AI-based image segmentation for brain aneurysm computational fluid dynamics models showed almost perfect agreement with manual segmentation for all 11 morphological and 5 of 8 hemodynamic parameters.
Does deep-learning-based image segmentation provide comparable morphological and hemodynamic results to manual segmentation for CFD simulations of brain aneurysms?
Deep-learning-based image segmentation is a feasible and potentially time-saving alternative to manual segmentation for creating computational fluid dynamics models of brain aneurysms.
"Image-based" computational fluid dynamics (CFD) simulations provide insights into each patient's hemodynamic environment. However, 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 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 models generated by AI segmentation methods were compared with those obtained by two human users for the same data. Interclass coefficients (ICC), Bland-Altman plots, and Pearson's correlation coefficients (PCC) were combined to assess how well AI-generated CFD models were performed. We found that almost perfect agreement was obtained between the human and AI results for all eleven morphological 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.
Rezaeitaleshmahalleh et al. (Mon,) conducted a other in Intracranial aneurysms. Deep-learning-based image segmentation (AI segmentation) vs. Manual segmentation by human users was evaluated on Aneurysm morphological and hemodynamic parameters. AI-based image segmentation for brain aneurysm computational fluid dynamics models showed almost perfect agreement with manual segmentation for all 11 morphological and 5 of 8 hemodynamic parameters.