医学图像分割的弱监督学习.zip

# Weakly-supervised learning for medical image segmentation (WSL4MIS). * This project was originally developed for our two previous works **[WORD](https://www.sciencedirect.com/science/article/pii/S1361841522002705)** (**MedIA2022**) and **[WSL4MIS](https://link.springer.com/chapter/10.1007/978-3-031-16431-6_50)** (**MICCAI2022**). If you use this project in your research, please cite the following works: @article{luo2022scribbleseg, title={Scribble-Supervised Medical Image Segmentation via Dual-Branch Network and Dynamically Mixed Pseudo Labels Supervision}, author={Xiangde Luo, Minhao Hu, Wenjun Liao, Shuwei Zhai, Tao Song, Guotai Wang, Shaoting Zhang}, journal={Medical Image Computing and Computer Assisted Intervention -- MICCAI 2022}, year={2022}, pages={528--538}} @article{luo2022word, title={{WORD}: A large scale dataset, benchmark and clinical applicable study for abdominal organ segmentation from CT image}, author={Xiangde Luo, Wenjun Liao, Jianghong Xiao, Jieneng Chen, Tao Song, Xiaofan Zhang, Kang Li, Dimitris N. Metaxas, Guotai Wang, and Shaoting Zhang}, journal={Medical Image Analysis}, volume={82}, pages={102642}, year={2022}, publisher={Elsevier}} @misc{wsl4mis2020, title={{WSL4MIS}}, author={Luo, Xiangde}, howpublished={\url{https://github.com/Luoxd1996/WSL4MIS}}, year={2021}} * A re-implementation of this work based on the [PyMIC](https://github.com/HiLab-git/PyMIC) can be found here ([WSLDMPLS](https://github.com/HiLab-git/PyMIC_examples/tree/main/seg_wsl/ACDC)). # Dataset * The ACDC dataset with mask annotations can be downloaded from: [ACDC](https://www.creatis.insa-lyon.fr/Challenge/acdc/databases.html). * The Scribble annotations of ACDC can be downloaded from: [Scribble](https://gvalvano.github.io/wss-multiscale-adversarial-attention-gates/data). * The data processing code in [Here](https://github.com/Luoxd1996/WSL4MIS/blob/main/code/dataloaders/acdc_data_processing.py) the pre-processed ACDC data in [Here](https://github.com/HiLab-git/WSL4MIS/tree/main/data/ACDC). * The ISBI-MR-Prostate-2013 dataset with mask annotation can be downloaded from [TCIA](https://wiki.cancerimagingarchive.net/pages/viewpage.action?pageId=21267207), the scribble annotation of this dataset (annotated by Mr. X. Luo and M.D. W. Liao) can be downloaded [GoogleDrive](https://drive.google.com/file/d/1VFKP1-bychADhGw5rbRtd_JKLallccGz/view?usp=sharing) and [BaiduPan](https://pan.baidu.com/s/1jLz5tDAUBw4deKxHntv0gg?pwd=jqr1). * **To simulate the scribble annotation for other datasets, we further provide the simulation code at [Here](https://github.com/HiLab-git/WSL4MIS/blob/main/code/scribbles_generator.py)**. # Requirements Some important required packages include: * [Pytorch][torch_link] version >=0.4.1. * TensorBoardX * Python >= 3.6 * Efficientnet-Pytorch `pip install efficientnet_pytorch` * Some basic python packages such as Numpy, Scikit-image, SimpleITK, Scipy ...... Follow official guidance to install [Pytorch][torch_link]. [torch_link]:https://pytorch.org/ # Usage 1. Clone this project. ``` git clone https://github.com/HiLab-git/WSL4MIS cd WSL4MIS ``` 2. Data pre-processing os used or the processed data. ``` cd code python dataloaders/acdc_data_processing.py ``` 3. Train the model ``` cd code bash train_wss.sh # train model with scribble or dense annotations. bash train_ssl.sh # train model with mix-supervision (mask annotations and without annotation). ``` 4. Test the model ``` python test_2D_fully.py --sup_type scribble/label --exp ACDC/the trained model fold --model unet python test_2D_fully_sps.py --sup_type scribble --exp ACDC/the trained model fold --model unet_cct ``` 5. Training curves on the fold1:  **Note**: pCE means partially cross-entropy, TV means total variation, label denotes supervised by mask, scribble represents just supervised by scribbles. # Implemented methods * [**pCE**](https://openaccess.thecvf.com/content_cvpr_2018/papers/Tang_Normalized_Cut_Loss_CVPR_2018_paper.pdf) * [**pCE + TV**](https://arxiv.org/pdf/1605.01368.pdf) * [**pCE + Entropy Minimization**](https://arxiv.org/pdf/2111.02403.pdf) * [**pCE + GatedCRFLoss**](https://github.com/LEONOB2014/GatedCRFLoss) * [**pCE + Intensity Variance Minimization**](https://arxiv.org/pdf/2111.02403.pdf) * [**pCE + Random Walker**](http://vision.cse.psu.edu/people/chenpingY/paper/grady2006random.pdf) * [**pCE + MumfordShah_Loss**](https://arxiv.org/pdf/1904.02872.pdf) * [**Scribble2Label**](https://arxiv.org/pdf/2006.12890.pdf) * [**USTM**](https://www.sciencedirect.com/science/article/pii/S0031320321005215) * [**ScribbleVC**](https://github.com/HUANGLIZI/ScribbleVC) # Acknowledgement * The GatedCRFLoss is adapted from [GatedCRFLoss](https://github.com/LEONOB2014/GatedCRFLoss) for medical image segmentation. * The codebase is adapted from our previous work [SSL4MIS](https://github.com/HiLab-git/SSL4MIS). * The WORD dataset will be presented at [WORD](https://github.com/HiLab-git/WORD).