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Updated README to be connected to the arXiv paper

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  # Automatic lung tumor segmentation in CT
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- This repository offers a proof of concept release of an automatic lung tumor segmentation method given a CT scan. Pre-trained weights are available for anyone to use. The repository structure is inspired by [Johannes Hofmanninger's lungmask repo](https://github.com/JoHof/lungmask), and part of the preprocessing pipeline is based on his lungmask release.
 
 
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  ![sample of masked output](https://github.com/VemundFredriksen/LungTumorMask/releases/download/0.0.1/sample_images.png "Sample output of two different tumors")
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  ![sample of 3d render](https://github.com/VemundFredriksen/LungTumorMask/releases/download/0.0.1/sample_renders.png "3D render of two masked outputs")
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  ## Dependencies
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- In addition to the python packages specified in requirements.txt, [PyTorch](https://pytorch.org/get-started/locally/) and [Hofmanninger's lungmask](https://github.com/JoHof/lungmask) must be installed.
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  ## Installation
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  ```
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-
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  pip install git+https://github.com/VemundFredriksen/LungTumorMask
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-
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  ```
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  ## Usage
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  # Example
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  lungtumormask patient_01.nii.gz mask_01.nii.gz
 
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  ```
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- ## Limitations
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- This repository is a proof of concept. It is not intended to be used in clinical or commercial use. However, it might be interesting for research, play a role as a baseline, or even aid semi-supervised lung tumor segmentation.
 
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  # Automatic lung tumor segmentation in CT
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+ This is the official repository for the paper [_"Teacher-Student Architecture for Mixed Supervised Lung Tumor Segmentation"_](https://arxiv.org/abs/2112.11541), submitted to the International Journal of Computer Assisted Radiology and Surgery ([IJCARS](https://www.springer.com/journal/11548)).
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+
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+ A pretrained model is made available and can be used as you please. However, the current model is not intended for clinical use. The model is a result of a proof-of-concept study, and an improved model will be made available in the near future, when more training data is made available.
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  ![sample of masked output](https://github.com/VemundFredriksen/LungTumorMask/releases/download/0.0.1/sample_images.png "Sample output of two different tumors")
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  ![sample of 3d render](https://github.com/VemundFredriksen/LungTumorMask/releases/download/0.0.1/sample_renders.png "3D render of two masked outputs")
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  ## Dependencies
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+ In addition to the python packages specified in requirements.txt, [PyTorch](https://pytorch.org/get-started/locally/) and [lungmask](https://github.com/JoHof/lungmask) must be installed.
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  ## Installation
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  ```
 
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  pip install git+https://github.com/VemundFredriksen/LungTumorMask
 
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  ```
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  ## Usage
 
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  # Example
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  lungtumormask patient_01.nii.gz mask_01.nii.gz
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+ ```
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+ ## Acknowledgements
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+ If you found this repository useful in your study, please, cite the following paper:
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+ ```
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+ @misc{fredriksen2021teacherstudent,
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+ title={Teacher-Student Architecture for Mixed Supervised Lung Tumor Segmentation},
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+ author={Vemund Fredriksen and Svein Ole M. Svele and André Pedersen and Thomas Langø and Gabriel Kiss and Frank Lindseth},
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+ year={2021},
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+ eprint={2112.11541},
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+ archivePrefix={arXiv},
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+ primaryClass={eess.IV}}
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  ```