Contrast-agnostic spinal cord segmentation project with softseg
This repo creates a series of preparations for comparing the newly trained ivadomed models (Pytorch based), with the old models that are currently implemented in spinal cord toolbox [SCT] (tensorflow based).
The function creates a joblib that allocates data from the testing set of the SCT model to the testing set of the ivadomed model. The output (new_splits.joblib) needs to be assigned on the config.json in the field "split_dataset": {"fname_split": new_splits.joblib"}. Multiple datasets (BIDS folders) can be used as input for the creation of the joblib. The same list should be assigned on the config.json file in the path_data field.
The comparison is being done by running sct_deepseg_sc on every subject/contrast that was used in the testing set on ivadomed.
One thing to note, is that the SCT scores have been marked after the usage of the function sct_get_centerline and cropping around this prior.
In order to make a fair comparison, the ivadomed model needs to be tested on a testing set that has the centerline precomputed.
The function compare_with_sct_model.py prepares the dataset for this comparison by using sct_get_centerline on the images and using this prior on the TESTING set.
The output folder will contain as many folders as inputs are given to compare_with_sct_model.py, with the suffix SCT. These folders "siumulate" output folders from ivadomed (they contain evaluation3dmetrics.csv files) in order to use violinpolots visualizations from the script visualize_and_compare_testing_models.py
Problems with this approach:
- _centerline.nii.gz derivatives for the testing set files are created in the database
- The order that processes need to be done might confuse people a bit: i. Joblib needs to be created ii. The ivadomed model needs to be trained iii. compare_with_sct_model script needs to run iv. The ivadomed model needs to be tested