Last active
July 22, 2021 19:54
-
-
Save jcreinhold/9453dbd25a7d65ea9831658cdaaa6876 to your computer and use it in GitHub Desktop.
Example CSV for lesion-train CLI in tiramisu-brulee
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| subject | flair | label | pd | t1 | |
|---|---|---|---|---|---|
| train_subject_1 | /path/to/flair_1.nii.gz | /path/to/ground_truth_segmentation_1.nii.gz | /path/to/pd_1.nii.gz | /path/to/t1_1.nii.gz | |
| train_subject_2 | /path/to/flair_2.nii.gz | /path/to/ground_truth_segmentation_2.nii.gz | /path/to/pd_2.nii.gz | /path/to/t1_2.nii.gz |
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
The names in the
subjectcolumn are arbitrary designators the row and do not affect data loading.The non-
outcolumns in the example prediction CSV file are the same columns used here (except thelabelcolumn which isn't required for prediction).The order of the columns doesn't matter. The non-
label/non-outcolumns are sorted for input to the network. For example, in this case, the first channel will be a FLAIR image, the second channel will be a PD-w image, and the third channel will be a T1-w image (if you are using a 3D network).