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PyMeshTool - A framework for building efficient automated image-based cardiac anatomical twinning workflows in

Matthias A F Gsell1, Benedikt A Klöckl1, Aurel Neic2

  • 1Gottfried Schatz Research Center, Division of Medical Physics and Biophysics, Medical University of Graz, Graz, 8010, Austria.

Computer Methods and Programs in Biomedicine
|June 4, 2026
PubMed
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This summary is machine-generated.

PyMeshTool, a new Python interface, significantly accelerates anatomical twinning for digital twin models by reducing computational time and storage needs. This tool streamlines complex cardiac electrophysiology simulations for clinical applications.

Area of Science:

  • Computational biology
  • Medical imaging analysis
  • Cardiovascular research

Background:

  • Digital twin models are crucial for patient-specific cardiac electrophysiology simulations in clinical settings.
  • Accurate anatomical twin construction from high-resolution imaging is computationally intensive and time-consuming.
  • Efficient tools are needed to streamline the anatomical twinning workflow.

Purpose of the Study:

  • To develop PyMeshTool, a Python interface for MeshTool, simplifying and accelerating anatomical twinning.
  • To provide a user-friendly interface for core MeshTool functionalities.
  • To facilitate the development of complex cardiac modeling pipelines.

Main Methods:

  • Restructured the C/C++ codebase of MeshTool to support Python interface development.
Keywords:
Cardiac model generationImage manipulationMesh manipulationMeshToolPython3 bindings

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  • Designed PyMeshTool for streamlined interaction with Python modules and NumPy.
  • Implemented and compared two anatomical twinning pipelines: one using PyMeshTool and another calling MeshTool externally.
  • Evaluated performance based on runtime and data output across varying OpenMP threads.

    • Main Results:

    • PyMeshTool offers seamless integration with Python modules and NumPy.
    • Reduced storage usage by ~88% (4 files, 89.5MB vs. 418 files, 774.9MB).
    • Shortened source code by ~63% and achieved up to four times faster runtime.

    Conclusions:

    • PyMeshTool significantly streamlines image and mesh processing in Python.
    • The module simplifies the development of complex computational pipelines for cardiac modeling.
    • Freely available PyMeshTool aims to accelerate research and clinical applications of digital twin technology.