Open Nilser3 opened 5 months ago
Here in the QC for GT MS lesions for 104 subjects from nih-ms-mp2rage
dataset
Legend of QC masks
label-lesion_algo2.nii.gz -> prediction of algo 2
(3D nnUnet, for details see. https://github.com/ivadomed/model_seg_ms_mp2rage/issues/75#issuecomment-1867059653)
label-lesion_desc-rater1.nii.gz -> Mask segmented by algo 2
corrected by rater 1 (Nilser)
great job! these are the subjects where I think algo2 is better than the correction (and should therefore be kept as the 'GT'):
I stopped at this subject, anticipating that the same trends goes on for the other subjects. I suggest you revisit your method for manual correction. That will also be much less work for you!
Thank you for your comments @jcohenadad following the suggestions, here the QC for 45 patients.
TODO:
Here in the QC for GT MS lesions for all nih-ms-mp2rage
dataset
label-lesion_algo2.nii.gz -> prediction of algo 2 (3D nnUnet, from https://github.com/ivadomed/model_seg_ms_mp2rage/issues/75#issuecomment-1867059653)
label-lesion_desc-rater1.nii.gz -> Mask segmented by algo 2 corrected by rater 1 (Nilser)
I have also indicated with the Artifact
flag to some subjects.
Excellent work @Nilser3!
My review 👉 qc_flags.json
Legend:
exclude.yaml
file so the image is not considered for training/testing.In general, I find that you oversegment compared to the automatic segmentation.
Specific suggested changes:
sub-nih061_UNIT1.nii.gz
: algo2 is better (yours is oversegmented). Use as is for GT. sub-nih062_UNIT1.nii.gz
: algo2 is better (yours is oversegmented). Use as is for GT. sub-nih063_UNIT1.nii.gz
: algo2 is better (yours is oversegmented). Use as is for GT. sub-nih090_UNIT1.nii.gz
: algo2 is better (yours is oversegmented). Use as is for GT. sub-nih141_UNIT1.nii.gz
: algo2 is better (yours is oversegmented). Use as is for GT. sub-nih149_UNIT1.nii.gz
: algo2 is better (yours is oversegmented). Use as is for GT. sub-nih165_UNIT1.nii.gz
: algo2 is better (yours is oversegmented). Use as is for GT. sub-nih166_UNIT1.nii.gz
: algo2 is better (yours is oversegmented). Use as is for GT. sub-nih173_UNIT1.nii.gz
: algo2 is better (yours is oversegmented). Use as is for GT. sub-nih175_UNIT1.nii.gz
: algo2 is better (yours is oversegmented). Use as is for GT. sub-nih179_UNIT1.nii.gz
: algo2 is better (yours is oversegmented). Use as is for GT. sub-nih181_UNIT1.nii.gz
: algo2 is better (yours is oversegmented). Use as is for GT. sub-nih182_UNIT1.nii.gz
: algo2 is better (yours is oversegmented). Use as is for GT. sub-nih196_UNIT1.nii.gz
: algo2 is better (yours is oversegmented). Use as is for GT. Thanks for your feedback @jcohenadad
I have push to the git-annex 163 MS maks (159 masks corrected by rater_1
✅ + 14 made by algo-2
❌)
sub-nih061
sub-nih062
sub-nih063
sub-nih090
sub-nih141
sub-nih149
sub-nih165
sub-nih166
sub-nih173
sub-nih175
sub-nih179
sub-nih181
sub-nih182
sub-nih196
)branch
: nlm/add_ms_lesion_gt
commit
: 8187361e4f5143ffc6c8d93750a34a57e424a3a8
Redy for PR @mguaypaq
sub-nih008
sub-nih009
sub-nih013
sub-nih017
sub-nih020
sub-nih021
sub-nih034
sub-nih037
sub-nih052
sub-nih054
sub-nih078
sub-nih079
sub-nih086
sub-nih095
sub-nih098
sub-nih134
sub-nih152
sub-nih159
sub-nih174
sub-nih177
sub-nih189
sub-nih192
@Nilser3 I'm confused about the status of this issue. Is it ready for review, or are there still TODO items?
@mguaypaq It's ready for your review,
The TODO list will be for a next PR
Thanks you,
Ok, sorry for the delay. All the files are properly git-annexed. Merged into master.
QC of subjects with lesions to be reviewed with a neuroradiologist
Hi @Nilser3 I see 22 subjects to be revised. I am not sure we will have time to go through all cases since we have 9 subjects for tSCI and dcm (other projects) (except if you have very specific questions). Is it possible to prioritize some?
Description
Data
nih-ms-mp2rage
marseille-7T-iso-mp2rage
marseille-7T-aniso-mp2rage
As discussed here, the manual segmentations from
algo-2
(3D nnUnet, for details see. #75) will be used to start creating MS GTHere in the QC for
nih-ms-mp2rage
Legend of QC masks
Related Issues
267 #56 #80 #75