| # SimCortex v2: Pre-trained Models and Reproducibility Files |
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| This archive contains the finalized pre-trained model weights and reproducibility files for SimCortex v2, a deep-learning pipeline for simultaneous reconstruction of left/right white-matter and pial cortical surfaces from T1-weighted MRI. |
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| The archive contains inference-ready PyTorch state dictionaries, configuration files, the training/validation/test split used for model development, and the evaluation cohort definition used for the final benchmark. |
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| ## Recommended checkpoint |
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| The primary deformation checkpoint is: |
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| `deform/deform_best_rmse.pth` |
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| This is the recommended checkpoint and the one used for the final SimCortex v2 paper evaluation. |
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| The archive also includes: |
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| `deform/deform_best_model.pth` |
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| as a secondary checkpoint retained for reproducibility and comparison. |
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| The segmentation checkpoint is: |
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| `seg/seg_best_dice.pt` |
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| ## Archive contents |
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| ```text |
| SimCortex_v2_pretrained_models/ |
| ├── README.md |
| ├── MODEL_CARD.md |
| ├── provenance.json |
| ├── SHA256SUMS |
| ├── configs/ |
| │ ├── deform/ |
| │ │ ├── eval.yaml |
| │ │ ├── inference.yaml |
| │ │ └── train.yaml |
| │ ├── initsurf/ |
| │ │ └── generate.yaml |
| │ └── seg/ |
| │ ├── eval.yaml |
| │ ├── inference.yaml |
| │ └── train.yaml |
| ├── deform/ |
| │ ├── deform_best_model.pth |
| │ └── deform_best_rmse.pth |
| ├── seg/ |
| │ └── seg_best_dice.pt |
| └── splits/ |
| ├── dataset_split.csv |
| └── evaluation_sample40.csv |
| ``` |
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| ## Model weights |
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| ### Segmentation |
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| `seg/seg_best_dice.pt` |
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| Pre-trained weights for the 3D U-Net segmentation stage. The network predicts nine classes used by the subsequent cortical-surface initialization stage. |
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| ### Deformation |
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| `deform/deform_best_rmse.pth` |
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| Primary and recommended deformation checkpoint. |
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| `deform/deform_best_model.pth` |
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| Secondary deformation checkpoint retained for reproducibility and comparison. |
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| The released `.pt` and `.pth` files contain model state dictionaries intended for inference. Optimizer state and training-resume checkpoints are not included. |
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| ## Data splits |
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| `splits/dataset_split.csv` contains the HCP-YA and OASIS-1 subject split used for model development: |
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| - 515 subjects total |
| - 361 training subjects |
| - 77 validation subjects |
| - 77 test subjects |
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| Dataset composition: |
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| - HCP-YA: 100 subjects |
| - OASIS-1: 415 subjects |
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| `splits/evaluation_sample40.csv` defines the final evaluation cohort: |
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| - 560 cases |
| - 14 datasets |
| - 40 cases per dataset |
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| The evaluation cohort file contains only public case identifiers and does not contain workstation-specific paths. |
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| ## Configuration files |
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| The `configs/` directory contains the frozen YAML configuration files associated with the released pipeline: |
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| - segmentation training, inference, and evaluation |
| - initial-surface generation |
| - deformation training, inference, and evaluation |
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| These files are also maintained in the SimCortex source-code repository. |
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| ## Pipeline |
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| The released model corresponds to the following processing stages: |
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| 1. preprocessing of T1-weighted MRI into MNI152 space; |
| 2. nine-class 3D U-Net segmentation; |
| 3. initialization of left/right white and pial cortical surfaces; |
| 4. simultaneous deformation of the four cortical surfaces. |
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| The deformation stage reconstructs the following four surfaces: |
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| ```text |
| lh_pial |
| lh_white |
| rh_pial |
| rh_white |
| ``` |
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| ## Reference-space data |
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| The MNI152 template itself is not redistributed in this archive. |
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| Users should obtain the required reference-space resources through the software dependencies or sources documented by the SimCortex code repository. |
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| ## Dataset availability |
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| The MRI datasets and derived neuroimaging data are not redistributed in this model archive. |
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| The associated SimCortex dataset release is archived separately in the Federated Research Data Repository (FRDR). |
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| FRDR dataset DOI: |
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| `10.20383/103.01769` |
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| ## File integrity |
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| SHA256 checksums for the released files are provided in: |
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| `SHA256SUMS` |
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| Additional checkpoint and cohort provenance is recorded in: |
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| `provenance.json` |
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| ## Citation |
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| Please cite the SimCortex v2 paper and the corresponding Zenodo release when using these pretrained models. |
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| The final publication DOI and Zenodo DOI should be used once available. |
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