Abstract
The wireless capsule endoscopy has revolutionized early diagnosis of small bowel diseases. However, a single examination has up to 10 h of video and requires between 30–120 min to read. Computational methods are needed to increase both efficiency and accuracy of the diagnosis. In this paper, an evaluation of deep learning U-Net architecture is presented, to detect and segment red lesions in the small bowel. Its results were compared with those obtained from the literature review. To make the evaluation closer to those used in clinical environments, the U-Net was also evaluated in an annotated sequence by using the Suspected Blood Indicator tool (SBI). Results found that detection and segmentation using U-Net outperformed both the algorithms used in the literature review and the SBI tool.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
- 1.
At the time of submission these datasets were waiting for publication approval from the Ethical Council. In case of approval it will be available at https://rdm.inesctec.pt/dataset/nis-2018-003.
References
Buscaglia, J.M., Giday, S.A., Kantsevoy, S.V., Clarke, J.O., Magno, P., Yong, E., Mullin, G.E.: Performance characteristics of the suspected blood indicator feature in capsule endoscopy according to indication for study. Clin. Gastroenterol. Hepatol. 6(3), 298–301 (2008). http://linkinghub.elsevier.com/retrieve/pii/S1542356507012062
Figueiredo, I.N., Kumar, S., Leal, C., Figueiredo, P.N.: Computer-assisted bleeding detection in wireless capsule endoscopy images. Comput. Methods Biomech. Biomed. Eng. Imaging Vis. 1(4), 198–210 (2013). http://www.tandfonline.com/doi/abs/10.1080/21681163.2013.796164
Iakovidis, D.K., Koulaouzidis, A.: Software for enhanced video capsule endoscopy: challenges for essential progress. Nat. Rev. Gastroenterol. Hepatol. 12(3), 172–186 (2015). http://dx.doi.org/10.1038/nrgastro.2015.13%5Cn10.1038/nrgastro.2015.13
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(2), 217–235 (2015)
Koulaouzidis, A., Iakovidis, D.K., Yung, D.E., Rondonotti, E., Kopylov, U., Plevris, J.N., Toth, E., Eliakim, A., Wurm Johansson, G., Marlicz, W., Mavrogenis, G., Nemeth, A., Thorlacius, H., Tontini, G.E.: KID Project: an internet-based digital video atlas of capsule endoscopy for research purposes. Endosc. Int. Open 5(6), E477–E483 (2017). http://www.thieme-connect.de/DOI/DOI?10.1055/s-0043-105488
Park, S.C., Chun, H.J., Kim, E.S., Keum, B., Seo, Y.S., Kim, Y.S., Jeen, Y.T., Lee, H.S., Um, S.H., Kim, C.D., Ryu, H.S.: Sensitivity of the suspected blood indicator: an experimental study. World J. Gastroenterol (WJG) 18(31), 4169–4174 (2012)
Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. Med. Image Comput. Comput. Assisted Interv. (MICCAI) 15(1), 348–356 (2015). https://doi.org/10.1007/978-3-319-24574-4_28
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(4), 25 (2014). http://link.springer.com/10.1007/s10916-014-0025-1
Seguí, S., Drozdzal, M., Pascual, G., Radeva, P., Malagelada, C., Azpiroz, F., Vitrià, J.: Generic feature learning for wireless capsule endoscopy analysis. Comput. Biol. Med. 79, 163–172 (2016). http://linkinghub.elsevier.com/retrieve/pii/S0010482516302712
Spada, C., Hassan, C., Munoz-Navas, M., Neuhaus, H., Deviere, J., Fockens, P., Coron, E., Gay, G., Toth, E., Riccioni, M.E., Carretero, C., Charton, J.P., Van Gossum, A., Wientjes, C.A., Sacher-Huvelin, S., Delvaux, M., Nemeth, A., Petruzziello, L., de Frias, C.P., Mayershofer, R., Aminejab, L., Dekker, E., Galmiche, J.P., Frederic, M., Johansson, G.W., Cesaro, P., Costamagna, G.: Second-generation colon capsule endoscopy compared with colonoscopy. Gastrointest. Endosc. 74(3), 581–589 (2011). http://dx.doi.org/10.1016/j.gie.2011.03.1125
Taha, A.A., Hanbury, A.: Metrics for evaluating 3D medical image segmentation: analysis, selection, and tool. BMC Med. Imaging 15(1) (2015). https://doi.org/10.1186/s12880-015-0068-x
Tuba, E., Tuba, M., Jovanovic, R.: An algorithm for automated segmentation for bleeding detection in endoscopic images. In: International Joint Conference on Neural Networks (IJCNN), pp. 4579–4586. IEEE, May 2017. http://ieeexplore.ieee.org/document/7966437/
Usman, M.A., Satrya, G., Usman, M.R., Shin, S.Y.: Detection of small colon bleeding in wireless capsule endoscopy videos. Comput. Med. Imaging Graph. 54, 16–26 (2016). https://doi.org/10.1016/j.compmedimag.2016.09.005
Wilcox, C.M., Cryer, B.L., Henk, H.J., Zarotsky, V., Zlateva, G.: Mortality associated with gastrointestinal bleeding events: comparing short-term clinical outcomes of patients hospitalized for upper GI bleeding and acute myocardial infarction in a US managed care setting. Clin. Exp. Gastroenterol. 2, 21–30 (2009). http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3108636/
Xiong, Y., Zhu, Y., Pang, Z., Ma, Y., Chen, D., Wang, X.: Bleeding detection in wireless capsule endoscopy based on MST clustering and SVM. In: IEEE Workshop on Signal Processing Systems (SiPS), vol. 35, pp. 1–4. IEEE, October 2015. http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=7345001
Yung, D.E., Sykes, C., Koulaouzidis, A.: The validity of suspected blood indicator software in capsule endoscopy: a systematic review and meta-analysis. Expert Rev. Gastroenterol. Hepatol. 11(1), 43–51 (2017). https://www.tandfonline.com/doi/full/10.1080/17474124.2017.1257384
Acknowledgments
This work is financed by the ERDF – European Regional Development Fund through the Operational Program for Competitiveness and Internationalization - COMPETE 2020 Program within project POCI-01-0145-FEDER-006961
, and by National Funds through the Portuguese funding agency, FCT - Fundação para a Ciência e a Tecnologia as part of project
UID/EEA/50014/2013
.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
Coelho, P., Pereira, A., Leite, A., Salgado, M., Cunha, A. (2018). A Deep Learning Approach for Red Lesions Detection in Video Capsule Endoscopies. In: Campilho, A., Karray, F., ter Haar Romeny, B. (eds) Image Analysis and Recognition. ICIAR 2018. Lecture Notes in Computer Science(), vol 10882. Springer, Cham. https://doi.org/10.1007/978-3-319-93000-8_63
Download citation
DOI: https://doi.org/10.1007/978-3-319-93000-8_63
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-92999-6
Online ISBN: 978-3-319-93000-8
eBook Packages: Computer ScienceComputer Science (R0)