Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Shifted GT spinal cord segmentations for PSIR and STIR contrasts #102

Open
naga-karthik opened this issue Jul 22, 2024 · 6 comments
Open

Comments

@naga-karthik
Copy link
Member

naga-karthik commented Jul 22, 2024

There is a slight shift in the GT segmentations of PSIR and STIR contrasts of the canproco dataset. We used these shifted segmentations in training the contrast-agnostic model v2.4. This is a problem as it risks making the model biased and keep outputting shifted predictions in the future versions of the model.

example sub-edm010

ezgif com-animated-gif-maker

Mitigation strategy 1 (short term):

  • Use only a subset of PSIR/STIR images where the segmentations are of good quality (i.e. not shifted) and train a model

Mitigation strategy 2 (long term):

  • QC each segmentation 1-by-1 and correct the manual segmentations
@naga-karthik naga-karthik changed the title Shifted GT spinal cord segmentations Shifted GT spinal cord segmentations for PSIR and STIR contrasts Jul 22, 2024
@jcohenadad
Copy link
Member

QC each segmentation 1-by-1 and correct the manual segmentations

isn't there a more efficient way to do it? Eg: running sct_deepseg_sc if it works better.

@valosekj
Copy link
Member

isn't there a more efficient way to do it? Eg: running sct_deepseg_sc if it works better.

I tried sct_deepseg_sc 3D, sct_deepseg_sc 2D, and sct_propseg on STIR and PSIR images in the past, but none of the methods worked well. Details in #4 (comment)

@valosekj
Copy link
Member

There is a slight shift in the GT segmentations of PSIR and STIR contrasts of the canproco dataset. We used these shifted segmentations in training the contrast-agnostic model v2.4. This is a problem as it risks making the model biased and keep outputting shifted predictions in the future versions of the model.

If the shift is related to v2.4 (i.e., after adding PSIR and STIR contrasts), how would you explain that the shift is also presented for v2.3 predictions (i.e., before adding PSIR and STIR contrasts)? See v2.3 vs 2.4 comparison: valosekj/dcm-brno#19 (comment)

@jcohenadad
Copy link
Member

I would like to understand the cause for this shift. Could it be as simple as an interpolation issue?

@jcohenadad
Copy link
Member

jcohenadad commented Jul 22, 2024

If the shift is related to v2.4

IIUC this is not what Naga implied-- the GT were already existing before, and they were used to train the v2.4 model (TLDR: shift exists for 2.3 and, consequently, 2.4)

@jcohenadad
Copy link
Member

jcohenadad commented Jul 22, 2024

Another example from valosekj/dcm-brno#19:
sub-1860B6472B_ses-1860B_acq-ZOOMit_dir-AP_dwi_crop_crop_moco_dwi_mean.nii.gz

sct_deepseg -i sub-1860B6472B_ses-1860B_acq-ZOOMit_dir-AP_dwi_crop_crop_moco_dwi_mean.nii.gz -task seg_sc_contrast_agnostic -qc qc -thr 0

Further investigation in sct-pipeline/contrast-agnostic-softseg-spinalcord#113

Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment
Labels
None yet
Projects
None yet
Development

No branches or pull requests

3 participants