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Convolutional Neural Networks for Red Blood Cell Trajectory Prediction in Simulation of Blood Flow

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Bioinformatics and Biomedical Engineering (IWBBIO 2019)

Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 11466))


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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Correspondence to Katarína Bachratá .

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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.

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-17934-2

  • Online ISBN: 978-3-030-17935-9

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