Add more contrasts for training a multi-contrast model

[naga-karthik]

Our experiment based on the preliminary hypothesis used on a single contrast (T2w) from the CanProCo dataset. It is important to gather the remaining contrasts for each subject and train a multi-channel/multi-contrast model (each additional contrast being added as an additional channel). This will be useful for robustness and also contrast-agnostic centerline detection.

@valosekj Would you please be able to preprocess the remaining contrasts as we did for T2w?

[valosekj]

@valosekj Would you please be able to preprocess the remaining contrasts as we did for T2w?

For remaining contrasts, we do not have ground truth spinal cord segmentation. Thus, we cannot easily obtain the centerline for these contrasts.

But I can co-register all contrasts to T2w. Then, we would be able to use the T2w centerline also for other contrasts.

[jcohenadad]

 For remaining contrasts, we do not have ground truth spinal cord segmentation. Thus, we cannot easily obtain the centerline for these contrasts.
But I can co-register all contrasts to T2w. Then, we would be able to use the T2w centerline also for other contrasts.

It would be much easier to instead leverage our many other datasets on which we already have the spinal cord segmentations (spine-generic, etc.)

[naga-karthik]

But I can co-register all contrasts to T2w. Then, we would be able to use the T2w centerline also for other contrasts.

Just for my understanding, this should also work right? I understand that it will take time but it should be good if we want to use all contrasts from CanProCo?

[jcohenadad]

Just for my understanding, this should also work right? I understand that it will take time but it should be good if we want to use all contrasts from CanProCo?

yes, it should work, but registration needs to be QCed, which takes a bit of time

[valosekj]

Just for my understanding, this should also work right? I understand that it will take time but it should be good if we want to use all contrasts from CanProCo?

yes, it should work, but registration needs to be QCed, which takes a bit of time

I tried to co-register other contrasts to T2w based on nii headers using -identity 1:

model_sc_centerline/scripts/get_centerline_from_segmentation.sh

Line 150 in cd6ffe1

sct_register_multimodal -i ${contrast}.nii.gz -d ${file_t2w}.nii.gz -o ${contrast}2${file_t2w}.nii.gz -identity 1 -x nn

To allow easy QC (we have >1000 coregistered images), I'm projecting the T2w centerline to the coregistered image using:

model_sc_centerline/scripts/get_centerline_from_segmentation.sh

Lines 152 to 155 in cd6ffe1

	# Create QC report to assess registration quality
	# Note: registration quality is assessed by comparing the ${contrast} image to the T2w centerline
	sct_qc -i ${contrast}2${file_t2w}.nii.gz -s ${FILESEG}_centerline.nii.gz -p sct_get_centerline -qc ${PATH_QC} -qc-subject ${SUBJECT}
	sct_qc -i ${contrast}2${file_t2w}.nii.gz -s ${FILESEG}_centerline.nii.gz -p sct_label_vertebrae -qc ${PATH_QC} -qc-subject ${SUBJECT}

Example (coregistered PSIR image and T2w_centerline):

Based on the initial quick QC assessment, it seems that the registration works well for most subjects, and also, it seems that this type of QC is reasonable!