The MRIQC classifier for T1w images

MRIQC is shipped with a random-forests classifier, using the combination of the ABIDE and DS030 datasets as training sample.

To predict the quality labels (0=”accept”, 1=”reject”) on a features table computed by mriqc with the default classifier, the command line is as follows:

mriqc_clf --load-classifier -X aMRIQC.csv -o mypredictions.csv

where aMRIQC.csv is the file generated by the group level run of mriqc.

Custom classifiers can be fitted using the same mriqc_clf tool in fitting mode:

mriqc_clf --train aMRIQC_train.csv labels.csv --log-file fit_clf.log --save-classifier myclassifier.pklz

where aMRIQC_train.csv contains the IQMs calculated by mriqc and labels.csv contains the matching ratings assigned by an expert. The labels can be numerical (-1``= exclude, ``0``= doubtful, ``1 = accept) or textual (“bad”, “fail” can be used for exclude; “may be” or “maybe” for doubtful and “ok”, “good” for accept).

The trained classifier can be then used for prediction on unseen data with the command at the top, indicating now which classifier should be used:

mriqc_clf --load-classifier myclassifier.pklz -X aMRIQC.csv -o mypredictions.csv

Predictions are stored as a CSV file, containing the BIDS identifiers as indexing columns and the predicted quality label under the prediction column.