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Convolutional Recurrent Neural Network for Bubble Detection in a Portable Continuous Bladder Irrigation Monitor

  • Xiaoying TanEmail author
  • Gerd Reis
  • Didier Stricker
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11526)

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.

Keywords

Continuous bladder irrigation Blood concentration measurement Bubble detection Convolutional neural network Recurrent neural network 

Notes

Acknowledgments

This work is partially supported by the BMBF project VisIMON under grant No. 16SV7861R.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.DFKI, German Research Center for Artificial IntelligenceKaiserslauternGermany

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