The complexity and diversity of heart failure pathology poses significant challenges for clinicians and researchers, and the progression from the heart's compensatory remodeling to pathological remodeling is still not fully understood. However, mechanical factors appear to be a driving force in growth and remodeling, and thus high-fidelity digital twin models that adequately capture mechanical features may be able to provide useful insights and prediction [1]. However, such models generally require resource-intensive data preparation and analysis. This study presents an automated pipeline to generate high-fidelity finite element (FE) models of the left ventricle from longitudinal experimental datasets for use in study of growth and remodeling. We utilized datasets from a rat model of increased afterload via aortic banding, along with a sham procedure for a control group. Longitudinal cardiac magnetic resonance (MR) scans were performed at six, twelve, and twenty weeks post-surgery, and the imaging data was semi-automatically segmented. We developed an automated meshing algorithm based on Non-Uniform Rational B-Splines (NURBS) curves to create finite element models, compensating for common challenges in cardiac MR data, such as short-axis misalignment and low or non-uniform spatial resolution. Meshes were automatically annotated using anatomical landmarks. By assimilating additional experimental data, such as pressure-volume measurements, the FE models can be tuned to estimate the extent of myocardial remodeling over time, together with analysis of the mechanical features that may be causal. This automated approach significantly reduced manual intervention, enabling efficient processing of large datasets. The automated pipeline demonstrates the potential of high-fidelity digital twin models in enhancing the understanding of heart failure pathology. By streamlining the generation of detailed FE models, this approach facilitates the integration of physics-based modeling into clinical research, paving the way for more personalized diagnostics and treatments in cardiology.