From Raw Segmentations to Simulation-Ready Cardiac Meshes: An Automated Framework for Anatomical Reconstruction and Virtual Cohort Generation

2026-06-29

AI Summary

This work addresses the challenge that medical image segmentations containing artifacts or topological defects are unsuitable for direct use in multiphysics simulations. To bridge this gap, we propose the first end-to-end semi-automatic pipeline that transforms raw segmentations into simulation-ready cardiac meshes. Our approach integrates deep learning–based segmentation, template-based registration, and Chamfer distance–driven deformation optimization to produce high-quality, watertight, topologically consistent meshes with point-to-point correspondence. Furthermore, we construct a unified shape space and a statistical shape model by combining principal component analysis (PCA) with Gaussian mixture models. Validation on 58 healthy cardiac CT scans demonstrates that the framework efficiently generates anatomically consistent virtual cohorts suitable for large-scale in silico studies. The implementation is publicly available.

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 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.

Problem

• cardiac mesh generation
• medical image segmentation
• topological consistency
• virtual cohort
• simulation-ready geometry

Innovation

• simulation-ready cardiac meshes
• template-based registration
• Chamfer-distance morphing
• statistical shape modeling
• virtual cohort generation