https://github.com/yousefis/DenseUnet_Esophagus_Segmentation

https://github.com/yousefis/DenseUnet_Esophagus_Segmentation

Last synced: 3 months ago
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• Host: GitHub
• URL: https://github.com/yousefis/DenseUnet_Esophagus_Segmentation
• Owner: yousefis
• Created: 2020-02-24T13:03:49.000Z (over 4 years ago)
• Default Branch: master
• Last Pushed: 2023-03-24T23:37:02.000Z (over 1 year ago)
• Last Synced: 2024-01-22T17:04:24.261Z (5 months ago)
• Language: Python
• Size: 26.6 MB
• Stars: 22
• Watchers: 1
• Forks: 5
• Open Issues: 4
• Metadata Files:
  • Readme: README.md

Lists

• awesome_medical - DenseUnet_Esophagus_Segmentation - |DDAUnet|[1](https://ieeexplore.ieee.org/document/9481104),[2](https://link.springer.com/chapter/10.1007/978-3-030-00937-3_40)| (Segmentation)

README

        
# Dilated Dense Attention Unet (DDAUnet) for Esophageal GTV Segmentation

Find the manuscript here: https://ieeexplore.ieee.org/document/9481104

# Proposed CNN



    

    Figure 1- DDAUnet network.



# Results



    

    Figure 2- Qualitative comparison of DDAUnet with the other CNNs for two slices from two distinct patients. 2D DSC

values are show in yellow. The manual delineation and the network results are shown by green and red contours, respectively.



# Citation

    @article{yousefi2021esophageal,

      title={Esophageal Tumor Segmentation in CT Images using a Dilated Dense Attention Unet (DDAUnet)},

      author={Yousefi, Sahar and Sokooti, Hessam and Elmahdy, Mohamed S and Lips, Irene M and Shalmani, Mohammad T Manzuri and Zinkstok, Roel T and Dankers, Frank JWM and Staring, Marius},

      journal={IEEE Access},

      year={2021},

      publisher={IEEE}

    }

    @inproceedings{yousefi2018esophageal,

      title={Esophageal gross tumor volume segmentation using a 3D convolutional neural network},

      author={Yousefi, Sahar and Sokooti, Hessam and Elmahdy, Mohamed S and Peters, Femke P and Shalmani, Mohammad T Manzuri and Zinkstok, Roel T and Staring, Marius},  

      booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},

      pages={343--351},  

      year={2018},  

      organization={Springer}

    }

       

   

# Requirments

```

pip install -r ./requirements.txt

```

# run code

```

python3 DDSAUnetSkipA.py --server_path  --log 

```