sct_detect_pmj

Detection of the Ponto-Medullary Junction (PMJ). This method is based on a machine-learning algorithm published in (Gros et al. 2018, Medical Image Analysis, https://doi.org/10.1016/j.media.2017.12.001). Two models are available: one for T1w-like and another for T2w-like images. If the PMJ is detected from the input image, a NIfTI mask is output (”*_pmj.nii.gz”) with one voxel (value=50) located at the predicted PMJ position. If the PMJ is not detected, nothing is output.

usage: sct_detect_pmj -i <file> -c {t1,t2} [-h] [-s <file>] [-ofolder <folder>]
                      [-o <file>] [-qc <str>] [-qc-dataset <str>]
                      [-qc-subject <str>] [-r {0,1}] [-v <int>]

MANDATORY ARGUMENTS

-i

Input image. Example: t2.nii.gz

-c

Possible choices: t1, t2

Type of image contrast, if your contrast is not in the available options (t1, t2), use t1 (cord bright/ CSF dark) or t2 (cord dark / CSF bright)

OPTIONAL ARGUMENTS

-s

SC segmentation or centerline mask. Provide this mask helps the detection of the PMJ by indicating the position of the SC in the Right-to-Left direction. Example: t2_seg.nii.gz

-ofolder

Output folder. Example: My_Output_Folder

-o

Output filename. Example: pmj.nii.gz

-qc

The path where the quality control generated content will be saved.

-qc-dataset

If provided, this string will be mentioned in the QC report as the dataset the process was run on.

-qc-subject

If provided, this string will be mentioned in the QC report as the subject the process was run on.

-r

Possible choices: 0, 1

Remove temporary files.

Default: 1

-v

Possible choices: 0, 1, 2

Verbosity. 0: Display only errors/warnings, 1: Errors/warnings + info messages, 2: Debug mode

Default: 1