An automated framework combining deep-learning segmentation and geometry processing robustly created CFD-suitable left ventricular meshes for 78 out of 80 test cases (97.5%).
A novel deep-learning based framework can fully automate the generation of patient-specific left ventricular models for computational fluid dynamics simulations, significantly reducing manual effort.
Computational fluid dynamics (CFD) modeling of left ventricle (LV) flow combined with patient medical imaging data has shown great potential in obtaining patient-specific hemodynamics information for functional assessment of the heart. A typical model construction pipeline usually starts with segmentation of the LV by manual delineation followed by mesh generation and registration techniques using separate software tools. However, such approaches usually require significant time and human efforts in the model generation process, limiting large-scale analysis. In this study, we propose an approach toward fully automating the model generation process for CFD simulation of LV flow to significantly reduce LV CFD model generation time. Our modeling framework leverages a novel combination of techniques including deep-learning based segmentation, geometry processing, and image registration to reliably reconstruct CFD-suitable LV models with little-to-no user intervention.1 We utilized an ensemble of two-dimensional (2D) convolutional neural networks (CNNs) for automatic segmentation of cardiac structures from three-dimensional (3D) patient images and our segmentation approach outperformed recent state-of-the-art segmentation techniques when evaluated on benchmark data containing both magnetic resonance (MR) and computed tomography(CT) cardiac scans. We demonstrate that through a combination of segmentation and geometry processing, we were able to robustly create CFD-suitable LV meshes from segmentations for 78 out of 80 test cases. Although the focus on this study is on image-to-mesh generation, we demonstrate the feasibility of this framework in supporting LV hemodynamics modeling by performing CFD simulations from two representative time-resolved patient-specific image datasets.
Kong et al. (Fri,) conducted a other in Left ventricle flow simulation (n=80). Automated model generation framework using deep-learning vs. State-of-the-art segmentation techniques was evaluated on Creation of CFD-suitable LV meshes from segmentations. An automated framework combining deep-learning segmentation and geometry processing robustly created CFD-suitable left ventricular meshes for 78 out of 80 test cases (97.5%).