PPGL_AI

This repository is created to release the model weights for the following paper: "An Automated Pheochromocytoma and Paraganglioma lesion segmentation AI-model at whole-body 68Ga- DOTATATE PET/CT".

Please cite if you are using the model:

Haque, F., Carrasquillo, J.A., Turkbey, E.B. et al. An automated pheochromocytoma and paraganglioma lesion segmentation AI-model at whole-body 68Ga- DOTATATE PET/CT. EJNMMI Res 14, 103 (2024).https://doi.org/10.1186/s13550-024-01168-5

We have used 3D full resolution nnUNet framework. Follow the instrustion below to run inference on new dataset using our model.

Insturctions:

1. First, create a conda environment. You can name it to your liking; for example, 'petct-env'.
2. Install nnUNet. Installation process can be found in the following link: documentation/installation_instructions.md
3. Create a new folder (anyname). Create the following three sub-folder in this directory: 'nnUNet_raw', 'nnUNet_preprocessed','nnUNet_results' inside the folder. The names should be exactly the same.
4. Create another folder "Dataset101_PETCT" inside 'nnUNet_raw', 'nnUNet_preprocessed','nnUNet_results' folders. This is important for nnUNet to identify which dataset to process.
5. nnU-Net expects datasets in a structured format. This format is inspired by the data structure of the Medical Segmentation Decthlon. Please read the following link for dataset conversion: how-to-use-nnUNet
6. Image file should be in nifti format. USe the following package: TCIA_processing and use the following command to conver the images from DICOM to NIFTI:

  python3 -W ignore tcia_dicom_to_nifti.py /PATH/TO/DICOM/FDG-PET-CT-Lesions/ /PATH/TO/NIFTI/FDG-PET-CT-Lesions/

9. PET images should be renamed as channel 1 input with '_0000.nii.gz' extension and CT images '_0001.nii.gz'. Example PET image: PETCT_0ea07b421b_0000.nii.gz, CT Image: PETCT_0ea07b421b_0001.nii.gz
10. The PET/CT image files needs to be put inside the '/nnUNet_raw/Dataset101_PETCT/imagesTe' path.
11. "dataset_fingerprint.json", "nnUNetPlans.json","dataset.json" files should place inside "/nnUNet_preprocessed/Dataset101_PETCT" path.
12. Model weights can be obtained by request only.
13. Plase the model weights inside the following path: "nnUNet_results\Dataset101_PETCT\nnUNetTrainer__nnUNetPlans__3d_fullres/". Inside 'nnUNetTrainer__nnUNetPlans__3d_fullres' folder, model weights from 5 folds in speparate folder be present.
14. Once everything is set, run the bash file "inference.sh" to run inference using the model weights. Please modify the folder paths 'nnUNet_raw', 'nnUNet_preprocessed','nnUNet_results' according to your set up directories inside the .sh file.