What if things go wrong?

If you spot any issues during QC, e.g., error during segmentation, you can correct the issue (manually or by adjusting SCT parameters), and then re-run the processing.

Manual corrections

SCT provides a downloadable script called manual_correction.py that can be used to conveniently correct any problematic images (i.e. the subjects that were marked with “Fail” during the previous QC step).

Setting up the environment

As a prerequisite, make sure you have an image viewer installed (in this case: FSLeyes):

# Next, make sure that your image viewer is callable from the
# Terminal. In the course we use FSLeyes but you can do the same
# with another viewer (eg: ITKsnap).
fsleyes --version

Next, download the manual correction script and check its options:

# First, download the manual correction script into a folder called `manual-correction`
sct_download_data -d manual-correction -o manual-correction

# Then you can look at the files in the folder
ls manual-correction

# Make sure to access the version of Python inside the SCT installation
sct_python="$SCT_DIR/python/envs/venv_sct/bin/python"

# Now we can check the options of the manual correction script (using the help flag -h)
$sct_python manual-correction/manual_correction.py -h

Note

By typing $SCT_DIR/python/envs/venv_sct/bin/python instead of just python, we can directly access the version of Python that lives inside the SCT installation. This is useful because the manual-correction.py script depends on packages that are installed with SCT, but that may not be installed in your default Python environment.

Running the correction script

Once your setup is ready, you can run the manual correction script.

# Run the manual correction script
$sct_python manual-correction/manual_correction.py -config qc_fail.yml -path-img output/data_processed/ -path-out data/derivatives/labels
# Check the files output to the ‘derivatives/’ folder
tree data/derivatives

Input arguments:

• -config: A YAML file containing a list of files. Here, we supply qc_fail.yml, which can be generated by marking subjects in a QC report, then clicking the “Export Fails” button. (Note: We provide a sample qc_fail.yml for you, so this file should already be present in the working directory.)

• -path-image : The path to the parent folder containing the images to be corrected.

• -path-out : The path to the parent folder where corrected images should be outputted to.

Output files/folders:

• The output segmentations will be organized into subject-specific subfolders according to BIDS convention.

• JSON sidecar files will be created with first and last name (entered at runtime) and date, for traceability.

Re-run analysis (with corrections)

process_data.sh contains a number of convenience functions that will prioritize any file with suffix -manual (if it exists) and use that file instead of re-computing the segmentation. That way, you can manually process outlier subjects without interrupting the automated processing for the dataset as a whole.

sct_run_batch -script process_data.sh -path-data data/ -path-output output_correction -jobs 3