Problem Description:
Running into trouble with Moose? These error messages might be the culprit:
Error Message 1:
moose_ct_atlas = ie.segment_ct(ct_file[0], out_dir)
File "/export/moose/moose-0.1.0/src/inferenceEngine.py", line 78, in segment_ct
out_label = fop.get_files(out_dir, pathlib.Path(nifti_img).stem + '*')[0]
IndexError: list index out of range
Error Message 2:
[1/1] Running prediction for PETCT_0225325b91 using clin_ct_muscles...Traceback (most recent call last):
...
IndexError: list index out_of_range
Solutions:
-
Double-Check Requirements: ✅
- Make sure you're using Python version 3.10. You can check this by running
python --version
in your terminal. - Ensure you have enough RAM available for your tasks. Moose might require more RAM for complex datasets.
- Refer to the official Moose repository for the complete list of requirements: https://github.com/ENHANCE-PET/MOOSE
- Make sure you're using Python version 3.10. You can check this by running
-
Watch Out for Spaces!
- Moose might have trouble with spaces in your directory paths. Try renaming directories to replace spaces with underscores (_).
-
Set Environment Variables (if applicable): ⚙️
- Some systems might require specific environment variables to be set for Moose to function correctly. Refer to the Moose documentation for details on any necessary environment variables.
-
Consider Specific Versions (for Stability): ✨
- If you're facing persistent issues, try using a specific version of PyTorch (2.1.1) and CUDA (11.8) known to be compatible with Moose.
Moose is currently designed to work specifically with CT scans. If you're looking to analyze MRI data, you'll need a different tool. For more information on Moose's functionalities, refer to the README section: https://github.com/ENHANCE-PET/MOOSE
Q3: Using Moose as a Package?
Important Notes:
- To use Moose as a package, you'll need to convert your DICOM files to the .nifti format (.nii or .nii.gz).
- Moose currently processes data for one subject at a time. Batch processing is not yet supported.