| --- |
| license: cc-by-4.0 |
| library_name: mlx |
| tags: |
| - mlx |
| - native-mlx |
| - apple-silicon |
| - medical-imaging |
| - mri |
| - ct |
| - brain |
| - head |
| - segmentation |
| - nnunet |
| - research |
| language: |
| - en |
| inference: false |
| pipeline_tag: image-segmentation |
| --- |
| |
| # SIAM 路 Head tissue segmentation |
|
|
| Segment brain and head tissues from any 3D head MRI or CT with SIAM v0.3, locally on Apple Silicon. |
|
|
| > **SIAM is the work of Romain Valabregue and co-authors.** The model, its training method and the weights are theirs: see the original repository, [github.com/romainVala/SIAM](https://github.com/romainVala/SIAM), and the paper, [arXiv:2605.02737](https://arxiv.org/abs/2605.02737). This repository only repackages their released weights ([Zenodo 17733431](https://zenodo.org/records/17733431)) for MLX on Apple Silicon. Please cite their paper and star their repository if SIAM is useful to you. |
|
|
| [Original SIAM repository](https://github.com/romainVala/SIAM) 路 [SIAM paper](https://arxiv.org/abs/2605.02737) 路 [Original weights](https://zenodo.org/records/17733431) 路 [nnunet-mlx runtime](https://github.com/MedMLX/nnunet-mlx) 路 [Files](https://huggingface.co/MedMLX/siam/tree/main) |
|
|
| <img src="https://huggingface.co/MedMLX/siam/resolve/main/showcase.png" alt="SIAM segmentations of a full-head T1 MRI and of a glioma case in T1, contrast-enhanced T1, T2 and FLAIR" width="100%"> |
|
|
| <sub>SIAM v0.3 segmentations produced by this MLX port (five-fold ensemble); the 3D view is a cutaway of the predicted label surfaces. Left: full-head T1 from [OpenNeuro ds000001](https://openneuro.org/datasets/ds000001), sub-01 (CC0, defaced). Right: glioma case 100 from Bartnik et al., [Zenodo 22205631](https://doi.org/10.5281/zenodo.22205631) (CC-BY-4.0, skull-stripped), the same model applied to each contrast.</sub> |
|
|
| | Input | Output | |
| | :--- | :--- | |
| | One 3D head volume (T1, T2, FLAIR, CT, ...) 路 NIfTI | 17 tissue labels and an anomaly label | |
|
|
| **Apple Silicon 路 Metal 路 Python 3.13** |
|
|
| [SIAM](https://github.com/romainVala/SIAM) (Segment It All Model) by Valabregue et al. is an nnU-Net residual-encoder network trained on synthetic images generated from a few high-quality head templates, so it does not depend on the input contrast. Version 0.3 adds an `Anomalies` label for pathology. It predicts at 0.75 mm isotropic and returns labels on the input grid. |
|
|
| ## Quick start |
|
|
| ```bash |
| uv venv --python 3.13 |
| source .venv/bin/activate |
| |
| uv pip install "nnunet-mlx>=0.7.1" |
| ``` |
|
|
| ```python |
| from nnunet_mlx.named_models import siam |
| |
| result = siam(image_path="head.nii.gz", output_dir="outputs") |
| print(result["outputs"]["mask"]) |
| ``` |
|
|
| Without `weights_path`, the runner downloads this repository into the shared MedMLX cache on first use. `voxel_size=` reproduces SIAM's `-voxelsize` option for small heads, such as newborns; `fold=` selects one fold instead of the ensemble. |
|
|
| The runner follows the inference of the original [`siam-pred -m 0`](https://github.com/romainVala/SIAM): the volume is reoriented to RAS voxel order without interpolation, all folds are ensembled with Gaussian sliding windows at a 0.5 step and no mirroring, and the label map is reoriented back exactly onto the input grid. |
|
|
| ## Labels |
|
|
| | Value | Label | Value | Label | |
| | ---: | :--- | ---: | :--- | |
| | 1 | GM (cortical grey matter) | 10 | Accu (accumbens) | |
| | 2 | WM | 11 | Amyg (amygdala) | |
| | 3 | CSF | 12 | Hippo (hippocampus) | |
| | 4 | CSFv (ventricles) | 13 | Dura | |
| | 5 | cerGM (cerebellar grey matter) | 14 | vascular | |
| | 6 | Thal (thalamus) | 15 | Skull | |
| | 7 | Pal (pallidum) | 16 | Head (extracranial soft tissue) | |
| | 8 | Put (putamen) | 17 | Anomalies | |
| | 9 | Caud (caudate) | | | |
|
|
| ## Conversion |
|
|
| Converted from the five-fold release on [Zenodo 17733431](https://zenodo.org/records/17733431) (`pred_DS108_LcsfP_Ano`, trainer `nnUNetTrainerNoDA`, `3d_fullres`, epoch 4000). Network weights are stored as float32 safetensors; no retraining, pruning or quantization. Optimizer state and the duplicate decoder aliases of encoder weights are not carried over. To reproduce from the original release: |
|
|
| ```bash |
| uv pip install "nnunet-mlx[convert]" |
| nnunet-mlx convert pred_DS108_LcsfP_Ano siam |
| ``` |
|
|
| Checked against PyTorch nnU-Net 2.8.1 (CPU, float32) running the same inference as `siam-pred`: all five folds, 0.5-step Gaussian sliding windows, no mirroring. On a full-head T1 MRI, 99.995% of voxels receive the same label, every label has a Dice of at least 0.9996 except `Anomalies` (0.998, under 0.5 mL in that healthy head), and class probabilities differ by at most 0.004. On fold 0, a skull-stripped glioma T1 in LPS orientation, which exercises the reorientation path, gives 99.990% voxel agreement with a minimum Dice of 0.9995. |
|
|
| A typical head MRI needs about 16 GB of unified memory; memory grows with the field of view. |
|
|
| ## Use & license |
|
|
| For research and education. Not validated for clinical use. |
|
|
| Weights: [CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/), as released by the SIAM authors. Credit Valabregue et al. and cite the SIAM paper when you use these weights. Upstream code: [Apache-2.0](https://github.com/romainVala/SIAM/blob/main/LICENSE). |
|
|
| ## Paper references |
|
|
| - [SIAM: Head and Brain MRI Segmentation from Few High-Quality Templates via Synthetic Training](https://arxiv.org/abs/2605.02737). |
| - [Framework: nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation](https://arxiv.org/abs/1809.10486). |
|
|
| ## Citation |
|
|
| Please cite SIAM: |
|
|
| > Valabregue, R., Khemir, I., Bardinet, E., Rousseau, F., Auzias, G. & Dorent, R. (2026). SIAM: Head and Brain MRI Segmentation from Few High-Quality Templates via Synthetic Training. arXiv:2605.02737. |
|
|
| If you use MedMLX, please cite the [MedMLX paper](https://doi.org/10.64898/2026.10.28.26366762): |
|
|
| ```bibtex |
| @article{sandoval2026medmlx, |
| title = {MedMLX: On-Device Medical Imaging AI for Apple Silicon}, |
| author = {Sandoval, J. and Sandoval, A. and Saleem, A.}, |
| journal = {medRxiv}, |
| year = {2026}, |
| doi = {10.64898/2026.10.28.26366762} |
| } |
| ``` |
|
|
