# Automatic lung tumor segmentation in CT

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This is the official repository for the paper [_"Teacher-student approach for lung tumor segmentation from mixed-supervised datasets"_](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0266147), published in PLOS ONE.

A pretrained model is made available in a command line tool and can be used as you please. However, the current model is not intended for clinical use. The model is the result of a proof-of-concept study. An improved model will be made available in the future, when more training data is made available.

![sample of masked output](https://github.com/VemundFredriksen/LungTumorMask/releases/download/0.0.1/sample_images.png "Sample output of two different tumors")
![sample of 3d render](https://github.com/VemundFredriksen/LungTumorMask/releases/download/0.0.1/sample_renders.png "3D render of two masked outputs")

## Dependencies
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.

## Installation
```
pip install git+https://github.com/VemundFredriksen/LungTumorMask
```

## Usage
After install, the software can be used as a command line tool. Simply specify the input and output filenames to run:
```
# Format
lungtumormask input_file output_file

# Example
lungtumormask patient_01.nii.gz mask_01.nii.gz
```

## Acknowledgements
If you found this repository useful in your study, please, cite the following paper:
```
@article{fredriksen2021teacherstudent,
title = {Teacher-student approach for lung tumor segmentation from mixed-supervised datasets},
author = {Fredriksen, Vemund AND Sevle, Svein Ole M. AND Pedersen, André AND Langø, Thomas AND Kiss, Gabriel AND Lindseth, Frank},
journal = {PLOS ONE},
publisher = {Public Library of Science},
year = {2022},
month = {04},
doi = {10.1371/journal.pone.0266147},
volume = {17},
url = {https://doi.org/10.1371/journal.pone.0266147},
pages = {1-14},
number = {4}}
```