{"slug": "from-raw-segmentations-to-simulation-ready-cardiac-meshes-an-automated-framework", "title": "From Raw Segmentations to Simulation-Ready Cardiac Meshes: An Automated Framework for Anatomical Reconstruction and Virtual Cohort Generation", "summary": "Researchers developed a semi-automatic pipeline that converts CT-based cardiac segmentations into simulation-ready meshes in minutes, preserving anatomical and topological consistency. Validated on 58 healthy cardiac CT scans, the framework enables construction of statistical shape models and synthetic anatomy generation, supporting large-scale in silico studies.", "body_md": "arXiv:2607.02564v1 Announce Type: new\nAbstract: 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 simulations.\nIn this work, we present a semi-automatic pipeline that converts CT-based segmentations into simulation-ready cardiac meshes within a few minutes while preserving anatomical and topological consistency. Building on modern deep learning segmentation methods, the framework incorporates a template-based registration stage to regularize artifacts and enforce mesh-quality constraints. A Chamfer-distance morphing strategy deforms a high-quality template toward each segmented heart, matching individual chambers while preserving topology.\nThe resulting meshes are watertight, isotopological, and endowed with consistent point-to-point correspondence. The pipeline is validated on 58 healthy cardiac CT scans, including all cardiac chambers and proximal vessel segments. The resulting meshes can be represented in a unified shape space, enabling the construction of a statistical shape model of the heart and major vessels. Principal Component Analysis shows that a low-dimensional latent space efficiently captures population variability, while Gaussian Mixture Modeling enables synthetic anatomy generation. Overall, the proposed framework (released open-source) provides a pathway from raw segmentations to simulation-ready cardiac geometries, enabling anatomically consistent virtual cohorts for large-scale in silico studies.", "url": "https://wpnews.pro/news/from-raw-segmentations-to-simulation-ready-cardiac-meshes-an-automated-framework", "canonical_source": "https://arxiv.org/abs/2607.02564", "published_at": "2026-07-07 04:00:00+00:00", "updated_at": "2026-07-07 04:07:43.931202+00:00", "lang": "en", "topics": ["machine-learning", "computer-vision", "ai-research"], "entities": [], "alternates": {"html": "https://wpnews.pro/news/from-raw-segmentations-to-simulation-ready-cardiac-meshes-an-automated-framework", "markdown": "https://wpnews.pro/news/from-raw-segmentations-to-simulation-ready-cardiac-meshes-an-automated-framework.md", "text": "https://wpnews.pro/news/from-raw-segmentations-to-simulation-ready-cardiac-meshes-an-automated-framework.txt", "jsonld": "https://wpnews.pro/news/from-raw-segmentations-to-simulation-ready-cardiac-meshes-an-automated-framework.jsonld"}}