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