PGE-UNet is a physics-guided deep learning framework for cine cardiovascular magnetic resonance image segmentation. The framework combines a Noise-Aware Encoder, a Physics-Regularized Decoder, and a simulation-based pseudo-(B_1^+) prior.

Paper

Journal: Computerized Medical Imaging and Graphics
Volume: 134
Article: 102798
Year: 2026
DOI: https://doi.org/10.1016/j.compmedimag.2026.102798
Source code: https://github.com/QuocKhanhLuong/PGE-UNet

Available Checkpoints

Dataset	Checkpoint	Input	Classes
ACDC	`best_model_acdc_no_anatomical.pth`	Five adjacent slices, 2.5D	Background, RV, MYO, LV
M&Ms	`best_model_mnm.pth`	Five adjacent slices, 2.5D	Background, LV, MYO, RV
SCD	`best_model_scd.pth`	Five adjacent slices, 2.5D	Background, LV

The checkpoints contain PyTorch model state_dict objects. They must be used with the model implementation and preprocessing pipeline provided in the official GitHub repository.

Despite its filename, best_model_acdc_no_anatomical.pth is the official ACDC checkpoint used for the reported evaluation. The original filename is retained for reproducibility and traceability with the experimental setup.

Download

Install the Hugging Face Hub CLI:

pip install --upgrade huggingface_hub

Download all checkpoints:

bash

hf download QuocKhanhLuong/PGE-UNet \
  best_model_acdc_no_anatomical.pth \
  best_model_mnm.pth \
  best_model_scd.pth \
  SHA256SUMS \
  --local-dir weights

Evaluation

Clone the official repository:

bash

git clone https://github.com/QuocKhanhLuong/PGE-UNet.git
cd PGE-UNet

Evaluate ACDC:

bash

python scripts/evaluate_acdc.py \
  --weights weights/best_model_acdc_no_anatomical.pth

Evaluate M&Ms:

bash

python scripts/evaluate_mnm.py \
  --weights weights/best_model_mnm.pth

Evaluate SCD:

bash

python scripts/evaluate_scd.py \
  --weights weights/best_model_scd.pth

Important Notes

The models expect 2.5D inputs consisting of five adjacent cine CMR slices.
The class ordering differs between ACDC and M&Ms.
Input images must follow the preprocessing procedure provided in the official repository.
These models are intended for research use only.
The checkpoints have not been validated for clinical diagnosis or clinical decision-making.

Citation

bibtex

@article{trannguyen2026pgeunet,
  title   = {Integrating physics guidance into deep learning for cine cardiovascular segmentation},
  author  = {Tran-Nguyen, My Anh and Luong, Quoc Khanh and Kha, Minh Bao and Dang, Tuan Linh},
  journal = {Computerized Medical Imaging and Graphics},
  volume  = {134},
  pages   = {102798},
  year    = {2026},
  doi     = {10.1016/j.compmedimag.2026.102798},
  url     = {https://doi.org/10.1016/j.compmedimag.2026.102798}
}

License

The source code and pretrained checkpoints are provided for research purposes only. For commercial use, please contact the authors.