ScribbleVC (ACM MM 2023)

This repository is the official implementation of the paper ScribbleVC: Scribble-supervised Medical Image Segmentation with Vision-Class Embedding (ACM MM 2023). Paper, Arxiv, ResearchGate

The ScribbleVC has been compatible with WSL4MIS Codebase, feel free to use it.

Datasets

ACDC

1. The ACDC dataset with mask annotations can be downloaded from ACDC.
2. The scribble annotations of ACDC have been released in ACDC scribbles.
3. The pre-processed ACDC data used for training could be directly downloaded from ACDC_dataset.

MSCMR

1. The MSCMR dataset with mask annotations can be downloaded from MSCMRseg.
2. The scribble annotations of MSCMRseg have been released in MSCMR_scribbles.
3. The scribble-annotated MSCMR dataset used for training could be directly downloaded from MSCMR_dataset.

The slice classfication files have been available.

Requirements

Some important required packages include:

• Python 3.8
• CUDA 11.7
• Pytorch 1.13.1
• torchvision 0.14.1
• Some basic python packages such as Numpy, Scikit-image, SimpleITK, Scipy.

Follow official guidance to install Pytorch.

Training

To train the model, run this command:

python train_ACDC.py --root_path <dataset_path> --exp <path_to_save_model> --bilinear --linear_layer --max_epoches 100 --pretrain_weights cnnTransformer.pth 

Evaluation

To evaluate the model, run this command:

python test_ACDC.py --bilinear --linear_layer --exp <path_to_save_model> --save_prediction

Citation

@inproceedings{li2023scribblevc,
  title={ScribbleVC: Scribble-supervised Medical Image Segmentation with Vision-Class Embedding},
  author={Li, Zihan and Zheng, Yuan and Luo, Xiangde and Shan, Dandan and Hong, Qingqi},
  booktitle={Proceedings of the 31st ACM International Conference on Multimedia},
  year={2023}
}