Canproco SC segmentation were saved with a wrong header

[NathanMolinier]

Description

I tried to work with T2w spinal cord (SC) segmentations stored in the canproco dataset and I noticed that images from Calgary were saved with a wrong header. Leading to a wrong display and more troubles.

In the RPI system:	Header	Image	SC segmentation
X (pixel)	56	56
Y (pixel)	512	320
Z (pixel)	512	320
px (mm/pixel)	0.800011	0.800011
py (mm/pixel)	0.5	0.5
pz (mm/pixel)	0.5	0.5

The real resolution for the SC segmentation should be:

• px = 0.800011 mm/pixel
• py = 0.8 mm/pixel
• pz = 0.8 mm/pixel

Solution

To fix this I wrote a script to overwrite the header. Also, I would like to save the SC segmentations in the same space as the one used for images to simplify future use.

Related issues

• https://github.com/neuropoly/data-management/issues/197
• https://github.com/neuropoly/data-management/issues/303

Note provided in the README

Note: T2w sagittal images were preprocessed before the spinal cord was segmented ('_T2w_seg-manual.nii.gz') and disc labels were identified ('_T2w_labels-manual.nii.gz'). Namely, reorientation to RPI and resampling to 0.8mm isotropic voxel were performed. This is why the original T2w images have different dimensions than segmentations and labels. Preprocessing steps: https://github.com/ivadomed/canproco/blob/8e1b2c35f96eeeb3838b512dd93eba25e5a5e97a/scripts-t2w_csa/sct-preprocess_data.sh#L162-L170 For context, see https://github.com/neuropoly/data-management/issues/197

I tried the preprocessing steps but it did not work

[valosekj]

Good catch @NathanMolinier! Thanks for reporting this! I wonder how that could happen!

Can you please list the problematic subejcts? I looked at the first one (sub-cal056) and dimensions are fine:

# Raw T2w image: 0.8, 0.5, 0.5
canproco$ sct_image -i sub-cal056/ses-M0/anat/sub-cal056_ses-M0_T2w.nii.gz -header | grep pixdim
pixdim      [1.0, 0.8, 0.5, 0.5, 2.51667, 0.0, 0.0, 0.0]

# SC seg obtained from resampled and reoriented image: 0.800011, 0.8, 0.8
canproco$ sct_image -i derivatives/labels/sub-cal056/ses-M0/anat/sub-cal056_ses-M0_T2w_seg-manual.nii.gz -header | grep pixdim
pixdim      [-1.0, 0.800011, 0.8, 0.8, 1.0, 1.0, 1.0, 1.0]

[NathanMolinier]

I double checked the subject sub-cal056 with a fresh git-annex download and these are the images that I get.

So it looks like you are using a modified (or older) version of the canproco dataset. Because the problem that I showed is the same for every subject from calgary (sub-calXXX).

To avoid future mistakes I think it would be preferable to push my modified images to git-annex. Also, to simplify the future use of the data, I added these lines to my script to put the SC segmentations in the same space as the one used for images. Is it ok ?

[NathanMolinier]

After more investigation I found this commit:

I checked out to the previous commit and downloaded the same images from sub-cal056:

This commit is indeed the problem.

Did something unexpected could have happened @plbenveniste ?

[plbenveniste]

Thanks for highlighting this ! Great catch ! This is the script used : dataset_correction.py. I wonder what could have happened. Maybe, we should take a look at the other datasets as well then ...

[jcohenadad]

This is the script used : dataset_correction.py.

It would be useful to see the terminal log (ie stdout) of the script applied to the dataset, so we can investigate what differed before applying the script, and what changed (or was supposed to change).

[valosekj]

It seems that the dataset_correction.py script iterated not only through PSIR/STIR images (used by Pierre-Louis for his trainings) but also through T2w images due to line 22. Then since the T2w SC segs had different orientation and resolution than the T2w images (because reorientation and resampling were applied before SC seg), the script incorrectly copied the T2w header to T2w SC seg (line 64).

I believe, we can recover T2w SC segs by going back in the git history.

BTW. I would also be very careful about other contrasts and datasets since the script could have also modified other images in an unpredictable way.

[NathanMolinier]

I fixed the SC segmentation headers, resampled them and reoriented all the images to RPI using this script. I also QCed the final segmentations to make sure that everything was OK.

Now, I just need to push the data but I have to ask for the access first.

Notes: I noticed that the subject sub-cal088 had a really bad SC segmentation probably due to the artifacts present in the image, however I still updated the header. I also noticed that the subject sub-064 had a T2w image but no SC segmentations even if the image looks good.

[jcohenadad]

Few comments:

• this script https://github.com/NathanMolinier/vrac/blob/main/fix_canproco_calgary.py should include at the top a cross-reference to this issue, so in 10 years we know what the purpose of this script is
• also, we should make sure that the output segmentation files are in DTYPE UINT8 to save space

[plbenveniste]

Closing this issue as corrections were applied, and corrected data was merged in the main branch. Thanks everybody for your feedback!