Structural Analysis Practical

In this practical you will learn to use the main tools for structural analysis: FAST (tissue-type segmentation), FIRST (sub-cortical structure segmentation) and FSL-VBM (local grey matter volume difference analysis). In addition there are several optional extensions, including BIANCA and SIENA, that will be relevant to those with interests in particular types of structural analysis. We advise people to pick and choose based on their own particular interests; everyone should do FAST, but after that the parts are quite separate and can be done in any order (e.g. people particularly interested in VBM might want to do that before the section on FIRST).

FAST: Perform tissue-type segmentation and bias-field correction using FAST.
FIRST: Use FIRST for segmentation of sub-cortical structures. Introduces basic segmentation and vertex analysis for detecting group differences.
FSL-VBM: Perform an FSL-VBM (voxel-based morphometry) analysis for detecting differences in local grey matter volume.

Optional extensions:

BIANCA: Use BIANCA to detect white matter lesions.
SIENA: Use SIENA for detecting global grey-matter atrophy in longitudinal scans.
SIENAX: An introduction to the cross-sectional version of SIENA.

Advanced extensions:

For more advanced practical material visit this page, which includes some of the more advanced usage and concepts in FAST, FIRST and SIENA:

An introduction to the multi-channel version of FAST for use with multiple acquisitions (e.g. T1-wt, T2-wt, PD, ...).
FIRST revisited: looking at the "uncorrected" FIRST outputs.
Vertex Analysis on FIRST output meshes using fsl_utils.
SIENA "LOOK AT YOUR DATA" to investigate some problem cases.

FAST

In this section we will segment single T1-weighted images with FAST and look at how to quantify the grey matter volume and amount of bias field present.

cd ~/fsl_course_data/seg_struc/fast

FAST Input Preparation - BET

To begin with we will prepare data for FAST; this requires running BET for brain extraction. In addition, just for this practical, we will also extract a small ROI containing a few central slices so that FAST only takes a minute to process the data, instead of 10-15 minutes for a full brain.

Run BET on the input image structural to create structural_brain (type Bet for the GUI [Bet_gui on a Mac], or bet for the command-line program).

Look at your data

View the output to check that BET has worked OK (e.g. change the colourmap for structural_brain to say Red-Yellow):

fsleyes structural structural_brain &

Back in the terminal, create a cut-down version (containing a few central slices) of the brain-extracted image using the region-of-interest program fslroi. This will let you try out some of the FAST options without having to wait more than a minute each time.

fslroi structural_brain structural_brain_roi 0 175 0 185 100 5

Load structural_brain_roi into FSLeyes to see the cut-down image. See how few slices are left. Leave FSLeyes open for the moment.

Image with Bias Field

You will also find an image in this directory called structural_brain_7T.nii.gz which contains a section of the same brain acquired on a 7 Tesla scanner with a different bias field or inhomogeneity, and a cut-down version structural_brain_7T_roi.nii.gz.

Add structural_brain_7T_roi.nii.gz to the already open FSLeyes (or open a new one and load structural_brain_roi first) and then look at the difference between these images. Note how both grey matter and white matter are darker in the left anterior portion of the 7T image.

FAST - Single Channel Example

Run FAST (separately) on both structural_brain_roi and structural_brain_7T_roi. Use the GUI (Fast [or Fast_gui on a Mac]) and turn on the Estimated bias field button (which saves a copy of the bias field) and Restored input button (which corrects the original image with the calculated bias field). For both images also open the Advanced Options tab and change the Number of iterations for bias field removal to 10 to account for the strong bias fields in both cases.

Finally, don't forget to check that the output name is different for the two runs (structural_brain_roi and structural_brain_7T_roi)! Once this is set up press Go for both - they should only take a minute to run.

digraph G { rankdir=LR node [shape=box]; 1 [label="structural, T1 image"]; 2 [label="Fast"]; 3 [label="Bias field estimation"]; 4 [label="Tissue segmentation"]; 5 [label="Partial volume estimates"]; 1 -> 2; 2 -> 3; 2 -> 4; 2 -> 5; }

Bias Field Correction

Now let's look at the bias field outputs - structural_brain_roi_bias and structural_brain_7T_roi_bias (these are FAST's estimates of the bias fields). View these in FSLeyes and set the display ranges to be equal for both images (e.g. 0.6 to 1.4). Notice how different the two bias fields are.

Why do you think there might be a worse bias field when acquiring images on different scanners, such as that seen in the brain scan from the 7 Tesla scanner?

Scanners with higher B0 fields have less homogeneous B1 fields, causing a larger bias. Correct! This is actually because the wavelength of the RF (B1) fields get smaller at higher B0 fields and so the bias field is less smooth and thus has greater inhomogeneities.
Subjects tend to feel more claustrophobic in 7T scanners (due to the long bore) and hence move around more and make the bias field worse. Incorrect. Movement artifacts can interact with the bias field, but the bias field itself is not stronger due to movement.
It is more difficult to design gradient coils for 7T scanners and one of the compromises that has to be made for better SNR is to have larger gradient inhomogeneities. Incorrect. Bias field is due to RF (B1) fields, not the gradient fields.

Open both of the *_seg.nii.gz output segmentations in FSLeyes. Try using different colour maps for the segmentations when viewing the results. You can now see how a different bias field can alter the segmentation of the image.

Partial Volume Segmentation

Now let's look at the partial volume segmentations. View the different outputs in FSLeyes by first loading structural_brain_roi_restore, then loading the PVE (Partial Volume Estimate) images as overlays, adjusting the overlay opacity as necessary. Note that you can tell FSLeyes what colourmaps and intensity ranges to use from the command line:

fsleyes structural_brain_roi_restore \
  structural_brain_roi_pve_0 -cm green -dr 0.5 1 \
  structural_brain_roi_pve_1 -cm blue-lightblue -dr 0.5 1 \
  structural_brain_roi_pve_2 -cm red-yellow -dr 0.5 1 &

Identify which PVE component is the grey matter. Choose a voxel on the border of the grey matter and look at the values contained in the three PVE components. The values represent the volume fractions for the 3 classes (GM, WM, CSF) and should add up to one. Now pick a point in the middle of the grey matter and look at the three values here.

The PVE images are the most sensitive way to calculate the tissue volume which is present. For example, we can find the total GM volume with fslstats by doing:

fslstats structural_brain_roi_pve_1 -M -V

The first number reported by fslstats gives the mean voxel GM PVE across the image ignoring all voxels which are zero. The second is the number of such non-zero voxels and the third number gives the total volume covered by these non-zero voxels in the image (in mm³). Multiplying the first and third numbers together will give the total GM volume in mm³ (for more details on fslstats just type fslstats to see its usage description).

For an introduction to the multi-channel version of FAST, for use with multiple acquisitions (e.g. T1-wt, T2-wt, PD, ...) see the advanced practical.

FIRST

In this section we lead you through examples of subcortical structure segmentation with FIRST, and some post-fitting statistical analyses.

cd ~/fsl_course_data/seg_struc/first/

Segmentation of structures

We begin by segmenting the left hippocampus and amygdala from a single T1-weighted image. The image is con0047_brain.nii.gz. Load this into FSLeyes to start with to see the image. Note that although this is not normally done, this image has had brain extraction run on it. This is due to the anonymisation done to the original image.

To perform the segmentation of the left hippocampus and amygdala we simply need to run one command:

run_first_all -i con0047_brain -b -s L_Hipp,L_Amyg \
  -o con0047 -a con0047_brain_to_std_sub.mat

This command will run several steps for you and has several options. While it is running read through the following description.

Options used in `run_first_all`

-i: specifies the input image (T1-weighted)
-o: specifies the output image basename (extensions will be added to this)
-b: specifies that the input image has been brain extracted
-s: specifies a restricted set of structures to be segmented (just two in this case)
-a: specifies the affine registration matrix to standard space (optional)

digraph G { rankdir=LR node [shape=box]; 1 [label="structural, T1 image"]; 2 [label="First"]; 3 [label="Structural image registered to standard space"]; 4 [label="3D subcortical structure segmentation (no overlap)"]; 5 [label="4D subcortical structure segmentation (before boundary correction)"]; 6 [label="3D mesh representation of final segmentation (*_first.vtk)"]; 7 [label="Mode/shape parameters (*_first.bvars)"]; 1 -> 2; 2 -> 3; 2 -> 4; 2 -> 5; 2 -> 6; 2 -> 7; }

The run_first_all script uses the best set of parameters (number of modes, intensity reference) to run for each structure, as determined by empirical experiments. Therefore it is not necessary to specify these values when running the method.

Normally the affine registration would be run as part of this script (just leave off the -a option and it will be done automatically), but it has been pre-supplied here in order to save time - as the registration takes about 6 minutes.

We will now go through how this script works and what to look for in the output.

Check the registration

Load the image con0047_brain_to_std_sub.nii.gz together with the 1mm standard space template image into FSLeyes. Look at the alignment of the subcortical structures. It should be quite close but we do not expect it to be perfect.

This registration is normally created by run_first_all as the initial stage, but has been included here from a previous run to save time. The registration should always be performed using the tools in FIRST since it does a special registration, optimised for the sub-cortical structures. It begins with a typical 12 DOF affine registration using FLIRT, but then refines this in a second stage with a sub-cortical weighting image that concentrates purely on the sub-cortical parts of the image. Thus the final registration may not be as good in the cortex but will better fit the sub-cortical structures. However, this registration only corrects the global affine component of the differences in the structures and therefore will not be highly precise. Importantly, it leaves the relative positions and orientations of the structures unchanged.

Always make sure you check that the registration has worked before looking at other outputs.

We will now move onto looking at the other outputs which should have been generated by run_first_all at this point. If the run_first_all command has not finished have a quick look at the FIRST documentation page.

Before doing anything else we will check the output logs to see if any errors have occured. Do this with the command:

cat con0047.logs/*.e*

You should always check the error files in the log directories for FIRST and other FSL commands that create log directories like this (e.g. TBSS, FSL-VBM, BEDPOSTX, etc.).

If everything worked well you will see no output from this, otherwise it will show the errors. If any errors are shown, ask a tutor about them.

Boundary corrected segmentation output

In FSLeyes, open the image con0047_brain and add the image con0047_all_fast_firstseg on top.

This *_firstseg image shows the combined segmentation of all structures based on the surface meshes that FIRST has fit to the image (change colourmap to e.g. Random to see the different structures in different colours). It is in the native space of the structural image (not in the standard space, although the registration before was required to move the model from the standard space back into this image's native space).

As converting the underlying FIRST meshes to a voxel-based image can create overlap at the boundaries, these boundary voxels have been "corrected" or re-classified by run_first_all using the default method (here it is FAST - which classifies the boundary voxels according to intensity). Now look at the uncorrected segmentations with the following:

fsleyes con0047_brain con0047_all_fast_origsegs.nii.gz \
  -cm Red-Yellow -dr 0 118 &

The con0047_all_fast_origsegs image is a 4D image with each structure in a different volume. The intensity values assigned to the interior of each structure is given by the CMA labels. Each structure is labeled with a different intensity value inside and 100 + this value for the boundary voxels.

Have a look at these images to see how good the segmentation is. Play with the opacity settings (or turn the segmentation on and off) to get a feeling for the quality.

The corrected image (*_firstseg) is normally the one that you would use to define an ROI or mask for a particular subcortical structure. For more details on the uncorrected image (*_origsegs) see the advanced practical.

For an introduction to Vertex Analysis on the mesh output of FIRST using fsl_utils, see the advanced practical.

FSL-VBM

In this section we look at a small study comparing patients and controls for local differences in grey matter volume, using FSL-VBM. Most of the steps have already been carried out, as there isn't enough time in this practical to run all of the registrations required to carry out a full analysis from scratch.

cd ~/fsl_course_data/seg_struc/vbm

Do an ls in the directory. Note that we have renamed the image files with some prefixes so that all controls and patients would be organised in "blocks". This is to make the statistical design easily match the alphabetical order of the image files (who will be later concatenated to be statistically analysed).

We have 10 controls and 8 patients and wish to carry out a control>patient comparison. First, we need to define the statistical design, which here will be a simple two-tailed t-test to compare both groups. For this, use the Glm GUI to generate simple design.mat and design.con files, using the Higher-level/non-timeseries design option in the GLM setup window.

At this point, you need to enter the appropriate overall number of subjects as inputs in the GLM setup window (here n=18, then press enter), and then use the Wizard button of the GLM setup window with the two groups, unpaired option and appropriate number of subjects for the first group (here ncontrols=10). If the design looks correct (if you are not sure, ask a tutor), then save it by pressing Save in the GLM setup window and give it the output basename of design. In this analysis, only the design.mat and design.con files will be used.

Moreover, since we have more controls than patients, you will need to list the subjects used for the creation of an unbiased study-specific template by missing out 2 controls (for instance the last two: con_3699.nii.gz and con_4098.nii.gz), so that the number of controls used to build this study-specific template matches the number of patients in the template_list text file (we have provided this for you here). The contents of this file should therefore look like this:

con_1623.nii.gz
con_2304.nii.gz
con_2878.nii.gz
con_3456.nii.gz
con_3641.nii.gz
con_3642.nii.gz
con_3668.nii.gz
con_3670.nii.gz
pat_1433.nii.gz
pat_1650.nii.gz
pat_1767.nii.gz
pat_2042.nii.gz
pat_2280.nii.gz
pat_2632.nii.gz
pat_2662.nii.gz
pat_2996.nii.gz

Check the contents of this template_list file by doing: cat template_list

Preprocessing

We first ran the initial FSL-VBM script:

fslvbm_1_bet

digraph G { rankdir=LR node [shape=box]; 1 [label="All subject T1 images (listed in template_list text file)"]; 1b [label="Options"]; 2 [label="fslvbm_1_bet"]; 3 [label="Brain extracted subject T1 images"]; 1 -> 2; 1b -> 2; 2 -> 3; }

This moved all the original files into the origdata folder; to see what they all look like, run this command to view the slicesdir report in a web browser:

firefox origdata/slicesdir/index.html &

The fslvbm_1_bet command has also created some brain-extracted images. We actually ran fslvbm_1_bet both with the ‘default’ -b option and then, because the original images have a lot of neck in them, which was often being left in by the default brain extractions, we ran using the -N option. Compare the different results from the two options by loading in the two web pages:

firefox struc/slicesdir-b/index.html &
firefox struc/slicesdir-N/index.html &

It should be very obvious which option is working well and which one isn't!

Next, all the brain images are segmented into the different tissue types, and then the study-specific GM template is created, by registering all GM segmentations to standard space, and averaging them together. The command used was:

fslvbm_2_template -n

digraph G { rankdir=LR node [shape=box]; 1 [label="Template list"]; 2 [label="All brain extracted subject T1 images"]; 3 [label="fslvbm_2_template"]; 4 [label="Study specific template"]; 5 [label="Each subject's registration to study specific template"]; 1 -> 3; 2 -> 3; 3 -> 4; 3 -> 5; }

You can view all of the alignments to the MNI152 initial standard space by running the following, and turning on FSLeyes movie mode ( Gear button ):

fsleyes -std struc/template_4D_GM -cm blue-lightblue &

and then view the alignment of the study-specific template to the MNI152 standard space with:

fsleyes -std struc/template_GM -cm blue-lightblue -dr 0.2 1 &

Finally, the registrations to the new, study-specific, template were run for all subjects, and modulated by the warp field expansion (Jacobian), before being combined across subjects into the 4D image stats/GM_mod_merg. An initial GLM model-fit is run in order to allow you to view the raw tstat images at a range of potential smoothings. This was achieved by running (don't run this!):

fslvbm_3_proc

digraph G { rankdir=LR node [shape=box]; 1 [label="All betted subject T1 files"]; 2 [label="Each subject's registration to study specific template"]; 3 [label="Study specific template"]; 4 [label="Non-linear registration"]; 5 [label="4D concatenated image of processed subject images (registered to template, Jacobian modulated and smoothed)"]; 1 -> 4; 2 -> 4; 3 -> 4; 4 -> 5; }

So now you can have a look at the initial raw tstat images created at the different smoothing levels, pick the one you "like" best. You can change the colour maps for each tstat in FSLeyes to more clearly see the differences.

cd stats
fsleyes template_GM -dr .1 1 \
  GM_mod_merg_s4_tstat1 -dr 2.3 6 \
  GM_mod_merg_s3_tstat1 -dr 2.3 6 \
  GM_mod_merg_s2_tstat1 -dr 2.3 6 &

The different images that you can see in the stats directory are:

GM_mask: the result of thresholding the mean (across subjects) aligned GM image at 1% and turning into a binary mask.
GM_merg: a 4D image containing all subjects' aligned GM images.
GM_mod_merg: the same as above, but after the GM images have been "modulated by the warp field Jacobian" (adjusted for warp expansion/contraction).
GM_mod_merg_s2 / 3 / 4: the same as above, but after Gaussian smoothing of 2, 3 and 4mm sigma.
GM_mod_merg_s2_tstat1 / s3 / s4: the raw t-statistic images from feeding the smoothed datasets into a GLM via randomise.
design.mat / design.con: the design matrix and contrast file specifying the cross-subject model that is fit to the data by randomise.
template_GM: the study-specific GM template that was derived as part of the FSL-VBM analyses, and to which all subjects' GM images were finally aligned to.

You are now ready to carry out the cross-subject statistics. We will use randomise for this, as the above steps are very unlikely to generate nice Gaussian distributions in the data. Normally we would run at least 5000 permutations (to end up with accurate p-values), but this takes a few hours to run, so we will limit the number to 100 (to get a quick-and-dirty result). We will also use TFCE thresholding (Threshold-Free Cluster Enhancement - this is explained in the randomise lecture) which is similar to cluster-based thresholding but generally more robust and sensitive.

For example, if you decide that the appropriate amount of smoothing is with a sigma of 3mm, then the following will run randomise with TFCE and a reduced number of 100 iterations:

randomise -i GM_mod_merg_s3 -o tmp -m GM_mask \
  -d design.mat -t design.con -n 100 -T

digraph G { rankdir=LR node [shape=box]; 5 [label="4D concatenated image of processed subject images (GM_mod_merg_s3.nii.gz)"]; 6 [label="Design files (design.mat and design.con)"]; 7 [label="Grey matter mask from study template (GM_mask.nii.gz)"]; 8 [label="Multiple comparison correction option (-T ; for TFCE)"]; 8b [label="Other options (-n 100 ; for 100 permutations)"]; 9 [label="randomise"]; 10 [label="Statistical outputs (basename of tmp)"]; 5 -> 9; 6 -> 9; 7 -> 9; 8 -> 9; 8b -> 9; 9 -> 10; }

Once randomise has finished use FSLeyes to look at the results (corrected for multiple comparisons) showing the local differences in grey matter volume between the two groups:

fsleyes template_GM -dr .1 1 \
  tmp_tfce_corrp_tstat1 -cm red-yellow -dr 0.8 1 &

Note that in this example we set the corrected p-threshold to 0.2 (i.e. 0.8 in FSLeyes), because of the reduced number of subjects in this example and hence low sensitivity to effect - you would not be able to get away with this in practice!

What would happen if you didn't smooth your results in a VBM analysis?

BIANCA (Optional)

In this section we will use BIANCA to segment white matter lesions, specifically white matter hyperintensities of presumed vascular origin (WMH).

cd ~/fsl_course_data/seg_struc/bianca

We will prepare our data, train BIANCA on 9 subjects with manual labels available (sub-001 to sub-009), and test (i.e. segment lesions) on data from the 10th subject (sub-010). We are grateful to Dr. Giovanna Zamboni for providing the datasets used in this practical.

Before running BIANCA

Look at your data

Change directory into one subject folder (e.g. sub-001) to identify the following files we are going to use for each subject:

FLAIR_brain.nii.gz : main structural image, brain extracted
FLAIR_Lesion_mask.nii.gz : binary manual lesion mask for the subjects used to train BIANCA
FLAIR_brain_to-MNI_xfm.mat : transformation matrix from subject space (main structural image) to standard space (optional). This is to be able to use spatial features (MNI coordinates)
T1_brain_to-FLAIR_brain.nii.gz : additional input (optional). Other modalities that can help the lesion segmentation (e.g. T1), all registered to the main image. Click here to see how it was obtained.
T1 was bias field corrected using FAST (e.g. within fsl_anat) and then registered to the FLAIR using FLIRT, 6 dofs:
```
fsl_anat --nosubcortseg -i T1
flirt -dof 6 -in T1.anat/T1_biascorr_brain.nii.gz -ref FLAIR_brain.nii.gz \
      -omat T1_brain_to-FLAIR_brain_xfm.mat -out T1_brain_to-FLAIR_brain.nii.gz
```

Look at your data on FSLeyes:

  fsleyes T1_brain_to-FLAIR_brain.nii.gz FLAIR_brain.nii.gz FLAIR_Lesion_mask.nii.gz -cm red -a 70 &

Master file preparation

Now we need to put the information on where to find these files for each subject in a text file (master file), which we will later give as input to BIANCA.
The master file is a text file containing one row per subject and, on each row, a list of all files for that subject (columns).
Note: the order of the columns is not important, as long as it is the same for each subject/row.

Note that we listed FLAIR_Lesion_mask.nii.gz also for sub-010, which has no manual mask. This because we need to maintain the same structure of columns in the master file.
Since we will tell BIANCA to segment lesions for this subject, sub-010/FLAIR_Lesion_mask.nii.gz will just act as a "placeholder" and BIANCA will not look for the image.

Have a quick look at the content of the file we have already prepared for you:

cd ~/fsl_course_data/seg_struc/bianca
cat masterfile.txt

This is how it should look like:

sub-001/FLAIR_brain.nii.gz sub-001/T1_brain_to-FLAIR_brain.nii.gz sub-001/FLAIR_brain_to-MNI_xfm.mat sub-001/FLAIR_Lesion_mask.nii.gz
sub-002/FLAIR_brain.nii.gz sub-002/T1_brain_to-FLAIR_brain.nii.gz sub-002/FLAIR_brain_to-MNI_xfm.mat sub-002/FLAIR_Lesion_mask.nii.gz
sub-003/FLAIR_brain.nii.gz sub-003/T1_brain_to-FLAIR_brain.nii.gz sub-003/FLAIR_brain_to-MNI_xfm.mat sub-003/FLAIR_Lesion_mask.nii.gz
sub-004/FLAIR_brain.nii.gz sub-004/T1_brain_to-FLAIR_brain.nii.gz sub-004/FLAIR_brain_to-MNI_xfm.mat sub-004/FLAIR_Lesion_mask.nii.gz
sub-005/FLAIR_brain.nii.gz sub-005/T1_brain_to-FLAIR_brain.nii.gz sub-005/FLAIR_brain_to-MNI_xfm.mat sub-005/FLAIR_Lesion_mask.nii.gz
sub-006/FLAIR_brain.nii.gz sub-006/T1_brain_to-FLAIR_brain.nii.gz sub-006/FLAIR_brain_to-MNI_xfm.mat sub-006/FLAIR_Lesion_mask.nii.gz
sub-007/FLAIR_brain.nii.gz sub-007/T1_brain_to-FLAIR_brain.nii.gz sub-007/FLAIR_brain_to-MNI_xfm.mat sub-007/FLAIR_Lesion_mask.nii.gz
sub-008/FLAIR_brain.nii.gz sub-008/T1_brain_to-FLAIR_brain.nii.gz sub-008/FLAIR_brain_to-MNI_xfm.mat sub-008/FLAIR_Lesion_mask.nii.gz
sub-009/FLAIR_brain.nii.gz sub-009/T1_brain_to-FLAIR_brain.nii.gz sub-009/FLAIR_brain_to-MNI_xfm.mat sub-009/FLAIR_Lesion_mask.nii.gz
sub-010/FLAIR_brain.nii.gz sub-010/T1_brain_to-FLAIR_brain.nii.gz sub-010/FLAIR_brain_to-MNI_xfm.mat sub-010/FLAIR_Lesion_mask.nii.gz

The master file can be prepared using any text editor (or with excel and exporting it in txt format), but it's quicker with some scripting (click here to see how you can do it).

cd ~/fsl_course_data/seg_struc/bianca
for sub in $(ls -d sub-???) ; do
  echo  ${sub}/FLAIR_brain.nii.gz \
        ${sub}/T1_brain_to-FLAIR_brain.nii.gz \
        ${sub}/FLAIR_brain_to-MNI_xfm.mat \
        ${sub}/FLAIR_Lesion_mask.nii.gz >> masterfile.txt
done

Running BIANCA

Now we can give the master file as input to BIANCA, together with details on where to find the information inside it, and some additional information:

digraph G { rankdir=LR node [shape=box]; 1 [label="Master file: --singlefile=masterfile.txt"]; 2 [label="Training: training subjects --trainingnums (rows n.) + lesion masks --labelfeaturenum (column n.) or training file --loadclassifierdata"]; 3 [label="Test: test subject --querysubjectnum (row n.) "]; 4 [label="Brain extracted image: --brainmaskfeaturenum (column n.)"]; 5 [label="BIANCA"]; 6 [label="Lesion Probability map: -o bianca_output.nii.gz"]; 7 [label="[optional] Training file: --saveclassifierdata"]; 8 [label="[optional] List of intensity features: --featuresubset (columns n.)"]; 9 [label="[optional] Transformation matrix to derive spatial features: --matfeaturenum (column n.)"]; 10 [label="other options (see documentation)"]; 1 -> 5; 2 -> 5; 3 -> 5; 4 -> 5; 5 -> 6; 5 -> 7; 8 -> 5; 9 -> 5; 10 -> 5 }

Run BIANCA with the following call (you can ignore warning messages):

bianca --singlefile=masterfile.txt --trainingnums=1,2,3,4,5,6,7,8,9 --labelfeaturenum=4 \
--querysubjectnum=10 --brainmaskfeaturenum=1 --featuresubset=1,2 --matfeaturenum=3 \
--trainingpts=2000 --nonlespts=10000 --selectpts=noborder -o sub-010/bianca_output \
--saveclassifierdata=mytraining -v

Try to understand what the different options/flags mean and check your answer here. For more details consult the help (type bianca in the terminal) or the BIANCA documentation page.

Look at the output

Let's have a look at the Lesion Probability Map:

fsleyes sub-010/FLAIR_brain.nii.gz sub-010/bianca_output.nii.gz -cm red-yellow -a 70 &

Play with the display range to see which (minimum) threshold gives you a good lesion segmentation. Keep FSLeyes open.

Since we used the option --saveclassifierdata we also have two additional outputs:

mytraining: file containing the features derived from the training points
mytraining_labels: file containing the labels (lesion/non-lesion) for the training points

After running BIANCA

Thresholding. BIANCA output is a probability map. However, for most of the applications we want a binary lesion mask, so we need to apply a threshold and binarise the image. In this case we chose a threshold of 0.9:

fslmaths sub-010/bianca_output.nii.gz -thr 0.9 -bin sub-010/bianca_output_bin.nii.gz

Add the thresholded lesion map in FSLeyes and look at the results. Keep FSLeyes open.

Masking. To further reduce false positive voxels, we can mask our output. For example, we can exclude areas we are either not interested in (e.g. the cortex), or where BIANCA currently struggles to correctly segment lesions (e.g. subcortical structures and cerebellum).

In this case we prepared such a mask for you: sub-010/prebaked/T1_bianca_mask_to-FLAIR_bin.nii.gz.
Have a look at it on FSLeyes.
How do you apply the mask to the lesion map using fslmaths? Check the command line here.

fslmaths sub-010/bianca_output_bin.nii.gz -mul sub-010/prebaked/T1_bianca_mask_to-FLAIR_bin.nii.gz \
          sub-010/bianca_output_bin_masked.nii.gz

or, alternatively

fslmaths sub-010/bianca_output_bin.nii.gz -mas sub-010/prebaked/T1_bianca_mask_to-FLAIR_bin.nii.gz \
          sub-010/bianca_output_bin_masked.nii.gz

Add the masked lesion map on FSLeyes and look at the results.

The mask we just used was created using BIANCA side-script make_bianca_mask (more details on the BIANCA documentation page)
Optional extension: to see how to create and apply the mask, click here.

The solution we adopted is to create a mask from T1 images, which excludes the cortical GM and the following structures: putamen, globus pallidus, nucleus accumbens, thalamus, brainstem, cerebellum, hippocampus, amygdala. We implemented this in the script make_bianca_mask (check usage on the help).

What would be the make_bianca_mask command line to use for subject sub-010?
What other FSL tools would you need to run to create the inputs for make_bianca_mask?
After creating the mask, do you need to run any other FSL tool in order to use the masks? Which one(s)?
Check your answer here.

Preprocessing: we need to run FAST to get T1_fast_pve_0, calculate the non-linear registration from T1 to MNI (FLIRT/FNIRT) and calculate inverse transformation (using invwarp)
Alternatively, all the files we need are part of the output from fsl_anat.
```
 fsl_anat --nosubcortseg -i sub-010/T1
```
Since it would take too long, we ran this for you, you can find the output in sub-010/prebaked/T1.anat
Command line (using filenames as output from fsl_anat):
```
make_bianca_mask sub-010/prebaked/T1.anat/T1_biascorr sub-010/prebaked/T1.anat/T1_fast_pve_0 \
                  sub-010/prebaked/T1.anat/MNI_to_T1_nonlin_field.nii.gz
```
Note that we use the CSF output of FAST (T1_fast_pve_0), not the GM. This is because GM segmentation using FAST can be compromised by the presence of WMH (which usually appear darker than the WM and can be misclassified as GM), In this approach, the cortical GM is excluded from the brain mask by extracting the cortical CSF from single-subject CSF pve map, dilating it to reach the cortical GM, and excluding these areas. The other structures are identified in MNI space, non-linearly registered to the single-subject image, and removed from the brain mask.
Postprocessing: we need to register the mask from T1 to FLAIR space, then apply it to the lesion map.
Since BIANCA requires that for each subject all the modalities are registered to the main image (FLAIR in this case), we already have the transformation matrix from T1 to FLAIR (T1_brain_to-FLAIR_brain_xfm.mat), we only need to apply it to the mask (after thresholding it to compensate for interpolation and binarising it):
```
flirt -in sub-010/prebaked/T1.anat/T1_biascorr_bianca_mask.nii.gz -ref sub-010/FLAIR_brain \
      -applyxfm -init sub-010/T1_brain_to-FLAIR_brain_xfm.mat -out sub-010/T1_bianca_mask_to-FLAIR

fslmaths sub-010/T1_bianca_mask_to-FLAIR -thr 0.25 -bin sub-010/T1_bianca_mask_to-FLAIR_bin
fslmaths sub-010/bianca_output_bin.nii.gz -mul sub-010/T1_bianca_mask_to-FLAIR_bin.nii.gz sub-010/bianca_output_bin_masked.nii.gz
```

Add the masked lesion map on FSLeyes and look at the results.

Volume calculation. How would you calculate the volume (in mm³) of the final lesion map using fslstats? Check your answer here.

fslstats sub-010/bianca_output_bin_masked.nii.gz -V

The second number is the volume in mm³ (the first is the number of voxels)

Extension: segment lesions for a new subject with existing BIANCA training file

In the example above, we trained and tested BIANCA within a single call. Since we saved the training files (features and labels) we can apply BIANCA to one (or more) new subject(s) without the need to re-train BIANCA. How would you use BIANCA to segment lesions on a new subject (e.g. sub-011, not present in this dataset), using the training file we obtained above? Consult the BIANCA documentation page if you are unsure. Check your answer here.

Here are the steps to run BIANCA on a new subject (here we are using sub-011 as an example, but note that data for this subject is not included in the dataset provided).

First, we need to create a new masterfile with the information about the files for the new subject (e.g. masterfile_new.txt)
Note: the order of the columns needs to be the same as in the masterfile used for creating the training.

echo  sub-011/FLAIR_brain.nii.gz \
      sub-011/T1_brain_to-FLAIR_brain.nii.gz \
      sub-011/FLAIR_brain_to-MNI_xfm.mat \
      sub-011/FLAIR_Lesion_mask.nii.gz >> masterfile_new.txt

Then we can run bianca using the new master file as input, with -querysubjectnum=1 (since there is only one row in the new masterfile), and using the option --loadclassifierdata to select the training file created earlier:

  bianca --singlefile=masterfile_new.txt --loadclassifierdata=mytraining --querysubjectnum=1 \
  --brainmaskfeaturenum=1 --featuresubset=1,2 --matfeaturenum=3 \
  -o sub-011/bianca_output -v

Note: also the features (intensities, --featuresubset, and spatial --matfeaturenum) used to estimate the lesions need to be the same as those used to create the training file.

If you want to run BIANCA on more than one new subject, you can either create a single master file for each subject and change the input each time with --querysubjectnum always 1, or you can add all the information about the new subjects in a common masterfile and point --querysubjectnum at a different row each time.

Advanced extension: training and testing with leave-one-out

If you are testing different BIANCA options to select the best ones to use, leave-one-out is a suitable approach. However, if your aim is to report BIANCA performance, you should derive the metrics from an unseen portion of the data or a new dataset.

If we want to evaluate BIANCA performance against the manual masks in an unbiased way, we should use a leave-one-out approach: the subject we want to evaluate BIANCA performance on should not be included in the training.
In BIANCA, if --querysubjectnum is also included in the --trainingnums list, it is automatically excluded from the training set. In this way we can look at how well the lesion mask obtained with BIANCA compares with the manual mask.

bianca --singlefile=masterfile.txt --trainingnums=1,2,3,4,5,6,7,8,9 --labelfeaturenum=4 --querysubjectnum=1 \
--brainmaskfeaturenum=1 --featuresubset=1,2 --matfeaturenum=3 \
--trainingpts=2000 --nonlespts=10000 --selectpts=noborder -o sub-001/bianca_output -v

Open the output on FSLeyes, together with the FLAIR image and the manual mask.

Performance metrics. If we want to quantify how well the automated segmentation with BIANCA matches the manual mask, we can calculate some performance metrics. BIANCA side-script bianca_overlap_measures can calculate some commonly used metrics. Check the usage and the metrics that will be calculated on the help (type bianca_overlap_measures in the terminal) or on the BIANCA documentation page.

bianca_overlap_measures sub-001/bianca_output 0.9 sub-001/FLAIR_Lesion_mask.nii.gz 1

How can you assess the performance on BIANCA output after masking (you can find the mask in prebaked_advanced/T1_bianca_mask_to-FLAIR_bin.nii.gz? Check your answer here.

fslmaths sub-001/bianca_output -mas sub-001/prebaked_advanced/T1_bianca_mask_to-FLAIR_bin.nii.gz sub-001/bianca_output_masked.nii.gz
bianca_overlap_measures sub-001/bianca_output_masked 0.9 sub-001/FLAIR_Lesion_mask.nii.gz 1

More details and guidelines for your analysis can be found in the BIANCA documentation.

SIENA (Optional)

SIENA is a package for both single-time-point ("cross-sectional") and two-time-point ("longitudinal") analysis of brain change, in particular, the estimation of atrophy (volumetric loss of brain tissue).

digraph G { rankdir=LR node [shape=box]; 1 [label="T1 image from time point 1"]; 2 [label="T1 image from time point 2"]; 3 [label="bet"]; 4 [label="Brain images and skull images (for both time points 1 and 2)"]; 5 [label="siena_flirt"]; 6 [label="Registrations of brains to half-way space (from time points 1 and 2)"]; 7 [label="siena_diff"]; 9 [label="Estimated change (between two aligned brains)"]; 1 -> 3; 2 -> 3; 3 -> 4; 4 -> 5; 5 -> 6; 6 -> 7; 7 -> 9; }

cd ~/fsl_course_data/seg_struc/siena
ls

The example data is two time points, 24 months apart, from a subject with probable Alzheimer's disease. The command that was used to create the example analysis is (don't run this - it takes too long!):

siena sub3m0 sub3m24 -d -m -b -30

The -d flag tells the siena script not to clean up the many intermediate images it creates - you would not normally use this. The other options are explained later.

SIENA has already been run for you. Change directory into the SIENA output directory:

cd sub3m0_to_sub3m24_siena
ls

In the SIENA output directory the first timepoint image is named "A" and the second "B", to keep filenames simple and short. To view the output report, open report.html in a web browser. The next few sections take you through the different parts of the webpage report, which correspond to the different stages of the SIENA analysis.

BET brain extraction results

First BET was run on the two input images, with options telling it to create the skull surface image and the binary mask image, as well as the default brain image.

Other BET options can be included in the call to siena by adding -B "betopts" - for example

siena sub3m0 sub3m24 -d -m -b -30 -B "-f 0.3"

the command line tells siena to pass on the -f 0.3 option to BET, which causes the estimated brain to be larger if the value used is less than 0.5, and smaller otherwise.

You also might need to use the -c option to BET if you need to tell BET where to center the initial brain surface, such as when you have a huge amount of neck in the image. For example, if it looks like the centre of the brain is at 112,110,78 (in voxels, e.g. as viewed in FSLeyes), and you want to combine this option with the above -f option, you would add, to the siena command,

siena sub3m0 sub3m24 -d -m -b -30 -B "-f 0.3 -c 112 110 78"

You can see the two brain and skull extractions in the webpage report. If you want to see these in more detail, open the relevant images in FSLeyes, for example:

fsleyes A A_brain -cm red-yellow A_brain_skull -cm green &

Be aware that the skull estimate is usually very noisy but that it is only used to determine the overall scaling and this process is not very sensitive to the noise as long as the majority of points lie on the skull.

FLIRT A-to-B registration results

Now the two time points are registered using the script siena_flirt. This runs the 3-step registration (brains, then skulls, then brains again). The transformation is "halved" so that each image can be transformed into the space halfway between the two. The webpage report shows the alignment of the two brains in this halfway space. You need to check that the two timepoints are fundamentally well-aligned, with only small (e.g. atrophy) changes between them. Look out for mistakes such as: the two images coming from different subjects, one image being left-right flipped relative to the other one, or one image having bad artefacts.

If you want to look at the registration in more detail:

fsleyes A_halfwayto_B_brain B_halfwayto_A_brain &

FLIRT standard space registration results

Now, if standard-space-based masking has been requested (it was in this case, using the -m option in the command above), the two brain images are registered to the standard brain $FSLDIR/data/standard/MNI152_T1_2mm_brain using FLIRT. The transforms (and their inverses) are saved. The two brains are registered separately and their transforms compared to test for consistency.

The webpage report shows the two images transformed into standard space, with the overlaying red lines derived from the edges of the standard space template, for comparison.

Field-of-view and standard space masking

If the -m option was set, a standard space brain mask is now transformed into the native image space and applied to the original brain masks produced by BET. This is in most areas a fairly liberal (dilated) brain mask, except around the eyes.

If the -t or -b options are set then an upper or lower limit (in the Z direction) in standard space is defined, to supplement the masking. This is useful, for example, to restrict the field-of-view of the analysis if you have variable field-of-view at the top or bottom of the head in different subjects.

Here you can see the bottom of the temporal lobes have not been included in the regions fed into the boundary edge movement analysis. You would use such a setting if these regions had not been acquired in one/some of the subjects in your dataset.

The webpage report shows the -m brain masking in blue, the -t/-b masking in red (you can see the effect of the -b -30 option), and the intersection of the two maskings in green. It is this intersection that is what gets finally used.

FAST tissue segmentation

In order to find all brain/non-brain edge points, tissue-type segmentation is now run on both brain-extracted images. The GM and WM voxels are combined into a single mask, and the mask edges (including internal ventricle edges) are used to find edge motion (discussed below). The webpage report shows the two segmentations.

Change Estimation

The final step is to carry out change analysis on the registered masked brain images. At all points which are reported as boundaries between brain and non-brain, the distance that the brain surface has moved between the two time points is estimated. The mean perpendicular surface motion is computed and converted to PBVC (percentage brain volume change).

The webpage report shows the edge motion colour coded at the brain edge points, and then shows the final global PBVC value. To see the edge motion image in more detail:

fsleyes A_halfwayto_B_render -cm render1 &

If you are interested to look at 4 examples of "problem cases" using SIENA, see the advanced practical

SIENAX (Optional)

cd ~/fsl_course_data/seg_struc/siena/sub3m0_sienax

In this section we look at how SIENAX works and look at the most useful outputs. SIENAX estimates total brain tissue volume, from a single image, normalised for skull size.

Open report.html in a web browser. The example data is one time point from a subject with probable Alzheimer's disease. The command that was used to create the example analysis is (don't run this!):

sienax sub3m0 -d -b -30 -r

SIENAX starts by running BET and FLIRT in a manner very similar to SIENA, except that the second time point image is replaced by standard space brain and skull images. Next a standard space brain mask is always used to supplement the BET segmentation.

As before, optional Z limits in standard space can be used to mask further.

digraph G { rankdir=LR node [shape=box]; 1 [label="Subject T1"]; 2 [label="Standard space images (brain extracted, mask and skull)"]; 3 [label="bet"]; 4 [label="Brain extracted image"]; 5 [label="siena_flirt"]; 6 [label="Structural image registered to standard brain (constrained by skull for scaling)"]; 7 [label="Fast"]; 8 [label="Tissue segmentations/volumes (of normalized subject T1)"]; 1 -> 3; 2 -> 3; 3 -> 4; 1 -> 5; 2 -> 5; 4 -> 5; 5 -> 6; 6 -> 7; 7 -> 8; }

Next, FAST is used, with partial volume estimation turned on, to provide an accurate estimate of grey and white matter volumes. In order to provide normalised volumes for GM/WM/total, the volumetric scaling factor derived from the registration to standard space is used to multiply the native volumes; the values are thus normalised for head size.

The End.

Structural Analysis Practical

Contents:

Optional extensions:

Advanced extensions:

FAST

FAST Input Preparation - BET

Look at your data

Image with Bias Field

FAST - Single Channel Example

Bias Field Correction

Partial Volume Segmentation

FIRST

Segmentation of structures

Options used in run_first_all

Check the registration

Boundary corrected segmentation output

FSL-VBM

Preprocessing

BIANCA (Optional)

Before running BIANCA

Look at your data

Master file preparation

Running BIANCA

Look at the output

After running BIANCA

Extension: segment lesions for a new subject with existing BIANCA training file

Advanced extension: training and testing with leave-one-out

SIENA (Optional)

BET brain extraction results

FLIRT A-to-B registration results

FLIRT standard space registration results

Field-of-view and standard space masking

FAST tissue segmentation

Change Estimation

SIENAX (Optional)

Options used in `run_first_all`