Testing contrast-agnostic model on GRE magnitude data

[jcohenadad]

As of SCT version https://github.com/spinalcordtoolbox/spinalcordtoolbox/commit/bb479d82ea1e2076dd50343177056a61bd17e260 (install dev version until SCT v6.4 is released)

Syntax:

# Cord segmentation
sct_deepseg -i sub-test5_magnitude1.nii.gz -task seg_sc_contrast_agnostic -qc qc
# And then to dilate
sct_maths -i sub-test5_magnitude1_seg.nii.gz -dilate 5 -shape disk -dim 1 -o sub-test5_magnitude1_seg_dil.nii.gz

Red: contrast-agnostic (release https://github.com/sct-pipeline/contrast-agnostic-softseg-spinalcord/releases/tag/v2.4), Green: sct_deepseg_sc:

With the mask:

@chaigner

[jcohenadad]

Trying with a lumbar data:

First thing to do is to reorient the image (bc AP and SI are swapped):

sct_image -i 109_Rekos_magnitude1.nii -transpose y,x,z -o 109_Rekos_magnitude1_transposed.nii.gz
sct_image -i 109_Rekos_magnitude1_transposed.nii.gz -flip x -o 109_Rekos_magnitude1_transposed.nii.gz

Which gives:

Now we can run the inference:

sct_deepseg -i 109_Rekos_magnitude1_transposed.nii.gz -task seg_sc_contrast_agnostic -qc qc

Result (room for improvement 😅):

QC report: qc.zip

[!note] For creating the mask for shimming, binary segmentation will suffice. However, for precise evaluation of shimming methods, e.g., computing B0 inside the spinal cord, then the soft segmentation should be used (see entry "2024-06-05 10:26:01" in the QC report).