Closed rohanbanerjee closed 4 months ago
Choose the fold whose inference results will be used for the next round of training
Out of all the folds_{0-5}, the fold_1
has the best performance in terms of dice score on it's respective test set. Given that, I don't think that is the best metric for us to choose the fold (or model). In my opinion, the baseline
model should be trained on the maximum number of good ground truth and fold_all
is the setting which fits this. The fold_all
model is trained on all the training images and does not have a validation set unlike folds_{0-5}.
After going through the QC and based on the above conclusion, I am going ahead with fold_all
.
I chose 30 subjects from the fold_all
inference that I manually corrected. The list of 30 chosen subjects is below:
qc_report_fold_all.zip
I manually corrected the segmentation for these subjects and attaching the QC for the same below: qc_fold_all_corrected.zip
After @jcohenadad approves these segmentations, I'll add these images to the training set and start the re-training.
CC: @MerveKaptan
review: qc_fail.yml.zip
details:
spinefmri-sub-genevaR104_003_0000.nii.gz
spinefmri-sub-genevaR106_005_0000.nii.gz
spinefmri-sub-genevaR108_007_0000.nii.gz
spinefmri-sub-genevaR202_018_0000.nii.gz
spinefmri-sub-genevaR207_023_0000.nii.gz
spinefmri-sub-genevaR209_025_0000.nii.gz
spinefmri-sub-genevaR211_027_0000.nii.gz
spinefmri_sub-nwM09_task-motor_bold_0000.nii.gz overseg:
spinefmri_sub-nwM16_task-motor_bold_0000.nii.gz
"Download all" button is not there-- please make sure to use the latest version of SCT:
Closing the issue since the baseline model training was successfully completed (including running inference and manual correction)
What is the baseline model
The model which was trained on ✅ as per the QCs mentioned in #25 is the
baseline
model. A total of 96 images were used in the training of this model. A list of subjects (for later reference) is below: participants_baseline.csvThe model was trained in 6 different settings - 5 models for the 5 fold cross-validation and 1 fold_all model (discussion can be found here - https://github.com/MIC-DKFZ/nnUNet/issues/1364#issuecomment-1492075312). The config (containing preprocessing, hyperparameters) for nnUNetv2 training is: plans.json
After the model was trained, the inference was run on the ❌ (failed segmentations) from #25. Below are the QCs from all the folds and fold_all:
qc_fold_0.zip qc_fold_1.zip qc_fold_2.zip qc_fold_3.zip qc_fold_4.zip qc_fold_all.zip
The steps to reproduce the above QC results (/run inference) are the following:
cd fmri-segmentation
Next steps:
held-out test set
(#33)