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quality_check - Evaluate registration quality

The quality check procedure relies on a pretrained network that learned to classify images that are adequately registered to a template from others for which the registration failed. It reproduces the quality check procedure performed in [Wen et al., 2020]. It is an adaptation of [Fonov et al., 2018], using their pretrained models. Their original code can be found on GitHub.

Warning

This quality check procedure is specific to the t1-linear pipeline and should not be applied to other preprocessing procedures as the results may not be reliable. Moreover you should be aware that this procedure may not be well adapted to anonymized data (for example images from OASIS-1) where parts of the images were removed or modified to guarantee anonymization.

Prerequisites

You need to execute the clinicadl preprocessing and clinicadl extract pipelines prior to running this task.

Running the task

The task can be run with the following command line: 

clinicadl quality_check <caps_directory> <tsv_path> <output_path>
where:

  • caps_directory(str) is the folder containing the results of the t1-linear pipeline and the output of the present command, both in a CAPS hierarchy.
  • tsv_path (str) is the path to a TSV file containing the subjects/sessions list to process (filename included).
  • output_path (str) is the path to the output TSV file (filename included).

Options:

  • --threshold (float) is the threshold applied to the output probability when deciding if the image passed or failed. Default value: 0.5.
  • --batch_size (int) is the size of the batch used in the DataLoader. Default value: 1.
  • --nproc (int) is the number of workers used by the DataLoader. Default value: 2.
  • --use_cpu (bool) forces to use CPU. Default behaviour is to try to use a GPU and to raise an error if it is not found.

Outputs

The output of the quality check is a TSV file in which all the sessions (identified with their participant_id and session_id) are associated with a pass_probability value and a True/False pass value depending on the chosen threshold. An example of TSV file is:

participant_id session_id pass_probability pass
sub-CLNC01 ses-M00 0.9936990737915039 True
sub-CLNC02 ses-M00 0.9772214889526367 True
sub-CLNC03 ses-M00 0.7292165160179138 True
sub-CLNC04 ses-M00 0.1549495905637741 False
... ... ... ...