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---
license: mit
library_name: pytorch
tags:
- medical
- segmentation
- stroke
- neurology
- mri
pipeline_tag: image-segmentation
---
# qSynth
Synthseg-style model trained on qMRI-constrained synthetic data derived from OASIS3 tissue maps and ATLAS binary lesion masks.
## Model Details
- **Name**: qSynth
- **Classes**: 0 (Background), 1 (Gray Matter), 2 (White Matter), 3 (Gray/White Matter Partial Volume), 4 (Cerebro-Spinal Fluid), 5 (Stroke)
- **Patch Size**: 192³
- **Voxel Spacing**: 1mm³
- **Input Channels**: 1
## Usage
### Loading from Hugging Face Hub
```python
import torch
from synthstroke_model import SynthStrokeModel
# Load the model from Hugging Face Hub
model = SynthStrokeModel.from_pretrained("liamchalcroft/synthstroke-qsynth")
# Prepare your input (example shape: batch_size=1, channels=1, H, W, D)
input_tensor = torch.randn(1, 1, 192, 192, 192)
# Get predictions (with optional TTA for improved accuracy)
predictions = model.predict_segmentation(input_tensor, use_tta=True)
# Get tissue probability maps
background = predictions[:, 0] # Background
gray_matter = predictions[:, 1] # Gray Matter
white_matter = predictions[:, 2] # White Matter
partial_volume = predictions[:, 3] # Gray/White Matter PV
csf = predictions[:, 4] # Cerebro-Spinal Fluid
stroke = predictions[:, 5] # Stroke lesion
# Alternative: Get logits without TTA
logits = model.predict_segmentation(input_tensor, apply_softmax=False)
```
## Citation
[arXiv](https://www.arxiv.org/abs/2412.03318)
```bibtex
@misc{chalcroft2025domainagnosticstrokelesionsegmentation,
title={Domain-Agnostic Stroke Lesion Segmentation Using Physics-Constrained Synthetic Data},
author={Liam Chalcroft and Jenny Crinion and Cathy J. Price and John Ashburner},
year={2025},
eprint={2412.03318},
archivePrefix={arXiv},
primaryClass={eess.IV},
url={https://arxiv.org/abs/2412.03318},
}
```
## License
MIT License - see the [LICENSE](https://github.com/liamchalcroft/synthstroke/blob/main/LICENSE) file for details.
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