Abstract
Continuous bladder irrigation (CBI) is commonly used to prevent urinary problems after prostate or bladder surgery. Nowadays, the irrigation flow rate is regulated manually based on the color (qualitative estimation of the blood concentration) of the drainage fluid. To monitor the blood concentration quantitatively and continuously, we have developed a portable CBI monitor based on the Lambert-Beer law. It measures transmitted light intensity via a camera sensor and deduces the blood concentration. To achieve high reliability, we need to guarantee that the measurement is conducted when there is no air bubble passing through the view of the camera. To detect bubble occurrences, we propose a convolutional recurrent neural network with a sequence of images as input: the convolutional layers extract spatial features from 2D images; the recurrent layers capture temporal features in the image sequence. Our experimental results show that our network has smaller scale and higher accuracy compared with conventional convolutional and recurrent neural networks.
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
References
Nojiri, Y., Okamura, K., Kinukawa, T., et al.: Continuous bladder irrigation following transurethral resection of the prostate (TURP). Nihon Hinyokika Gakkai Zasshi 98(6), 770–775 (2007)
Avallone, M.A., Sack, B.S., El-Arabi, A., et al.: Ten-year review of perioperative complications after transurethral resection of bladder tumors: analysis of monopolar and plasmakinetic bipolar cases. J. Endourol. 31(8), 767–773 (2017)
Molouk, H., Kamran, A., Ahmad, R.S., et al.: Continuous bladder irrigation prevents hemorrhagic cystitis after allogeneic hematopoietic cell transplantation. Urol. Oncol. 26(1), 43–46 (2008)
Okorie, C.: Is continuous bladder irrigation after prostate surgery still needed? World J. Clin. Urol. 4(3), 108–114 (2015)
Steve, S.: Management of clot retention following urological surgery. Nurs. Times 98(28), 48–50 (2002)
Aimin, D., Huling, C., Lihua, W., et al.: A novel automatic regulatory device for continuous bladder irrigation based on wireless sensor in patients after transurethral resection of the prostate: a prospective investigation. Medicine 95(52), e5721 (2016)
Rai, A., Yadav, N.: Autonomous continuous bladder irrigation devices, colorimeters, and methods of monitoring bladder pressure (2017)
Alex, K., Ilya, S., Geoffrey, E.H.: ImageNet classification with deep convolutional neural networks. Adv. Neural Inf. Process. Syst. 25(2), 1915–1929 (2012)
Ali, S.R., Hossein, A., Josephine, S., et al.: CNN features off-the-shelf: an astounding baseline for recognition. In: Conference on Computer Vision and Pattern Recognition Workshops. IEEE, USA (2014)
Vijay, B., Alex, K., Roberto, C.: SegNet: a deep convolutional encoder-decoder architecture for image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 39(12), 2481–2495 (2017)
Tomas, M., Martin, K., Lukas, B.: Recurrent neural network based language model. In: 11th Annual Conference of the International Speech Communication Association, Japan (2010)
Michael, A., Michel, G., Chris, Q., et al.: Joint language and translation modeling with recurrent neural networks. In: Conference on Empirical Methods in Natural Language Processing, pp. 1044–1054. Association for Computational Linguistics, USA (2013)
Alex, G., Navdeep, J.: Towards end-to-end speech recognition with recurrent neural networks. In: 31st International Conference on Machine Learning, China (2014)
Haiqiang, Z., Heng, F., Erik, B., et al.: Combining convolutional and recurrent neural networks for human skin detection. IEEE Signal Process. Lett. 24(3), 289–293 (2017)
Zhiwei, D., Arash, V., Hexiang, H., et al.: Structure inference machines: recurrent neural networks for analyzing relations in group activity recognition. In: Conference on Computer Vision and Pattern Recognition. IEEE, USA (2016)
Ming, L., Xiaolin, H.: Recurrent convolutional neural network for object recognition. In: Conference on Computer Vision and Pattern Recognition. IEEE, USA (2015)
Russell S., Mykhaylo A., Andrew Y.N.: End-to-end people detection in crowded scenes. In: Conference on Computer Vision and Pattern Recognition. IEEE, USA (2016)
Sutskever I.: Training recurrent neural networks. Ph.D. thesis, University of Toronto (2012)
Acknowledgments
This work is partially supported by the BMBF project VisIMON under grant No. 16SV7861R.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Tan, X., Reis, G., Stricker, D. (2019). Convolutional Recurrent Neural Network for Bubble Detection in a Portable Continuous Bladder Irrigation Monitor. In: Riaño, D., Wilk, S., ten Teije, A. (eds) Artificial Intelligence in Medicine. AIME 2019. Lecture Notes in Computer Science(), vol 11526. Springer, Cham. https://doi.org/10.1007/978-3-030-21642-9_9
Download citation
DOI: https://doi.org/10.1007/978-3-030-21642-9_9
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-21641-2
Online ISBN: 978-3-030-21642-9
eBook Packages: Computer ScienceComputer Science (R0)