README.md

metadata

title: LUNA25_TS_seg
license: cc-by-nc-sa-4.0
configs:
  - config_name: metadata
    data_files: metadata.csv

LUNA25 TS Segmentations

The LUNA25 challenge serves as a benchmark for evaluating lung nodule detection in low-dose CT scans. This repository includes segmentations generated using TotalSegmentator for the total, lung_vessels, and lung_nodules tasks. Note: Segmentation volumes have not been independently verified and are supplied "as is".

Steps to recreate

1. Use scripts/convert_nifti.py and scripts/convert_nifti.sh to convert LUNA25 .mha files to .nii.gz.
2. Install TotalSegmentator.
3. Run TS segmentation scripts (written for parallel execution):
   • scripts/ts_total.sh
   • scripts/ts_lung_vessels.sh
   • scripts/ts_lung_nodules.sh
4. Run scripts/get_metadata.py to get metadata for detected lung nodules.

Installation Info:
- Date installed: Mar 6, 2025.
- Python version: 3.9.15
- TotalSegmentator version: 2.7.0 

For full list of package versions, see requirements.txt.

Citation

@misc{luna25,
  title={{LUNA25: LUng Nodule Analysis Challenge}},
  author={{Grand-Challenge.org}},
  year={2025}
  howpublished={\url{https://luna25.grand-challenge.org/}},
}

@article{wasserthal2023totalsegmentator,
  title={TotalSegmentator: robust segmentation of 104 anatomic structures in CT images},
  author={Wasserthal, Jakob and Breit, Hanns-Christian and Meyer, Manfred T and Pradella, Maurice and Hinck, Daniel and Sauter, Alexander W and Heye, Tobias and Boll, Daniel T and Cyriac, Joshy and Yang, Shan and others},
  journal={Radiology: Artificial Intelligence},
  volume={5},
  number={5},
  pages={e230024},
  year={2023},
  publisher={Radiological Society of North America}
}

@article{poletti2022automated,
  title={Automated lung vessel segmentation reveals blood vessel volume redistribution in viral pneumonia},
  author={Poletti, Julien and Bach, Michael and Yang, Shan and Sexauer, Raphael and Stieltjes, Bram and Rotzinger, David C and Bremerich, Jens and Sauter, Alexander Walter and Weikert, Thomas},
  journal={European Journal of Radiology},
  volume={150},
  pages={110259},
  year={2022},
  publisher={Elsevier}
}