A machine-learning-based method for automatizing lattice-Boltzmann simulations of respiratory flows

Abstract

Many simulation workflows require to prepare the data for the simulation manually. This is time consuming and leads to a massive bottleneck when a large number of numerical simulations is requested. This bottleneck can be overcome by an automated data processing pipeline. Such a novel pipeline is developed for a medical use case from rhinology, where computer tomography recordings are used as input and flow simulation data define the results. Convolutional neural networks are applied to segment the upper airways and to detect and prepare the in- and outflow regions for accurate boundary condition prescription in the simulation. The automated process is tested on three cases which have not been used to train the networks. The accuracy of the pipeline is evaluated by comparing the network-generated output surfaces to those obtained from a semi-automated procedure performed by a medical professional. Except for minor deviations at interfaces between ethmoidal sinuses, the network-generated surface is sufficiently accurate. To further analyze the accuracy of the automated pipeline, flow simulations are conducted with a thermal lattice-Boltzmann method for both cases on a high-performace computing system. The comparison of the results of the respiratory flow simulations yield averaged errors of less than 1% for the pressure loss between the in- and outlets, and for the outlet temperature. Thus, the pipeline is shown to work accurately and the geometrical deviations at the ethmoidal sinuses to be negligible.

Use of Ensemble Learning to Improve Performance of Known Convolutional Neural Networks for Mammography Classification

“…Machine learning and artificial intelligence have come a long way in advancing practical solutions to our everyday problems. Due to their remarkable progress, these methods have become the gold standard when solving more complex issues, such as image recognition [1][2][3], automatized tasks [4,5], and optimization problems [6], among many others. In medical imaging, machine learning (ML) has been vital to building more modern versions of computer-aided diagnostic tools (CADs).…”