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Automatic lung tumor segmentation in CT
This is the official repository for the paper "Teacher-student approach for lung tumor segmentation from mixed-supervised datasets", 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.
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
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
- The software has been successfully integrated into the open platform Fraxinus
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}
}