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# Automatic lung tumor segmentation in CT | |
[![license](https://img.shields.io/github/license/DAVFoundation/captain-n3m0.svg?style=flat-square)](https://github.com/DAVFoundation/captain-n3m0/blob/master/LICENSE) | |
[![Build Actions Status](https://github.com/VemundFredriksen/LungTumorMask/workflows/Build/badge.svg)](https://github.com/VemundFredriksen/LungTumorMask/actions) | |
[![Paper](https://zenodo.org/badge/DOI/10.1371/journal.pone.0266147.svg)](https://doi.org/10.1371/journal.pone.0266147) | |
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. | |
<img src="https://github.com/VemundFredriksen/LungTumorMask/releases/download/0.0.1/sample_images.png" width="70%"> | |
<img src="https://github.com/VemundFredriksen/LungTumorMask/releases/download/0.0.1/sample_renders.png" width="70%"> | |
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## [Installation](https://github.com/VemundFredriksen/LungTumorMask#installation) | |
Software has been tested against Python `3.7-3.10`. | |
Stable latest release: | |
``` | |
pip install https://github.com/VemundFredriksen/LungTumorMask/releases/download/v1.2.0/lungtumormask-1.2.0-py2.py3-none-any.whl | |
``` | |
Or from source: | |
``` | |
pip install git+https://github.com/VemundFredriksen/LungTumorMask | |
``` | |
## [Usage](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 | |
# Custom arguments | |
lungtumormask patient_01.nii.gz mask_01.nii.gz --lung-filter --threshold 0.3 --radius 3 | |
``` | |
In the last example, we filter tumor candidates outside the lungs, use a lower probability threshold to boost recall, and use a morphological smoothing step | |
to fill holes inside segmentations using a disk kernel of radius 3. | |
## [Applications](https://github.com/VemundFredriksen/LungTumorMask#applications) | |
* The software has been successfully integrated into the open platform [Fraxinus](https://github.com/SINTEFMedtek/Fraxinus) | |
## [Citation](https://github.com/VemundFredriksen/LungTumorMask#citation) | |
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} | |
} | |
``` | |