From Raw Segmentations to Simulation-Ready Cardiac Meshes: An Automated Framework for Anatomical Reconstruction and Virtual Cohort Generation
arXiv:2607.02564v1 Announce Type: cross Abstract: Computational models of the human heart are widely used to study electromechanical and fluid-dynamical cardiac function and to support applications such as in silico clinical trials. However, most studies remain limited to single or patient-specific anatomies, restricting the inclusion of population-level variability required for uncertainty quantification. A key challenge is translating medical-image segmentations, which may contain artifacts, mesh defects or disjoint domains, into topologically coherent geometries suitable for multiphysics si
Advances in AI, particularly in computer vision and computational geometry, are enabling more sophisticated and automated pipelines for complex scientific modeling, overcoming previous manual bottlenecks.
This development significantly enhances the efficiency and scalability of creating computational models for human physiology, which is critical for drug discovery, personalized medicine, and in silico clinical trials.
The barrier to entry for generating simulation-ready cardiac meshes from raw medical images is lowered, allowing for population-level studies and uncertainty quantification that were previously resource-intensive or impractical.
- · Pharmaceutical companies
- · Medical device manufacturers
- · Biotech firms
- · Academic research institutions
- · Manual segmentation services
- · Traditional computational modeling approaches
Automated generation of diverse cardiac models will accelerate the development and validation of new cardiovascular therapies.
The ability to rapidly create virtual cohorts will reduce the need for animal testing and human clinical trials in early development phases.
This could lead to a broader application of 'digital twin' concepts in personalized medicine, tailoring treatments based on highly accurate patient-specific simulations.
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Read at arXiv cs.AI