sixitingting / AbsegNet

[RedJournal2023]Comprehensive evaluation of a deep learning model for automatic organs at risk segmentation on heterogeneous computed tomography images for abdominal radiotherapy.

AbsegNet

Comprehensive evaluation of a deep learning model for automatic organs at risk segmentation on heterogeneous computed tomography images for abdominal radiotherapy (Accepted to International Journal of Radiation Oncology Biology Physics).

Notes

• This work was modified from nnUNet.
• Due to data privacy protection, we can not release all-used hospital datasets, but we released 170 cases for academic research: please contact Xiangde (luoxd1996 AT gmail DOT com) for the dataset, please check the access requirement of this dataset in Here.

How to use

1. Before you can use this package for abdominal OARs segmentation. You should install:

• PyTorch version >=1.8
• Some common python packages such as Numpy, SimpleITK, OpenCV, Scipy......

2. Run the inference script.

• Download the trained model (trained based our proposed method) from Google Drive.
• Now, you can use the following code to generate 16 OARs delineation.

from InferRobustABOD import Inference3D
Inference3D(rawf="liver_70_img.nii.gz", save_path="liver_70_pred.nii.gz") # rawf is the path of input image; save_path is the path of prediction.

• This project was originally developed for our previous work AbsegNet, if you find it's useful for your research, please consider to cite the followings:

    @article{liao2023AbsegNet,
    title={Comprehensive evaluation of a deep learning model for automatic organs at risk segmentation on heterogeneous computed tomography images for abdominal radiotherapy},
    author={Liao, Wenjun, Luo, Xiangde, He, Yuan, Dong, Ye, Li, Churong, Li, Kang, Zhang, Shichuan, Zhang, Shaoting, Wang, Guotai, and Jianghong Xiao.},
    journal={ International Journal of Radiation Oncology Biology Physics},
    DOI={https://doi.org/10.1016/j.ijrobp.2023.05.034},
    year={2023},
    publisher={Elsevier}
    }
  

or

Liao, Wenjun, Luo, Xiangde, He, Yuan, Dong, Ye, Li, Churong, Li, Kang, Zhang, Shichuan, Zhang, Shaoting, Wang, Guotai, and Jianghong Xiao. "Comprehensive evaluation of a deep learning model for automatic organs at risk segmentation on heterogeneous computed tomography images for abdominal radiotherapy." International Journal of Radiation Oncology*Biology*Physics, (2023). Accessed May 26, 2023. https://doi.org/10.1016/j.ijrobp.2023.05.034.

Acknowledgment and Statement

If you have any question, please contact Xiangde Luo.