. IEEE Trans. Inf. Technol. Biomed. 16, 98–105 (2012).
Zhou, R., Li, B., Zhu, H. & Meng, M. Q. A novel method for capsule endoscopy video automatic segmentation. In Proc. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 3096–3101 (2013).
Nistér, D., Naroditsky, O. & Bergen J. Visual odometry. In Proc. IEEE Computer Society Conference 1–652 (2004).
Karargyris, A. & Koulaouzidis, A. Capsule-odometer: a concept to improve accurate lesion localisation. World J. Gastroenterol. 19, 5943 (2013).
Karargyris, A. & Koulaouzidis. A. OdoCapsule: next generation wireless capsule endoscopy with accurate localization and video stabilization. IEEE Trans. Biomed. Eng. http://dx.doi.org/10.1109/TBME.2014.2352493.
Szczypinski, P. M., Sriram, R. D., Sriram, P. V. & Reddy, D. N. A model of deformable rings for interpretation of wireless capsule endoscopic videos. Med. Image Anal. 13, 312–324 (2009).
Liu, L., Hu, C., Cai, W. & Meng, M. H. Capsule endoscope localization based on computer vision technique. In Proc. Engineering in Medicine and Biology Society, EMBC 2009. Annual International Conference of the IEEE 3711–3714 (2009).
Bao, G., Ye, Y., Khan, U., Zheng, X. & Pahlavan, K. Modeling of the movement of the endoscopy capsule inside GI tract based on the captured endoscopic images. In Proc. IEEE International Conference on Modeling, Simulation and Visualization Methods, MSV Vol. 12 (2012).
Spyrou, E. & Iakovidis, D. K. Video-based measurements for wireless capsule endoscope tracking. Meas. Sci. Technol. 25, 015002 (2014).
Bao, G., Mi, L. & Pahlavan, K. Emulation on motion tracking of endoscopic capsule inside small intestine. In Proc. 14th International Conference on Bioinformatics and Computational Biology, Las Vegas (2013).
Bao, G. & Pahlavan, K. Motion estimation of the endoscopy capsule using region-based Kernel SVM classifier. In Proc. IEEE International Conference on Electro/Information Technology (EIT) 1–5 (2013).
Talley, N. J. Decade in review—FGIDs: 'Functional' gastrointestinal disorders—a paradigm shift. Nat. Rev. Gastroenterol. Hepatol. 11, 649–650 (2014).
Rodriguez, L. & Nurko, S. in Clinical Management of Intestinal Failure (eds Duggan, C. P., Gura, K. M. & Jaksic, T.) 31 (2011).
Lee, Y. Y., Erdogan, A. & Rao, S. S. How to assess regional and whole gut transit time with wireless motility capsule. J. Neurogastroenterol. Motil. 20, 265–270 (2014).
Malagelada, C. et al. New insight into intestinal motor function via noninvasive endoluminal image analysis. Gastroenterology 135, 1155–1162 (2008).
Kellow, J. E. et al. Principles of applied neurogastroenterology: physiology/motility–sensation. Gut 45, (Suppl. 2), II17–II24 (1999).
Hansen, M. Small intestinal manometry. Physiol. Res. 51, 541–556 (2002).
Spyridonos, P., Vilariño, F., Vitria, J. & Radeva, P. in Advanced Concepts for Intelligent Vision Systems (eds Blanc-Talon, J., Philips, W., Popescu, D. & Scheunders, P.) 531–537 (Springer, 2005).
Vilarino, F. et al. Intestinal motility assessment with video capsule endoscopy: automatic annotation of phasic intestinal contractions. IEEE Trans. Med. Imaging 29, 246–259 (2010).
Segui, S. et al. Detection of wrinkle frames in endoluminal videos using betweenness centrality measures for images. IEEE J. Biomed. Health Inform. 18, 1831–1838 (2014).
Drozdzal, M. et al. Adaptable image cuts for motility inspection using WCE. Comput. Med. Imaging Graph. 37, 72–80 (2013).
Li, B. & Meng, M. Q. Wireless capsule endoscopy images enhancement via adaptive contrast diffusion. J. Vis. Commun. Image Represent. 23, 222–228 (2012).
Ramaraj, M., Raghavan, S. & Khan, W. A. Homomorphic filtering techniques for WCE image enhancement. In Proc. IEEE International Conference on Computational Intelligence and Computing Research (ICCIC) 1–5 (2013) (2013).
Vu, H. et al. in Abdominal Imaging Computational and Clinical Applications (eds Yoshida, H., Sakas, G. & Linguraru, M. G.) 35–43 (Springer, 2012).
Okuhata, H., Nakamura, H., Hara, S., Tsutsui, H. & Onoye T. Application of the real-time Retinex image enhancement for endoscopic images. In Proc. Engineering in Medicine and Biology Society (EMBC), 35th Annual International Conference of the IEEE 3407–3410 (2013).
Gopi, V. P. & Palanisamy, P. Capsule endoscopic image denoising based on double density dual tree complex wavelet transform. Int. J. Imag. Robot. 9, 48–60 (2013).
Liu, H., Lu, W. S. & Meng, M. H. De-blurring wireless capsule endoscopy images by total variation minimization. In Proc. IEEE Pacific Rim Conference on Communications, Computers and Signal Processing (PacRim) 102–106 (2011).
Karargyris, A. & Bourbakis, N. An elastic video interpolation methodology for wireless capsule endoscopy videos. In Proc. IEEE International Conference on BioInformatics and BioEngineering (BIBE) 38–43 (2010).
Häfner, M., Liedlgruber, M. & Uhl, A. POCS-based super-resolution for HD endoscopy video frames. In Proc. Computer Based Medical Systems 185–190 (2013).
Spyrou, E., Diamantis, D. & Iakovidis, D. K. Panoramic visual summaries for efficient reading of capsule endoscopy videos. In Proc. 8th International Workshop on Semantic and Social Media Adaptation and Personalization (SMAP) 41–46 (2013).
Rondonotti, E. et al. Utility of 3-dimensional image reconstruction in the diagnosis of small-bowel masses in capsule endoscopy (with video). Gastroint. Endosc. 80, 642–651 (2014).
Karargyris, A. & Bourbakis, N. Three-dimensional reconstruction of the digestive wall in capsule endoscopy videos using elastic video interpolation. IEEE Trans. Med. Imaging 30, 957–971 (2011).
Koulaouzidis, A. et al. Three-dimensional representation software as image enhancement tool in small-bowel capsule endoscopy: a feasibility study. Dig. Liver Dis. 45, 909–914 (2013).
d'Orazio, L. et al. Multimodal and multimedia image analysis and collaborative networking for digestive endoscopy. IRBM 35, 88–93 (2014).
Genta, R. M. & Sonnenberg, A. Big data in gastroenterology research. Nat. Rev. Gastroenterol. Hepatol. 11, 386–390 (2014).
Mell, P. & Grance, T. The NIST definition of cloud computing. The ACM Digital Library [online], (2010).
Khan, T. & Wahid, K. Low-complexity colour-space for capsule endoscopy image compression. Electronics Letters 47, 1217–1218 (2011).
Mehmood, I., Sajjad, M. & Baik, S. W. Video summarization based tele-endoscopy: a service to efficiently manage visual data generated during wireless capsule endoscopy procedure. J. Med. Syst. 38, 1–9 (2014).
Torres, J. S., Damian Segrelles Quilis, J., Espert, I. B. & García, V. H. Improving knowledge management through the support of image examination and data annotation using DICOM structured reporting. J. Biomed. Inform. 45, 1066–1074 (2012).
Iakovidis, D., Goudas, T., Smailis, C. & Maglogiannis, I. Ratsnake: a versatile image annotation tool with application to computer-aided diagnosis. ScientificWorldJournal http://dx.doi.org/10.1155/2014/286856.
Drozdzal, M. et al. in Pattern Recognition and Image Analysis (eds Vitrià, J., Sanches, J. M. & Mario Hernández, M.) 143–150 (Springer, 2011).
Müller, H. & Deserno, T. M. in Biomedical Image Processing (ed. Deserno, T. M.) 471–494 (Springer, 2011).
Hu, W., Xie, N., Li, L., Zeng, X. & Maybank, S. A survey on visual content-based video indexing and retrieval. IEEE Trans. Syst. Man Cybern. 41, 797–819 (2011).
Garaiman, D. D. & Saftoiu, A. A comparative study for methods of content search in multimedia databases with endoscopic images. Current Health Sci. J. 37, 86–88 (2011).
André, B., Vercauteren, T. & Ayache, N. in Medical Content-Based Retrieval for Clinical Decision Support (eds Müller, H., Hayit Greenspan, H. & Syeda-Mahmood, T.) 12–23 (Springer, 2012).
Wu, X. W., Yang, Y. B. & Yu, W. Y. Content-based medical image retrieval system for color endoscopic images. Advanced Mat. Res. 798, 1022–1025 (2013).
Iddan, G., Meron, G., Glukhovsky, A. & Swain, P. Wireless capsule endoscopy. Nature 405, 417 (2000).
Compton, C. C. et al. AJCC Cancer Staging Atlas 287–295 (Springer, 2012).
Carrion, A. F., Hindi, M., Molina, E. & Barkin, J. S. Ileal lines: a marker of the ileocecal valve on wireless capsule endoscopy. Gastrointest. Endosc. 79, 871–872 (2014).
Soper, T. D., Porter, M. P. & Seibel, E. J. Surface mosaics of the bladder reconstructed from endoscopic video for automated surveillance. IEEE Trans. Biomed. Eng. 59, 1670–1680 (2012).
Rey, J. F. et al. Blinded nonrandomized comparative study of gastric examination with a magnetically guided capsule endoscope and standard videoendoscope. Gastrointest. Endosc. 75, 373–381 (2012).
Iakovidis, D. K. et al. Towards intelligent capsules for robust wireless endoscopic imaging of the gut. In Proc. IEEE-IST Conference 95–100 (2014).
Hripcsak, G. et al. Health data use, stewardship, and governance: ongoing gaps and challenges: a report from AMIA's Health Policy 2012 Meeting. J. Am. Med. Inform Assoc. 21, 204–211 (2014).
Sliker, L. J. & Ciuti, G. Flexible and capsule endoscopy for screening, diagnosis and treatment. Expert Rev. Med. Devices 11, 649–666 (2014).
Aihara, H., Ikeda, K. & Tajiri, H. Image-enhanced capsule endoscopy based on the diagnosis of vascularity when using a new type of capsule. Gastrointest. Endosc. 73, 1274–1279 (2011).
Ryu, C. B., Song, J. Y., Lee, M. S. & Shim, C. S. Does capsule endoscopy with Alice improves visibility of small bowel lesions? Gastrointest. Endosc. 77 (Suppl), AB466 (2013).
Spada, C., Hassan, C. & Costamagna, G. Virtual chromoendoscopy: will it play a role in capsule endoscopy? Dig. Liver Dis. 43, 927–928 (2011).
Given Imaging. Capsule endoscopy [online], (2014).
Koulaouzidis, A. & Iakovidis, D. K. KID, a capsule endoscopy database for medical decision support [online], (2014).
University of Aveiro. Capview [online], (2010).
Gastrolab [online], (2014).
World Endoscopy Organization. WEO Clinical Endoscopy Atlas [online], (2014).
El Salvador atlas of gastrointestinal endoscopy [online], (2014).
Atlas of gastroenterological endoscopy [online], (2014).
A.K. has received research support from Given Imaging and SynMed UK, lecture honoraria from Dr Falk Pharma UK, and travel support from Abbott, Dr Falk Pharma UK, Almirall and MSD. D.K.I. declares no competing interests.
About this article
Cite this article
Iakovidis, D., Koulaouzidis, A. Software for enhanced video capsule endoscopy: challenges for essential progress. Nat Rev Gastroenterol Hepatol 12, 172–186 (2015). https://doi.org/10.1038/nrgastro.2015.13
This article is cited by
A Review of Biomedical Devices: Classification, Regulatory Guidelines, Human Factors, Software as a Medical Device, and Cybersecurity
Biomedical Materials & Devices (2023)
De-redundancy in wireless capsule endoscopy video sequences using correspondence matching and motion analysis
Multimedia Tools and Applications (2023)
Scientific Reports (2022)
Surgical Endoscopy (2022)
DFCA-Net: Dual Feature Context Aggregation Network for Bleeding Areas Segmentation in Wireless Capsule Endoscopy Images
Journal of Medical and Biological Engineering (2022)