https://github.com/huanglizi/scribformer

[IEEE Transactions on Medical Imaging/TMI] This repository is the official implementation of the paper ScribFormer: Transformer Makes CNN Work Better for Scribble-based Medical Image Segmentation.
https://github.com/huanglizi/scribformer

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[IEEE Transactions on Medical Imaging/TMI] This repository is the official implementation of the paper ScribFormer: Transformer Makes CNN Work Better for Scribble-based Medical Image Segmentation.

• Host: GitHub
• URL: https://github.com/huanglizi/scribformer
• Owner: HUANGLIZI
• License: mit
• Created: 2022-07-10T03:36:21.000Z (over 2 years ago)
• Default Branch: main
• Last Pushed: 2024-02-08T16:25:28.000Z (11 months ago)
• Last Synced: 2024-02-08T17:38:38.740Z (11 months ago)
• Language: Python
• Homepage:
• Size: 34.2 KB
• Stars: 7
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# ScribFormer [TMI 2024]

This repository is the official implementation of the paper ScribFormer: Transformer Makes CNN Work Better for Scribble-based Medical Image Segmentation. [Arxiv](https://arxiv.org/abs/2402.02029), [ResearchGate](https://www.researchgate.net/publication/377930252_ScribFormer_Transformer_Makes_CNN_Work_Better_for_Scribble-based_Medical_Image_Segmentation), [IEEEXplore](https://ieeexplore.ieee.org/document/10423891)

## Datasets

### ACDC

1. The ACDC dataset with mask annotations can be downloaded from [ACDC](https://www.creatis.insa-lyon.fr/Challenge/acdc/).

2. The scribble annotations of ACDC have been released in [ACDC scribbles](https://vios-s.github.io/multiscale-adversarial-attention-gates/data). 

3. The pre-processed ACDC data used for training could be directly downloaded from [ACDC_dataset](https://github.com/HiLab-git/WSL4MIS/tree/main/data/ACDC).

### MSCMR

1. The MSCMR dataset with mask annotations can be downloaded from [MSCMRseg](https://zmiclab.github.io/zxh/0/mscmrseg19/data.html). 

2. The scribble annotations of MSCMRseg have been released in [MSCMR_scribbles](https://github.com/BWGZK/CycleMix/tree/main/MSCMR_scribbles). 

3. The scribble-annotated MSCMR dataset used for training could be directly downloaded from [MSCMR_dataset](https://github.com/BWGZK/CycleMix/tree/main/MSCMR_dataset).

## Requirements

Some important required packages include:

* Python 3.8

* CUDA 11.3

* [Pytorch](https://pytorch.org) 1.10.1.

* torchvision 0.11.2

* Some basic python packages such as Numpy, Scikit-image, SimpleITK, Scipy ......

Follow official guidance to install [Pytorch](https://pytorch.org).

## Training

To train the model, run this command:

```train

python train.py --root_path  --exp  --bilinear --linear_layer --fold MAAGfold --max_epoches 200 --weight_cam_subloss 0.25 0.5 0.75 1 --weight_cam 0.1

```

## Evaluation

To evaluate the model, run this command:

```eval

python test.py --bilinear --linear_layer --fold MAAGfold --exp  --save_prediction

```

## Citation

```bash

@article{li2023lvit,

  title={ScribFormer: Transformer Makes CNN Work Better for Scribble-based Medical Image Segmentation},

  author={Li, Zihan and Zheng, Yuan and Shan, Dandan and Yang, Shuzhou and Li, Qingde and Wang, Beizhan and Hong, Qingqi and Shen, Dinggang},

  journal={IEEE Transactions on Medical Imaging},

  year={2024},

  publisher={IEEE}

}

```