Abstract
Colorectal polyp differentiation is an important clinical assessment to avoid colorectal cancer (CRC). To aid in proper diagnosis from colonoscopy images, a deep learning-based computer-aided analysis system is necessary. This paper first explains why the narrow band imaging technique is a better alternative to conventional white light images for the learning of this system. This is followed by exploring the concept of image-to-image translation using Cycle GAN which was used to acquire narrow band image data distribution via unsupervised learning. This was required because not all colonoscopy equipments are enabled with special optical enhancement tools to return NBIs, and we need more image data from this domain. The paper concludes with a set of experiments that have different combinations of the datasets acquired, to check how and which models should be trained to return the highest classification accuracy.
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
References
Boeriu, A., Drasovean, S., Pascarenco, O., Dobru, D.: Narrow-band imaging with magnifying endoscopy for the evaluation of gastrointestinal lesions. World J. Gastrointest Endosc. 7(2), 110–120 (2015)
Zhu, J.-Y., Park, T., Isola,P., Efros, A.A.: Unpaired image to image translation using cycle-consistent adversarial networks (2017)
Rondonotti, E., et al.: Blue-light imaging compared with high-definition white light for real-time histology prediction of colorectal polyps less than 1 centimeter: a prospective randomized study. Gastrointestinal Endoscopy 89(3), 554–564 (2019)
Usami, H., et al.: Colorectal polyp classification based on latent sharing features domain from multiple endoscopy images. Procedia Comput. Sci. 176, 2507–2514 (2020)
Fonolla, R., et al.: A CNN CADX system for multimodal classification of colorectal polyps combining WL, BLI, and LCI modalities. Appl. Sci. 10(15), 5040 (2020)
Yang, Y.J., et al.: Automated classification of colorectal neoplasms in white-light colonoscopy images via deep learning. J. Clin. Med. 9(5), 1593 (2020)
Hafner, M., Liedlgrubera, M., Uhl, A., Vecsei, A., Wrba, F.: Color treatment in endoscopic image classification using multi-scale local color vector patterns. Med. Image Anal. 16(1), 75–86 (2012)
Huang, C., Zeng, L.: An active contour model for the segmentation of images with intensity inhomogeneities and bias field estimation. PLoS One 10(4), e0120399 (2015)
Goodfellow, I.J., et al.: Generative adversarial nets. In: Advances in Neural Information Processing Systems (2014)
Isola, P., Zhu, J. Y., Zhou, T., Efros, A.A.: Image-to-image translation with conditional adversarial networks. In: Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, 2017-January, pp. 5967–5976 (2017)
Liu, M.-Y., Tuzel, O.: Coupled generative adversarial networks. In: NIPS (2016)
Aytar, Y., Castrejon, L., Vondrick, C., Pirsiavash, H., Torralba, A.: Cross-modal scene networks. In: PAMI (2016)
Liu,M.-Y., Breuel, T., Kautz, J.: Unsupervised image-to-image translation networks. In: NIPS (2017)
Chollet, F.: Xception: deep learning with depthwise separable convolutions (2017)
Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4700–4708 (2017)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the Inception Architecture for Computer Vision (2015)
Szegedy, C., Vanhoucke, V., Ioffe, S., Alemi, A.: Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning (2016)
Bernal, J., Sánchez, F.J., Fernández-Esparrach, G., Gil, D., RodrÃguez, C., Vilariño, F.: WM-DOVA maps for accurate polyp highlighting in colonoscopy: validation vs. saliency maps from physicians. Comput. Med. Imaging Graphics 43, 99–111 (2015)
Mesejo, P., et al.: Computer-aided classification of gastrointestinal lesions in regular colonoscopy. IEEE Trans. Med. Imaging 35(9), 2051–2063 (2016)
Acknowledgments
Iwahori’s research is supported by Japan Society for the Promotion of Science (JSPS) Grant-in-Aid for Scientific Research (C) (20K11873) and by Chubu University Grant.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Bhamre, N.V., Sharma, V., Iwahori, Y., Bhuyan, M.K., Kasugai, K. (2023). Colonoscopy Polyp Classification Adding Generated Narrow Band Imaging. In: Gupta, D., Bhurchandi, K., Murala, S., Raman, B., Kumar, S. (eds) Computer Vision and Image Processing. CVIP 2022. Communications in Computer and Information Science, vol 1777. Springer, Cham. https://doi.org/10.1007/978-3-031-31417-9_25
Download citation
DOI: https://doi.org/10.1007/978-3-031-31417-9_25
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-31416-2
Online ISBN: 978-3-031-31417-9
eBook Packages: Computer ScienceComputer Science (R0)