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---
language: en
tags:
  - pytorch
  - medical-imaging
  - neuroimaging
  - brain-mri
  - cortical-surface-reconstruction
  - 3d-mesh
  - arxiv:2610.07378
---

# SimCortex v2

**Joint Cortical Surface Reconstruction with Near-Zero Collisions and Self-Intersections**

[Paper (arXiv)](https://doi.org/10.48550/arXiv.2610.07378) · [Pretrained Model Archive (Zenodo)](https://doi.org/10.5281/zenodo.22710781) · [Source Code](https://github.com/Neuro-iX/SimCortex) · [Website](https://simcortex.app) · [3D Slicer Extension](https://github.com/Kaveh-Moradkhani/SlicerSimCortex)

## Model overview

SimCortex v2 is a deep-learning pipeline for joint reconstruction of the left and right cortical white-matter and pial surfaces from structural T1-weighted MRI. It combines a nine-class 3D U-Net segmentation stage, topology-correct surface initialization, and simultaneous learned deformation of four cortical surfaces using multi-scale stationary velocity fields.

The released pipeline operates on MRI preprocessed into the expected **MNI152 reference space**. Its four-surface ordering is:

```text
lh_pial
lh_white
rh_pial
rh_white
```

The complete preprocessing, surface initialization, and inference workflow is implemented in the [SimCortex source repository](https://github.com/Neuro-iX/SimCortex).

## Released checkpoints

The model files are located under [`zenodo-v2/`](https://huggingface.co/SimCortexAI/simcortex-v2/tree/main/zenodo-v2), which preserves the original Zenodo archive structure.

| Component | File | Role |
|---|---|---|
| Segmentation | `zenodo-v2/seg/seg_best_dice.pt` | Released nine-class segmentation checkpoint |
| Deformation (recommended) | `zenodo-v2/deform/deform_best_rmse.pth` | **Checkpoint used for the final paper evaluation** |
| Deformation (alternative) | `zenodo-v2/deform/deform_best_model.pth` | Alternative checkpoint retained for comparison and reproducibility |

These `.pt` and `.pth` files are PyTorch state dictionaries, not stand-alone applications or Hugging Face Transformers models. The repository also includes the frozen configurations, split manifests, provenance metadata, and checksum manifest under `zenodo-v2/`. The archive does not include optimizer states or training-resume checkpoints.

## Downloading the checkpoints

Use the Hugging Face Hub client to retrieve individual files:

```python
from huggingface_hub import hf_hub_download

repo_id = "SimCortexAI/simcortex-v2"

segmentation_weights = hf_hub_download(
    repo_id=repo_id,
    filename="zenodo-v2/seg/seg_best_dice.pt",
)

recommended_deformation_weights = hf_hub_download(
    repo_id=repo_id,
    filename="zenodo-v2/deform/deform_best_rmse.pth",
)
```

This example **downloads checkpoints only**; it is not a complete inference command. For preprocessing, network construction, inference, and output conversion, follow the [SimCortex code and documentation](https://github.com/Neuro-iX/SimCortex). While this repository remains private, authorized users must authenticate to Hugging Face before downloading. For a reproducible deployment, pin a tested Hugging Face commit or release tag instead of relying on the latest `main` revision.

## Training and evaluation

The archived development split comprises **515 HCP-YA and OASIS-1 subjects**: 361 training, 77 validation, and 77 test subjects. The exact split is preserved in `zenodo-v2/splits/dataset_split.csv`.

The paper evaluates SimCortex v2 on **560 MRI cases across 14 cohorts** (40 cases per cohort). The case selection is recorded in `zenodo-v2/splits/evaluation_sample40.csv`.

The accompanying [paper](https://doi.org/10.48550/arXiv.2610.07378) reports:

| Measure | Reported result |
|---|---|
| Average symmetric surface distance (ASSD) | **0.253 mm** |
| Cases with no detected inter-surface collision | **92.14%** |
| Self-intersection fraction | **0.044%** |

These are paper-level results; see the manuscript for the evaluation protocol, baseline comparisons, definitions, and limitations.

## Reproducibility and data

The files in `zenodo-v2/` mirror the [version-specific Zenodo release](https://doi.org/10.5281/zenodo.22710781). The original `zenodo-v2/SHA256SUMS` and `zenodo-v2/provenance.json` document integrity and checkpoint provenance. All 15 files listed in the checksum manifest were verified after downloading the archive from Hugging Face at commit `63929b9d4dd3dd37ec3b98a615a7382cbbfd6631`.

The model archive does **not** redistribute the source MRI scans or the MNI152 template. The associated research dataset is archived separately in the [Federated Research Data Repository (FRDR)](https://doi.org/10.20383/103.01769); users must observe the applicable dataset access and redistribution conditions.

## License

The pretrained models and reproducibility files are released under the **Creative Commons Attribution 4.0 International (CC BY 4.0)** license, consistent with the [official Zenodo release](https://doi.org/10.5281/zenodo.22710781).

The SimCortex source code is distributed separately under the license specified in its [GitHub repository](https://github.com/Neuro-iX/SimCortex).

## Citation

Moradkhani, K., & Bouix, S. (2026). *SimCortex v2: Joint Cortical Surface Reconstruction with Near-Zero Collisions and Self-Intersections*. arXiv:2610.07378. https://doi.org/10.48550/arXiv.2610.07378

```bibtex
@misc{moradkhani2026simcortexv2,
  title={SimCortex v2: Joint Cortical Surface Reconstruction with Near-Zero Collisions and Self-Intersections},
  author={Moradkhani, Kaveh and Bouix, Sylvain},
  year={2026},
  eprint={2610.07378},
  archivePrefix={arXiv},
  primaryClass={cs.CV},
  doi={10.48550/arXiv.2610.07378}
}
```

For the exact pretrained files, also cite the [Zenodo model archive](https://doi.org/10.5281/zenodo.22710781).