U-SAM

Tuning Vision Foundation Models for Rectal Cancer Segmentation from CT Scans

Model

• We provide our implementation of U-SAM.
• The dataloaders of CARE and WORD are also available in dataset.

Datasets

The following pictures are demonstrations of CARE.

We conducted our experiments on CARE and WORD. Here we provide public access to these datasets.

CARE: [paper] [dataset]

WORD: [paper] [dataset]

Get Started

Main Requirements

• python==3.9.12

• torch==1.11.0

• torchvision==0.12.0

• numpy==1.21.5

• matplotlib==3.5.2

Pre-trained Weights

We utilized the SAM-ViT-B in our model, the pre-trained weights are supposed to be placed in the folder weight.

Pre-trained weights are available here, or you can directly download them via the following link.

• vit_h: ViT-H SAM model.
• vit_l: ViT-L SAM model.
• vit_b: ViT-B SAM model.

Training

Train 100 epochs on CARE with one single GPU:

python u-sam.py --epochs 100 --batch_size 24 --dataset rectum

Train 100 epochs on CARE with multiple GPUs (via DDP, on 8 GPUs for example):

CUDA_LAUNCH_BLOCKING=1;PYTHONUNBUFFERED=1;CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 \
python -m torch.distributed.launch \
--master_port 29666 \
--nproc_per_node=8 \
--use_env u-sam.py \
--num_workers 4 \
--epochs 100 \
--batch_size 24 \
--dataset rectum

For convenience, you can use our default bash file:

bash train_sam.sh

Evaluation

Evaluate on CARE with one single GPU:

python u-sam.py --dataset rectum --eval --resume chkpt/best.pth

The model checkpoint for evaluation should be specified via --resume.

Feedback and Contact

For further questions, pls feel free to contact Shouhong Wan.

Acknowledgement

Our code is based on Segment Anything and SAMed. Thanks them for releasing their codes.

Citation

If this code is helpful for your study, please cite:

@article{zhang2025tuning,
  title={Tuning vision foundation models for rectal cancer segmentation from CT scans},
  author={Zhang, Hantao and Guo, Weidong and Wan, Shouhong and Zou, Bingbing and Wang, Wanqin and Qiu, Chenyang and Liu, Kaige and Jin, Peiquan and Yang, Jiancheng},
  journal={Communications Medicine},
  volume={5},
  number={1},
  pages={256},
  year={2025},
  publisher={Nature Publishing Group UK London}
}