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RWKV-UNet

Official Pytorch implementation of the paper RWKV-UNet: Improving UNet with Long-Range Cooperation for Effective Medical Image Segmentation.

Architecture

Quantitative Results

Qualitative Results

Usage

1. Prepare data

Synapse (BTCV preprocessed data) and ACDC data are available at TransUNet's repo.

The directory structure of the whole project is as follows:

.
├── RWKVUNet
│   ├──datasets
│   │       └── dataset_*.py
│   ├──train.py
│   ├──test.py
│   ├──...
│   └──data
│        └── Synapse
│        │        ├── test_vol_h5
│        │        │   ├── case0001.npy.h5
│        │        │   └── *.npy.h5
│        │        └── train_npz
│        │          ├── case0005_slice000.npz
│        │          └── *.npz
│        │
│        └──ACDC
│             ├── ACDC_training_volumes
│             │      ├── patient100_frame01.h5
│             │      └── *.h5
│             └── ACDC_training_slices
│                    ├── patient100_frame13_slice_0.h5
│                    └── *.h5

2. Environment

Please prepare an environment with python=3.9, and then use the command "pip install -r requirements.txt" for the dependencies.

3. Download weights

Pretrained weights for the encoder can be downloaded at (https://drive.google.com/drive/folders/1odF_NK5wYRkE0C3w9eoLUQEVbxefj66e?usp=sharing).

Checkpoints for RWKV-UNet can be downloaded at (https://drive.google.com/drive/folders/19y_8Mzmw5u6Bg-iVfmh6-vRCBDdy149_?usp=sharing).

4. Train/Test

  • Run the training script on the Synapse dataset. The batch size can be reduced to 12 or 6 to save memory (please also decrease the base_lr linearly), and both can reach similar performance.
python train.py --dataset Synapse --max_epochs 60 --base_lr 0.001 --img_size 224 --pretrained_path
  • Run the test script on the Synapse dataset.
python test.py --dataset Synapse --max_epochs 60 --base_lr 0.001 --img_size 224
  • You can also specify the weights for inference by yourself.
python test.py --dataset Synapse --path_specific 'ckpts/Synapse_base.pth'

Acknowledgements

This code base uses certain code blocks and helper functions from TransUNet and Vision-RWKV.

Citations

@misc{jiang2025rwkvunetimprovingunetlongrange,
      title={RWKV-UNet: Improving UNet with Long-Range Cooperation for Effective Medical Image Segmentation}, 
      author={Juntao Jiang and Jiangning Zhang and Weixuan Liu and Muxuan Gao and Xiaobin Hu and Xiaoxiao Yan and Feiyue Huang and Yong Liu},
      year={2025},
      eprint={2501.08458},
      archivePrefix={arXiv},
      primaryClass={eess.IV},
      url={https://arxiv.org/abs/2501.08458}, 
}