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  # Automatic liver segmentation in CT using deep learning
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  [![license](https://img.shields.io/github/license/DAVFoundation/captain-n3m0.svg?style=flat-square)](https://github.com/DAVFoundation/captain-n3m0/blob/master/LICENSE)
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- #### Trained 2D model on the LITS dataset is automatically downloaded when running the inference script and can be used as you wish, ENJOY! :)
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  <img src="figures/Segmentation_CustusX.PNG" width="70%" height="70%">
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@@ -12,17 +12,17 @@ The LITS dataset can be accessible from [here](https://competitions.codalab.org)
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  ## Usage:
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  ```
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- git clone https://github.com/andreped/livermask.git \
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- cd livermask \
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- python -m venv venv \
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- python -m pip install -r /path/to/requirements.txt . \ <- might want to run > python setup.py bdist_wheel < before
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- cd livermask \
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  python livermask.py "path_to_ct_nifti.nii" "output_name.nii"
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  ```
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  If you lack any modules after, try installing them through setup.py (could be done instead of using requirements.txt):
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  ```
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- pip install wheel \
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  python setup.py bdist_wheel
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  ```
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  # Automatic liver segmentation in CT using deep learning
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  [![license](https://img.shields.io/github/license/DAVFoundation/captain-n3m0.svg?style=flat-square)](https://github.com/DAVFoundation/captain-n3m0/blob/master/LICENSE)
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+ #### Trained U-Net on the LITS dataset is automatically downloaded when running the inference script and can be used as you wish, ENJOY! :)
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  <img src="figures/Segmentation_CustusX.PNG" width="70%" height="70%">
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  ## Usage:
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  ```
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+ git clone https://github.com/andreped/livermask.git
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+ cd livermask
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+ python -m venv venv
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+ python -m pip install -r /path/to/requirements.txt
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+ cd livermask
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  python livermask.py "path_to_ct_nifti.nii" "output_name.nii"
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  ```
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  If you lack any modules after, try installing them through setup.py (could be done instead of using requirements.txt):
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  ```
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+ pip install wheel
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  python setup.py bdist_wheel
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  ```
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