U-Mamba

Official repository for U-Mamba: Enhancing Long-range Dependency for Biomedical Image Segmentation. Welcome to join our mailing list to get updates.

Installation

Requirements: Ubuntu 20.04, CUDA 11.8

1. Create a virtual environment: conda create -n umamba python=3.10 -y and conda activate umamba
2. Install Pytorch 2.0.1: pip install torch==2.0.1 torchvision==0.15.2 --index-url https://download.pytorch.org/whl/cu118
3. Install Mamba: pip install causal-conv1d>=1.2.0 and pip install mamba-ssm --no-cache-dir
4. Download code: git clone https://github.com/bowang-lab/U-Mamba
5. cd U-Mamba/umamba and run pip install -e .

sanity test: Enter python command-line interface and run

import torch
import mamba_ssm

visual_seg.mp4

Model Training

Download dataset here and put them into the data folder. U-Mamaba is built on the popular nnU-Net framework. If you want to train U-Mamba on your own dataset, please follow this guideline to prepare the dataset.

Preprocessing

nnUNetv2_plan_and_preprocess -d DATASET_ID --verify_dataset_integrity

Train 2D models

• Train 2D U-Mamba_Bot model

nnUNetv2_train DATASET_ID 2d all -tr nnUNetTrainerUMambaBot

• Train 2D U-Mamba_Enc model

nnUNetv2_train DATASET_ID 2d all -tr nnUNetTrainerUMambaEnc

Train 3D models

• Train 3D U-Mamba_Bot model

nnUNetv2_train DATASET_ID 3d_fullres all -tr nnUNetTrainerUMambaBot

• Train 3D U-Mamba_Enc model

nnUNetv2_train DATASET_ID 3d_fullres all -tr nnUNetTrainerUMambaEnc

Inference

• Predict testing cases with U-Mamba_Bot model

nnUNetv2_predict -i INPUT_FOLDER -o OUTPUT_FOLDER -d DATASET_ID -c CONFIGURATION -f all -tr nnUNetTrainerUMambaBot --disable_tta

• Predict testing cases with U-Mamba_Enc model

nnUNetv2_predict -i INPUT_FOLDER -o OUTPUT_FOLDER -d DATASET_ID -c CONFIGURATION -f all -tr nnUNetTrainerUMambaEnc --disable_tta

CONFIGURATION can be 2d and 3d_fullres for 2D and 3D models, respectively.

Remarks

1. Path settings

The default data directory for U-Mamba is preset to U-Mamba/data. Users with existing nnUNet setups who wish to use alternative directories for nnUNet_raw, nnUNet_preprocessed, and nnUNet_results can easily adjust these paths in umamba/nnunetv2/path.py to update your specific nnUNet data directory locations, as demonstrated below:

# An example to set other data path,
base = '/home/user_name/Documents/U-Mamba/data'
nnUNet_raw = join(base, 'nnUNet_raw') # or change to os.environ.get('nnUNet_raw')
nnUNet_preprocessed = join(base, 'nnUNet_preprocessed') # or change to os.environ.get('nnUNet_preprocessed')
nnUNet_results = join(base, 'nnUNet_results') # or change to os.environ.get('nnUNet_results')

2. AMP could lead to nan in the Mamba module. We also provide a trainer without AMP: https://github.com/bowang-lab/U-Mamba/blob/main/umamba/nnunetv2/training/nnUNetTrainer/nnUNetTrainerUMambaEncNoAMP.py

Paper

@article{U-Mamba,
    title={U-Mamba: Enhancing Long-range Dependency for Biomedical Image Segmentation},
    author={Ma, Jun and Li, Feifei and Wang, Bo},
    journal={arXiv preprint arXiv:2401.04722},
    year={2024}
}

Acknowledgements

We acknowledge all the authors of the employed public datasets, allowing the community to use these valuable resources for research purposes. We also thank the authors of nnU-Net and Mamba for making their valuable code publicly available.