From Raw Segmentations to Simulation-Ready Cardiac Meshes: An Automated Framework for Anatomical Reconstruction and Virtual Cohort Generation
Quick Answer
This study introduces a semi-automatic pipeline that converts CT-based segmentations into simulation-ready cardiac meshes in minutes, ensuring anatomical consistency.
Quick Take
Validated on 58 cardiac CT scans, the framework utilizes deep learning and morphing strategies to create watertight, isotopological meshes, facilitating the generation of virtual cohorts for in silico studies.
Key Points
- Pipeline converts CT segmentations to cardiac meshes in minutes.
- Ensures anatomical and topological consistency in generated meshes.
- Validated on 58 healthy cardiac CT scans, covering all chambers.
- Facilitates statistical shape modeling and synthetic anatomy generation.
- Framework is open-source, promoting large-scale in silico studies.
Paper Resources
📖 Reader Mode
~2 min readAbstract: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.
In 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.
The 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.
| Subjects: | Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Tissues and Organs (q-bio.TO) |
| Cite as: | arXiv:2607.02564 [cs.CV] |
| (or arXiv:2607.02564v1 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2607.02564 arXiv-issued DOI via DataCite |
Submission history
From: Martino Andrea Scarpolini [view email]
[v1]
Mon, 29 Jun 2026 09:07:05 UTC (28,673 KB)
— Originally published at arxiv.org
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