t1-fsl – FSL-based pre-processing of T1-weighted MRI imagesLink

This pipeline performs segmentation of tissues (GM, WM, CSF), of subcortical structures and brain extraction (i.e. skull-stripping) from a T1-weighted MRI. To that aim, it wraps different tools of the FSL package: bet for brain extraction [Smith et al., 2002], fast for tissue segmentation and (optionally) bias correction [Zhang et al., 2001] and first for segmentation of the subcortical structures [Patenaude et al, 2011].

DependenciesLink

If you only installed the core of Clinica, this pipeline needs the installation of FSL on your computer. You can find how to install this software on the installation page.

Running the pipelineLink

The pipeline can be run with the following command line:

clinica run t1-fsl (-is_bias_corrected | -is_not_bias_corrected) bids_directory caps_directory subjects_sessions_tsv
where:

  • bids_directory is the input folder containing the dataset in a BIDS hierarchy.
  • caps_directory is the output folder containing the results in a CAPS hierarchy.
  • -is_bias_corrected | -is_not_bias_corrected is a mutually exclusive flag that specifies if your images are bias field corrected or not.

OutputsLink

Results are stored in the following folder of the CAPS hierarchy: subjects/sub-<participant_label>/ses-<session_label>/t1/fsl.

The main output files are:

  • <source_file>_tissue-csf_binaryMask.nii.gz: binary image of the segmented CSF.
  • <source_file>_tissue-gray-matter_binaryMask.nii.gz: binary image of the segmented Gray Matter.
  • <source_file>_tissue-white-matter_binaryMask.nii.gz: binary image of the segmented White Matter.

The full list of output files from FSL pipeline can be found in the The ClinicA Processed Structure (CAPS) Specification.

Describing this pipeline in your paperLink

Example of paragraph:

These results have been obtained using the t1-fsl pipeline of Clinica. This pipeline is a wrapper of the different tools of the FSL software (https://fsl.fmrib.ox.ac.uk/fsl/fslwiki): bet for brain extraction [Smith et al., 2002], fast for tissue segmentation and (optionally) bias correction [Zhang et al., 2001] and first for segmentation of the subcortical structures [Patenaude et al, 2011].

Tip

Easily access the papers cited on this page on Zotero.

Appendix: main steps of the t1-fsl pipelineLink

Preliminary step: pre-masking of the T1-weighted imageLink

Prior to performing brain extraction, we perform an initial masking of the image to standard space thanks to the FSL standard_space_roi command. flirt is used to register the input image to a standard space whole-head image; the resulting transformation is then inverted and a standard-space brain mask is transformed into the space of the input image, and finally applied to the image. We use the MNI 1 mm dilated brain mask as advised by the MRtrix community.

Brain extractionLink

bet (Brain Extraction Tool) [Smith et al., 2002] is used to remove non-brain tissue from an image of the whole head.

Tissue segmentation and bias correctionLink

The FMRIB’s Automated Segmentation Tool [Zhang et al., 2001], also known as fast, is used to perform generic tissue-type segmentation and bias field correction. It expects a brain-extracted image as input and segments into different tissue types. In parallel, it estimates the bias field of the image (this step is optional).

Gray nuclei segmentationLink

The FMRIB’s Integrated Registration & Segmentation Tool [Patenaude et al, 2011], also known as first, performs the segmentation of subcortical brain structures.