Abstract
Colonoscopy is currently the gold standard procedure for colorectal cancer (CRC) screening. However, the dominant explanations for the continued incidence of CRC are endoscopist-related factors. To address this, we have been investigating an automated feedback system which measures quality of colonoscopy automatically to assist the endoscopist to improve the quality of the actual procedure being performed. One of the fundamental steps for the automated quality feedback system is to distinguish a colonoscopy from an upper endoscopy since upper endoscopy and colonoscopy procedures are performed in the same room at different times, and it is necessary to distinguish the type of a procedure prior to execution of any quality measurement method to evaluate the procedure. In upper endoscopy, a bite-block is inserted for patient protection. By detecting this bite-block appearance, we can distinguish colonoscopy from upper endoscopy. However, there are various colors (i.e., blue, green, white, etc.) of bite-blocks. Our solution utilizes analyses of Hue and Saturation values and two Convolutional Neural Networks (CNNs). One CNN detects image patches of a bite-block regardless of its colors. The other CNN detects image patches of the tongue. The experimental results show that the proposed solution is highly promising.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
American Cancer Society: Colorectum Cancer Statistics (2020). https://cancerstatisticscenter.cancer.org/?_ga=2.95264916.902125337.1581945528-1873365005.1581945528#!/cancer-site/Colorectum
Xirasagar, S., Wu, Y., Tsai, M.-H., Zhang, J., Chiodini, S., de Groen, P.C.: Colorectal cancer prevention by a CLEAR principles-based colonoscopy protocol: an observational study. Gastrointest. Endosc. 91, 905-916.e4 (2020)
Tavanapong, W., Oh, J., Kijkul, G., Pratt, J., Wong, J., de Groen, P.C.: Real-time feedback for colonoscopy in a multi-center clinical trial. In: IEEE 33rd International Symposium on Computer Based Medical Systems (CBMS), Mayo Clinic, Rochester, MN, 28–30 July 2020, pp. 13–18 (2020)
Muthukudage, J., JungHwan, O., Nawarathna, R., Tavanapong, W., Wong, J., de Groen, P.: Fast object detection using color features for colonoscopy quality measurements. In: El-Baz, A.S., Saba, L., Suri, J. (eds.) Abdomen and Thoracic Imaging, pp. 365–388. Springer US, Boston (2014). https://doi.org/10.1007/978-1-4614-8498-1_14
Islam, A.B.M.R., Alammari, A., Oh, J., Tavanapong, W., Wong, J., de Groen, P.C.: Non-informative frame classification in colonoscopy videos using CNNs. In: Proceedings of the 2018 3rd International Conference on BIOMEDICAL Imaging, Signal Processing, pp. 53–60 (2018)
Fan, L., Zhang, F., Fan, H., Zhang, C.: Brief review of image denoising techniques. Visual Comput. Ind Biomed. Art 2(1), 1–12 (2019). https://doi.org/10.1186/s42492-019-0016-7
Bradski, G.: The openCV library. Dobb’s J. Softw. Tools Prof. Progr. 25, 120–123 (2000)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition (2014). arXiv preprint arXiv:1409.1556
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770–778 (2016)
Tajbakhsh, N., et al.: Convolutional neural networks for medical image analysis: Full training or fine tuning? IEEE Trans. Med. Imaging 35, 1299–1312 (2016)
Ramasubramanian, K., Singh, A.: Deep learning using keras and tensorflow. In: Ramasubramanian, K., Singh, A. (eds.) Machine Learning Using R: With Time Series and Industry-Based Use Cases in R, pp. 667–688. Apress, Berkeley (2019). https://doi.org/10.1007/978-1-4842-4215-5_11
Gulli, A., Pal, S.: Deep Learning with Keras. Packt Publishing Ltd., Birmingham (2017)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Rahman, M.M., Oh, J., Tavanapong, W., Wong, J., de Groen, P.C. (2021). Automated Bite-block Detection to Distinguish Colonoscopy from Upper Endoscopy Using Deep Learning. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2021. Lecture Notes in Computer Science(), vol 13018. Springer, Cham. https://doi.org/10.1007/978-3-030-90436-4_17
Download citation
DOI: https://doi.org/10.1007/978-3-030-90436-4_17
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-90435-7
Online ISBN: 978-3-030-90436-4
eBook Packages: Computer ScienceComputer Science (R0)