MeshGrow

Unified pipeline for patient-specific cardiac + vascular simulation mesh construction:

1. nnU-Net — binary cardiac localization
2. Crop — subvolume around the heart
3. LinFlo-Net — whole-heart mesh and segmentation
4. SeqSeg — aortic/vascular tracing (seeded from cardiac mesh)
5. Combine — merged simulation-ready model ({case_id}_LV_aorta.vtp)

Quick start

python3 -m venv .venv
source .venv/bin/activate   # Windows: .venv\Scripts\activate

pip install -e .

# Pipeline tools (nnU-Net, SeqSeg, LinFlo-Net)
pip install seqseg linflonet nnunetv2

# PyTorch — install before pytorch3d; use a CUDA wheel on Linux if you have a GPU
pip install torch

# pytorch3d (required by LinFlo-Net; not on PyPI for all platforms)
pip install --no-build-isolation \
  "git+https://github.com/facebookresearch/pytorch3d.git@stable"

meshgrow download-weights --dest models/
meshgrow doctor

meshgrow run \
  --input /path/to/images \
  --output /path/to/results \
  --modality ct

Install torch before pytorch3d. On macOS, use the default CPU torch build; MeshGrow runs nnU-Net on CPU automatically (runtime.device: auto). GPU builds are strongly recommended on Linux for nnU-Net and SeqSeg.

meshgrow doctor checks nnU-Net, SeqSeg, and LinFlo-Net CLIs but not pytorch3d — verify with:

python -c "import torch; import pytorch3d; print(torch.__version__, pytorch3d.__version__)"

No config file is required — built-in defaults are used after weights are downloaded.

Optional project scaffold:

meshgrow init --dest ./my_project
meshgrow run --config ./my_project/pipeline.yaml \
  --input ./my_project/images \
  --output ./results \
  --modality mr

Model weights

Step	Zenodo	Path after download
Cardiac binary seg	10.5281/zenodo.20804513	models/cardiac/nnUNet_cardiac_weights/
LinFlo-Net	10.5281/zenodo.21326357	models/linflonet/best_model.pth
SeqSeg aorta	10.5281/zenodo.15020477	models/seqseg/nnUNet_results/

Optional: use --cardiac-path ./nnUNet_cardiac_weights instead of downloading from Zenodo.

CLI

Command	Description
meshgrow download-weights	Fetch weights from Zenodo
meshgrow doctor	Check dependencies and model paths
meshgrow init --dest DIR	Create pipeline.yaml + images/
meshgrow run	Run full or partial pipeline

Resume from a step:

meshgrow run --output results/ --input images/ --modality ct \
  --case case_001 --from-step seqseg

Documentation

• Installation
• Pipeline

Results

Workflow. MeshGrow combines cardiac template deformation (LinFlo-Net) with growth-based vascular tracing (SeqSeg), then assembles a single simulation-ready mesh:

Geometric models. Resulting cardiac, aortic, and combined meshes on a CT test set, compared with ground truth:

Simulations. CFD on MeshGrow outputs (velocity and wall shear stress), confirming the models are simulation-ready:

Citation

If you use MeshGrow, please cite:

Sveinsson Cepero N, Narayanan A, Kong F, Shadden SC. MeshGrow: Integrated framework for simulation-ready cardiac and vascular mesh construction from medical imaging. JRSM Cardiovascular Disease. 2026;15. doi:10.1177/20480040261455944

@article{Sveinsson_Cepero_2026,
  title   = {MeshGrow: Integrated framework for simulation-ready cardiac and vascular mesh construction from medical imaging},
  volume  = {15},
  ISSN    = {2048-0040},
  url     = {https://doi.org/10.1177/20480040261455944},
  DOI     = {10.1177/20480040261455944},
  journal = {JRSM Cardiovascular Disease},
  publisher = {SAGE Publications},
  author  = {Sveinsson Cepero, Numi and Narayanan, Arjun and Kong, Fanwei and Shadden, Shawn C.},
  year    = {2026},
  month   = may
}

When using this workflow, please also cite SeqSeg, LinFlo-Net, and the underlying nnU-Net models. See the respective project pages for BibTeX entries.