Abstract
Computer simulations of a blood flow in microfluidic devices are an important tool to make their development and optimization more efficient. These simulations quickly become limited by their computational complexity. Analysis of large output data by machine learning methods is a possible solution of this problem. We apply deep learning methods in this paper, namely we use convolutional neural networks (CNNs) for description and prediction of the red blood cells’ trajectory, which is crucial in modeling of a blood flow. We evaluated several types of CNN architectures, formats of theirs input data and the learning methods on simulation data inspired by a real experiment. The results we obtained establish a starting point for further use of deep learning methods in reducing computational demand of microfluid device simulations.
M. Chovanec—Author of this work was supported by the Ministry of Education, Science, Research and Sport of the Slovak Republic under the contract No. VEGA 1/0643/17.
K. Bachratá—Authors of this work were supported by the Slovak Research and Development Agency under the contract No. APVV-15-0751.
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References
Bachratá, K., Bachratý, H.: On modeling blood flow in microfluidic devices. In: ELEKTRO 2014: 10th International Conference, pp. 518–521. IEEE (2014). ISBN 978-4799-3720-2
Bachratá, K., Bachratý, H., Slavík, M.: Statistics for comparison of simulations and experiments of flow of blood cells, EPJ Web of Conferences, vol. 143 (2017). Art. no. 02002
Bachratý, H., Bachratá, K., Chovanec, M., Kajánek, F., Smiešková, M., Slavík, M.: Simulation of blood flow in microfluidic devices for analysing of video from real experiments. In: Rojas, I., Ortuño, F. (eds.) IWBBIO 2018. LNCS, vol. 10813, pp. 279–289. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-78723-7_24
Bachratý, H., Kovalčíková, K., Bachratá, K., Slavík, M.: Methods of exploring the red blood cells rotation during the simulations in devices with periodic topology. In: 2017 International Conference on Information and Digital Technologies (IDT), Zilina, pp. 36–46 (2017)
Cimrák, I., et al.: Object-in-fluid framework in modeling of blood flow in microfluidic channels. Comun. Sci. Lett. Univ. Zilina 18(1a), 13–20 (2016)
Cimrák, I., Gusenbauer, M., Jančigová, I.: An ESPResSo implementation of elastic objects immersed in a fluid. Comput. Phys. Commun. 185, 900–907 (2014)
Huang, G., Liu, Z., Maaten, L.V., Weinberger, K.Q.: Densely connected convolutional networks. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2261–2269 (2017)
Tsai, C.H.D., et al.: An on-chip RBC deformability checker significantly improves velocity-deformation correlation. Micromachines 7, 176 (2016)
Kovalčíková, K., Bachratý, H., Bachratá, K., Jasenčáková, K.: Influence of the red blood cell model on characteristics of a numerical experiment. In: Experimental Fluid Mechanics conference, Prague (2018, in press)
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Chovanec, M., Bachratý, H., Jasenčáková, K., Bachratá, K. (2019). Convolutional Neural Networks for Red Blood Cell Trajectory Prediction in Simulation of Blood Flow. In: Rojas, I., Valenzuela, O., Rojas, F., Ortuño, F. (eds) Bioinformatics and Biomedical Engineering. IWBBIO 2019. Lecture Notes in Computer Science(), vol 11466. Springer, Cham. https://doi.org/10.1007/978-3-030-17935-9_26
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DOI: https://doi.org/10.1007/978-3-030-17935-9_26
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