Towards Deep Learning-based Wall Shear Stress Prediction for Intracranial Aneurysms
Annika Niemann, Lisa Schneider, Bernhard Preim, Samuel Voß, Philipp Berg, Sylvia Saalfeld
Otto von Guericke Universität Magdeburg
This work aims at a deep learning-based prediction of wall shear stresses (WSS) for intracranial aneurysms. Based on real patient cases, we created artificial surface models of bifurcation aneurysms. After simulation and WSS extraction, these models were used for training a deep neural network. The trained neural network for 3D mesh segmentation was able to predict areas of high wall shear stress.
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