--- language: en license: cc-by-4.0 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**

## 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).