File size: 4,052 Bytes
63929b9 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 | # SimCortex v2 Model Card
## Model overview
SimCortex v2 is a deep-learning pipeline for reconstructing four cortical surfaces from T1-weighted structural MRI:
- left pial surface
- left white-matter surface
- right pial surface
- right white-matter surface
The pipeline operates in MNI152 space and combines volumetric segmentation, cortical-surface initialization, and simultaneous deformation of the four cortical surfaces.
## Intended use
The released models are intended for research use in cortical surface reconstruction and downstream morphometric analysis.
They are not intended for standalone clinical diagnosis or treatment decisions.
## Pipeline stages
1. T1-weighted MRI preprocessing into MNI152 space.
2. Nine-class 3D U-Net segmentation.
3. Initial cortical-surface generation.
4. Simultaneous deformation of left/right white and pial surfaces.
## Released checkpoints
### Segmentation
File:
`seg/seg_best_dice.pt`
Role:
Final segmentation checkpoint used by the released pipeline.
SHA256:
`2cea6dc1606958dbdb2aea50748b73ff73e604b0c367b3b1588cef7a112c8e06`
### Primary deformation checkpoint
File:
`deform/deform_best_rmse.pth`
Role:
Recommended deformation checkpoint used for the final SimCortex v2 paper evaluation.
SHA256:
`3fb84c917953e479e72d59291837bd6b08f40fb197534046b95dc5a452e86b95`
### Secondary deformation checkpoint
File:
`deform/deform_best_model.pth`
Role:
Alternative deformation checkpoint retained for reproducibility and comparison.
SHA256:
`d30d3218069a6eb395dacd3ff0dd085a93f6f0102616eed809b90c362b885b40`
## Model file format
The released `.pt` and `.pth` files are PyTorch state dictionaries intended for inference.
Training-resume checkpoints, optimizer states, and scheduler states are not included.
## Training data split
The released training split contains 515 subjects from HCP-YA and OASIS-1:
- training: 361
- validation: 77
- test: 77
By dataset:
HCP-YA:
- 70 train
- 15 validation
- 15 test
- 100 total
OASIS-1:
- 291 train
- 62 validation
- 62 test
- 415 total
The exact split is stored in:
`splits/dataset_split.csv`
SHA256:
`68f42ef3ed09373685de5d1dd144c7d3d72b7f8083850057dcccd5780756188d`
## Evaluation cohort
The public evaluation cohort definition is stored in:
`splits/evaluation_sample40.csv`
It contains:
- 560 cases
- 14 datasets
- 40 cases per dataset
SHA256:
`2c41b21afea327756c8d7fccc30bd03e461670d59dc00f941e8cba06fcf59842`
## Configuration files
The archive includes the YAML configurations used by the released pipeline:
- `configs/seg/train.yaml`
- `configs/seg/inference.yaml`
- `configs/seg/eval.yaml`
- `configs/initsurf/generate.yaml`
- `configs/deform/train.yaml`
- `configs/deform/inference.yaml`
- `configs/deform/eval.yaml`
## Surface ordering
The deformation implementation uses the following four-surface order:
```text
lh_pial
lh_white
rh_pial
rh_white
```
## Input and output
Input:
T1-weighted MRI processed into the reference space expected by the SimCortex pipeline.
Output:
Left/right white-matter and pial cortical surface meshes.
## Data availability
The MRI datasets themselves are not included in this model archive.
The associated dataset release is hosted separately in the Federated Research Data Repository (FRDR).
FRDR dataset DOI:
`10.20383/103.01769`
## Limitations
The released models were developed and evaluated using the datasets and processing conventions described in the SimCortex v2 study.
Performance may vary for data acquired with different scanners, protocols, populations, preprocessing pipelines, or image quality.
The models are provided for research use and should be independently validated before use in new settings.
## Reproducibility
The archive includes:
- pretrained model state dictionaries
- frozen YAML configurations
- training/validation/test split
- final evaluation cohort definition
- provenance metadata
- SHA256 checksums
The source-code repository should be used together with this archive for inference and reproduction of the released pipeline.
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