PET-CT-Lung-Segmentation-Models

Two open-source nnU-Net models for automatic segmentation of lung tumors on PET and CT images with and without respiratory motion compensation. In this repository the model weights are stored for the trained nnU-Net models as described in Carles, M. et al. (2024).

Installation

1. For this project the nnU-Net v1 was utilized as described in Isensee, F. et al. (2021). Clone the nnU-Net repository and follow the installation instructions.

2. Download and extract the CT model or the PET model.

3. Copy the Task directory of the desired model into the nnU-Net directory at the correct location. For example:

your/path/to/nnUNet_trained_models/nnUNet/3d_fullres/Task998_LungCTSegmentation

your/path/to/nnUNet_trained_models/nnUNet/3d_fullres/Task999_LungPETSegmentation

If the directories do not exist create them and make sure you've set up the environment variables correctly.

Prediction

For lung tumors segmentation on CT images execute the following script

nnUNet_predict -i FOLDER_WITH_TEST_CASES -o OUTPUT_FOLDER -tr nnUNetTrainerV2 -ctr nnUNetTrainerV2CascadeFullRes -m 3d_fullres -p nnUNetPlansv2.1 -t Task998_LungCTSegmentation

and for segmentation on PET images execute

nnUNet_predict -i FOLDER_WITH_TEST_CASES -o OUTPUT_FOLDER -tr nnUNetTrainerV2 -ctr nnUNetTrainerV2CascadeFullRes -m 3d_fullres -p nnUNetPlansv2.1 -t Task999_LungPETSegmentation

Substitute FOLDER_WITH_TEST_CASES and OUTPUT_FOLDER with directory paths of your choice.

References

1. Carles, M., Kuhn, D., Fechter, T. et al. Development and evaluation of two open-source nnU-Net models for automatic segmentation of lung tumors on PET and CT images with and without respiratory motion compensation. Eur Radiol (2024). https://doi.org/10.1007/s00330-024-10751-2
2. Isensee, F. et al. (2021). nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nature methods, 18(2), 203–211. https://doi.org/10.1038/s41592-020-01008-z

Author

Montserrat Carles Fariña montserrat_carles@iislafe.es

THANK YOU FOR YOUR INTEREST IN THIS PACKAGE

If you have any questions, comments or suggestions about this package, please do not hesitate to contact me!

History

• Version 1.0: April 2024

Citation

Please cite the following paper if you use this work for your research:

Carles, M., Kuhn, D., Fechter, T. et al. Development and evaluation of two open-source nnU-Net models for automatic segmentation of lung tumors on PET and CT images with and without respiratory motion compensation. Eur Radiol (2024). https://doi.org/10.1007/s00330-024-10751-2