USING CONVOLUTIONAL NEURAL NETWORK-BASED SEGMENTATION FOR IMAGE-BASED COMPUTATIONAL FLUID DYNAMICS SIMULATIONS OF BRAIN ANEURYSMS: INITIAL EXPERIENCE IN AUTOMATED MODEL CREATION

Abstract

“Image-based” computational fluid dynamics (CFD) simulations provide insights into each patient’s hemodynamic environment. However, the current standard procedures for creating CFD models start with manual segmentation and are time-consuming, hindering the clinical translation of image-based CFD simulations. This feasibility study adopts deep-learning-based image segmentation [hereafter referred to as Artificial Intelligence (AI) segmentation] to replace manual segmentation to accelerate the CFD model creation. Two published convolutional neural network-based AI methods (MIScnn and DeepMedic) were selected to perform CFD model extraction from three-dimensional (3D) rotational angiography data containing intracranial aneurysms. In this study, aneurysm morphological and hemodynamic results using the models generated by AI segmentation methods were compared with those obtained by two human users for the same data. Interclass coefficients (ICCs), Bland–Altman plots, and Pearson’s correlation coefficients (PCCs) were combined to assess how well the AI-generated CFD models performed. We found that almost perfect agreement was obtained between the human and AI results for all 11 morphological parameters and five out of eight hemodynamic parameters, while a moderate agreement was obtained from the remaining three hemodynamic parameters. Given this level of agreement, using AI segmentation to create CFD models is feasible, given more developments.