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Erschienen in:
2019 | OriginalPaper | Buchkapitel
Convolutional Neural Networks for Red Blood Cell Trajectory Prediction in Simulation of Blood Flow
verfasst von : Michal Chovanec, Hynek Bachratý, Katarína Jasenčáková, Katarína Bachratá
Erschienen in: Bioinformatics and Biomedical Engineering
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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.