Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images

Summary

This protocol describes the process of applying seven different automated segmentation tools to structural T1-weighted MRI scans to delineate grey matter regions that can be used for the quantification of grey matter volume.

Abstract

Within neuroimaging research, a number of recent studies have discussed the impact of between-study differences in volumetric findings that are thought to result from the use of different segmentation tools to generate brain volumes. Here, processing pipelines for seven automated tools that can be used to segment grey matter within the brain are presented. The protocol provides an initial step for researchers aiming to find the most accurate method for generating grey matter volumes from T1-weighted MRI scans. Steps to undertake detailed visual quality control are also included in the manuscript. This protocol covers a range of potential segmentation tools and encourages users to compare the performance of these tools within a subset of their data before selecting one to apply to a full cohort. Furthermore, the protocol may be further generalized to the segmentation of other brain regions.

Introduction

Neuroimaging is widely used in both clinical and research settings. There is a current move to improve the reproducibility of studies that quantify brain volume from magnetic resonance imaging (MRI) scans; thus, it is important that investigators share experiences of using available MRI tools for segmenting MRI scans into regional volumes, to improve the standardization and optimization of methods¹. This protocol provides a step-by-step guide to using seven different tools to segment the cortical grey matter (CGM; grey matter which excludes subcortical regions) from T1-weighted MRI scans. These tools were previously used in a methodological comparison of segmentation methods², which demonstrated variable performance between tools on an Huntington's disease cohort. Since performance of these tools is thought to vary among different datasets, it is important for researchers to test a number of tools before selecting only one to apply to their dataset.

Grey matter (GM) volume is regularly used as a measure of brain morphology. Volumetric measures are generally reliable and able to discriminate between healthy controls and clinical groups³. The volume of different tissue types of brain regions is most often calculated using automated software tools that identify these tissue types. Thus, to create high quality delineations (segmentations) of the GM, accurate delineation of the white matter (WM) and cerebrospinal fluid (CSF) is critical in achieving accuracy of the GM region. There are a number of automated tools that may be used for performing GM segmentation, and each requires different processing steps and results in a different output. A number of studies have applied the tools to different datasets to compare them with one other, and some have optimized specific tools^{1,4,5,6,7,8,9,10,11}. Previous work has demonstrated that variability between volumetric tools can result in inconsistencies within the literature when studying brain volume, and these differences have been suggested as driving factors for false conclusions being drawn about neurological conditions¹.

Recently, a comparison of different segmentation tools in a cohort that included both healthy control participants and participants with Huntington's disease was performed. Huntington's disease is a genetic neurodegenerative disease with a typical onset in adulthood. Gradual atrophy of subcortical and CGM is a prominent and well-studied neuropathological feature of the disease. The results demonstrated variable performance of seven segmentation tools that were applied to the cohort, supporting previous work that demonstrated variability in findings depending on the software used to calculate brain volumes from MRI scans. This protocol provides information on the processing used in Johnson et al. (2017)² that encourages careful methodological selection of the most appropriate tools for use in neuroimaging. This manual covers the segmentation of GM volume but does not cover the segmentation of lesions, such as those seen in multiple sclerosis.

Protocol

Note: Ensure that all images are in NifTI format. Conversion to NifTI is not covered here.

1. Segmentation via SPM 8: Unified Segment

NOTE: This procedure is performed via the SPM8 GUI which operates within Matlab. The SPM8 guide provides further detail and can be found at: http://www.fil.ion.ucl.ac.uk/spm/doc/spm8_manual.pdf.

1. Make sure that SPM8 is installed and set in the software path.
2. SPM segmentation is performed using a GUI. To open SPM, open a command window and type 'spm' into the command line.
3. Press 'PET & VBM' to open the structural MRI toolbox.
4. Press 'Batch' to open the Batch Editor. This allows segmentation to be performed on multiple scans at a time.
5. Select 'SPM | Spatial | Segment'.
6. Click 'Data | Select Files'. Choose the T1-weighted scans as input.
   NOTE: The files must be unzipped NifTi files, with the extension being '.nii'.
7. Click on 'output files | Grey Matter' and ensure that 'Native Space' is selected, do the same for White Matter. If the CSF segmentation is not required leave this as 'None'.
8. If the scans have already been bias-corrected, change the 'Bias Corrected' option to 'Don't Save Corrected'. For the 'Clean up any partitions' option, test the three different options and use visual quality control (QC, Section 8) to determine which works best for the data.
9. Leave the other settings as their defaults. Then, click on the green flag to run the segmentation.
   NOTE: This takes around 5 minutes per participant, and the command line will say, 'Running Segment'. When finished, the command window will display 'Done'.
10. Perform visual QC on the GM (C1*.nii file) as described in Section 8.

2. Segmentation via SPM 8: New Segment

NOTE: This procedure is performed via the SPM8 GUI. The SPM8 guide provides further detail and can be found at: http://www.fil.ion.ucl.ac.uk/spm/doc/spm8_manual.pdf. Make sure that SPM8 is installed and set in the software path. Open the SPM software, typically performed by typing "spm" into a command line. This opens a graphical user interface (GUI) window with a range of options that can be selected to perform analysis. 

1. Press 'PET & VBM'.
2. Press 'Batch' to open the Batch Editor.
3. Select 'SPM | Tools | New Segment' in the Batch window. Select the T1 image files (with extension '.nii').
4. Set 'Native Tissue type' to 'Native Space'. As needed, turn off the different tissue classes (such as CSF) - if not required - by setting them to 'None'. Set 'Warped Tissue' to 'None'.
   NOTE: All other options can be left as the default setting.
5. Click the green flag to run the segmentation.
   NOTE: The command line will say, 'Running New Segment'. Once it has finished running the MATLAB command line will say, 'Done New Segment'.
6. Perform visual QC on the GM (C1*.nii file) as described in Section 8.

3. Segmentation via SPM 12: Segment

NOTE: This procedure is performed via the SPM12 GUI. The SPM12 guide provides further detail and can be found at:http://www.fil.ion.ucl.ac.uk/spm/doc/manual.pdf.

1. Open the SPM software by typing 'spm' into the command window. This opens a graphical user interface (GUI) window with a range of options that can be selected to perform analysis.
2. Press 'PET & VBM'. Press 'Batch' to open the Batch Editor.
3. Click on 'SPM | Spatial | Segment'. Then, click on 'Data | Volumes'.
4. Set 'Native Tissue type' to 'Native Space'. Turn off the tissue classes that are not required (such as CSF) by setting them to 'None'. Set 'Warped Tissue' to 'None'.
   NOTE: All other options can be left as default setting.
5. Click the green flag to run the segmentation.
   NOTE: The command window will display: 'Running Segment'. Once the run is complete, it will display: 'Done Segment'.
6. Perform visual QC on the GM (C1*.nii file) as described in Section 8.

4. Segmentation via FSL FAST

NOTE: This procedure is done in the command line. The FSL guide provides further detail and can be found at: https://fsl.fmrib.ox.ac.uk/fsl/fslwiki.

1. Run BET brain extraction. This may need to be optimized for different datasets, but the basic command is:
   bet T1_ID.nii bet_T1_ID.nii
2. Run FSL FAST segmentation:
   fast bet_T1_ID.nii
   NOTE: This will output partial volume maps and binary regions for GM, CSF, and WM.
3. Perform visual QC on the GM region (the file ending *_pve_1.nii.gz) as described in Section 8.

5. Segmentation via FreeSurfer

NOTE: This procedure is done in the command line. The FreeSurfer guide provides further detail and can be found at: https://surfer.nmr.mgh.harvard.edu/.

1. Set the directory where the data is by typing:
   export SUBJECTS_DIR=/path/to/nii/files
2. Run the segmentation by running the commands:
   recon-all -i T1_ID.nii -subjid T1_ID -autorecon1 -cw256
   recon-all -subjid T1_ID -autorecon2 -autorecon3
   NOTE: The commands take > 10 h per participant. The -cw256 flag is needed to crop scans with fields of view larger than 256 down to this size for processing.
3. Check that processing has completed correctly by looking at the script located in the 'output folder | scripts | recon-all.log'. Check that the last line says 'recon-all -s T1_ID finished without error'.
4. Perform visual QC on the GM region as described in Section 8.

6. Segmentation via ANTs

NOTE: This procedure is done in the command line. ANTs is a more complex software than the other tools and it should be noted that the procedure explained here could be further optimised for each cohort to improve the results. ANTs documentation can be found at: http://stnava.github.io/ANTsDoc/. There are two ways to segment the images into tissue classes as described below. 

1. To use the first method, run the command 'antsAtropos.sh' with default settings and without including tissue priors.
   NOTE: This often performs well especially when only 3 tissue classes are required: GM, WM, other.
   1. Set the path to ANTs software by typing the command:
      export ANTSPATH=/path/to/ANTs/bin/
   2. Run the segmentation pipeline by typing the command:
      antsAtroposN4.sh -d <image_dimension> -a <t1.nii.gz> -c <number of tissue classes> -o <output>
      1. Optional arguments for this command are:
         Brain mask: -x <mask.nii.gz>;
         Tissue priors: -p <segmentationPriors%d.nii.gz>.
   3. This will create a folder with the output including partial volume maps and an extracted brain. Perform visual QC on the GM region as described in Section 8.
2. To generate more tissue classes (GM, subcortical GM, WM, CSF, other, etc.) or perform the segmentation on a cohort showing neural pathology, use specific tissue priors. Download tissue priors from different websites. Alternatively, use a study-specific template to make priors - this is much more complex but can be beneficial, especially in cohorts with pathological brain changes.
   1. To create a study-specific template/priors, first create a study-specific template:
      antsMultivariateTemplateConstruction.sh -d <image_dimension> -o template <other options> <images.nii.gz>
      1. Optional arguments for this command are:
         -c: control for parallel computation.
         If running in serial, use a 0; -j: number of cores; -r: do rigid-body registration of inputs before creating template (default 0) -- 0 == off 1 == on. This is only useful when an initial template is not available.
   2. Download a brainmask and priors from the ANTs website.
      NOTE: This mask may need to be edited to make sure it is a good approximation of the template brain. The brainmask is one of the most important parts of the pipeline; if it is poor, then brain extraction/Atropos will run poorly. Some of the download options are:
      https://figshare.com/articles/ANTs_ANTsR_Brain_Templates?915436.
      The downloaded template should then be registered to the study template.
   3. Calculate the registration, which will output a series of warps that can then be applied to the downloaded template to transform it to study-specific template space. To calculate the registration, use the command:
      antsRegistrationSyNQuick.sh -d 3 -f template.nii.gz -m downloaded_template.nii.gz -o downloaded_to_template -n 6
      1. The options in this command are:
         -d: dimension (i.e., 3D scans would be '3'); -f: fixed image (i.e., the space where the images need to end up); -m: moving image (i.e., the image that needs to be moved); -o: output name (no extension needed); -n: number of threads.
   4. Apply the registration to the data:
      antsApplyTransforms -d 3 -i downloaded_template.nii.gz -r template.nii.gz -o downloaded_to_template.nii.gz -t downloaded_to_template1Warp.nii.gz -t downloaded_to_template0GenericAffine.mat.
      1. The options in this command are:
         -d: dimension (i.e., 3D scans would be '3'); -i: input image (i.e., the image that needs to be moved); -r: reference image (i.e., the reference image defines the spacing, origin, size, and direction of the output warped image); -o output name, this is the downloaded template in the study-specific template space (extension needed in this case); -t transform file name, the output file from the registration calculation.
   5. Visually check the registration for correspondence between the study-specific template and downloaded template (to do this, open the study-specific template on top of the downloaded template).
   6. If the registration has worked, apply the transformation to the downloaded priors and extracted template brain, repeating step 6.2.5.
      NOTE: Following these steps, there will be a study-specific template, a downloaded template aligned with the study-specific template, along with a downloaded brain extraction mask and tissue priors also aligned with the study-specific template.
   7. Run the study specific template through antsCorticalThickness.sh; this provides GM, WM, and CSF regions that can be used for study-specific priors:
      antsCorticalThickness.sh -d 3 -a template.nii.gz -e downloaded_to_template.nii.gz -m downloaded_binarised_template_extracted_brain_in_studyspace.nii.gz -p downloaded_labelsPriors%d.nii.gz -o CT_template
      1. The options in this command are:
         -d: dimension (i.e., 3D scans would be '3'); -a: image to be segmented (in this case, the study-specific template); -e: brain template (not skull stripped; in this case, the downloaded template that has been registered to the study-specific template); -m: downloaded brain extraction mask (in this case, the extracted brain from the downloaded template that has been registered to the study-specific template); -p: priors specified using c-style formatting (e.g., -p labelsPriors%02d.nii.gz).
         NOTE: The command assumes that the first four priors are ordered as follows: 1: CSF, 2: cortical GM, 3: WM, and 4: subcortical GM (in this case, the priors from the downloaded template that has been registered to the study-specific template).
   8. Running this command will result in generated priors for the template, but they need smoothing prior to use in Atropos segmentation. The smoothing command is part of ANTs software. Smooth all priors using the command:
      SmoothImage 3 CT_template_BrainSegmentationPosteriors2.nii.gz CT_template_BrainSegmentationPosteriors_smoothed.nii.gz
   9. Prior to running Atropos, run brain extraction on all native space scans. The study-specific template can be used, and extracted brain generated from running antsCorticalThickness.sh on the template (step 6.2.1):
      antsBrainExtraction.sh -d 3 -a T1.nii.gz -e template.nii.gz -m template_BrainExtractionBrain.nii.gz -o T1_brain.nii.gz
      1. The options in this command are:
         -d: dimensions; -a: anatomical image; -e: brain extraction template (i.e., template created, without skull stripping); -m: study specific brainmask used for brain extraction; -o: output prefix.
   10. Then run Atropos:
       antsAtroposN4.sh -d 3 -a T1.nii.gz -x T1_brain.nii.gz -c 3 -o Atropos_specific_template
       1. The options in this command are:
          -d = dimensions; -a: anatomical image; -x: brain extraction mask generated from the brain extraction; -c: number of tissue classes to segment; -o: output prefix; -p: study-specific segmentation priors <segmentationPriors%d.nii.gz>
   11. Perform visual QC on the GM region as described in Section 8.

7. Segmentation via MALP-EM

1. To run MALP-EM, open a terminal window, change the directory into the MALP-EM install directory and type:
   ./malpem-proot -i T1_scan.nii -o ./ -m optional_brain_mask_final.nii.gz -f 3T -t 6 -c
2. Once the command has been completed, check that there is an output folder with tissue classes and regional segmentations.
3. Perform visual QC on the GM as described in Section 8.

8. Visual Quality Control

NOTE: Visual quality control should be performed on all segmented regions to be used in the analysis. Quality control ensures that the segmentations are of a high standard and represent reliable segmentation of the CGM. To perform quality control, each scan is opened and overlaid on the original T1 to compare the generated region to the CGM visible on the scan.

1. SPM, FSL, ANTs, and MALP-EM Segmentations
   1. Perform visual QC using FSLeyes:
      https://users.fmrib.ox.ac.uk/~paulmc/fsleyes_userdoc/
      Note: FSLview (an older viewer) can also be used in the same way.
   2. Open a terminal window and open the T1 and the GM regions overlaid on the T1. To do this, type:
      fsleyes T1.nii Region1.nii Region2.nii.
   3. Once FSLeyes opens, use the opacity toggle on the top pane to adjust/reduce the opacity and allow visualisation of the T1 image underneath the GM region. Change the color of the segmentation overlay via the 'color dropdown tab' in the top pane.
   4. Scroll through every slice in the brain.
      NOTE: Here this is done using the coronal view, but users should use the view that they have most experience with.
   5. Check every slice for regions of under- or over-estimation of the region being inspected.
      NOTE: See the representative results section for examples of good and bad segmentations.
2. FreeSurfer QC
   1. Peform visual QC using FreeView.
      NOTE: Refer to the documentation here:
      https://surfer.nmr.mgh.harvard.edu/fswiki/FreeviewGuide/FreeviewGeneralUsage/FreeviewQuickStart.
   2. Open a terminal window. To view the volumetric GM region overlaid on the T1, change directory to the subject folder and type:
      freeview ./mri/T1.mgz ./mri/aparc+aseg.mgz:colormap=lut:opacity:.3
   3. Scroll through every slice in the brain.
      NOTE: Here this is done using the coronal view, but users should use the view that they have most experience with.
   4. Check every slice for regions of under- or over-estimation of the region being inspected.
      NOTE: See the representative results section for examples of the segmentations.

Results

Average brain volumes for 20 control participants, along with demographic information, is shown in Table 1. This acts as a guide for expected values when using these tools. Results should be viewed in the context of the original T1.nii image. All GM regions should be inspected as per the steps described in section 8. When performing visual QC, it is important to directly compare the GM regions to the T1 scan by viewing them overlaid on the T1.

Discussion

Recently, research has demonstrated that the use of different volumetric methods may have important implications for neuroimaging studies^1,2. By publishing protocols that help guide novice users in how to apply different neuroimaging tools, as well as how to perform QC on the results output by these tools, researchers may select the best method to apply to their dataset.

While most steps in this SOP can be adjusted to suit the data and...