#acl LcnGroup:read,write,delete,revert All:read

''See also: [[eTIV]]''

= sbTIV - segmentation-based (estimated) Total Intracranial Volume =

'''''Contributors:''''' ''Nick Schmansky (NS), Christian Larsen (CL), Bruce Fischl (BF), Doug Greve (DG), Andrew Hoopes (AH), Bram Diamond (BD), Oula Puonti (OP), and Koen Van Leemput (KVL)''

Total intracranial volume (TIV/ICV) is an important covariate for volumetric analyses of the brain and brain regions. It is commonly used to correct for head size variation (i.e., 'normalize' hippocampal volume size). The gold-standard method is manual delineation of T2 scans. Freesurfer currently provides the eTIV measure, described [[eTIV| here]]. It has been shown to be a robust covariate:

 * [[attachment:buckner2004.pdf|A unified approach for morphometric and functional data analysis in young, old, and demented adults using automated atlas-based head size normalization: reliability and validation against manual measurement of total intracranial volume]] Buckner et al. (2004) NeuroImage 23:724-738.

However, etiv is not obtained via direct segmentation of all structures within the skull boundaries. Rather, its estimation depends on the linear transform between the T1 image and a T1w atlas.

There are other estimation methods. Recent examples, where a comparison to freesurfer eTIV is made, are:

 * [[https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6449147/pdf/HBM-40-1507.pdf|Quantitative assessment of field strength, total intracranial volume, sex, and age effects on the goodness of harmonization for volumetric analysis on the ADNI database]] Hum Brain Mapp. 2019 Apr 1; 40(5): 1507–1527
 * [[attachment:TIV-Malone-NeuroImage-Jan2015.pdf|Accurate automatic estimation of total intracranial volume: A nuisance variable with less nuisance]] Malone, et al. (2015) NeuroImage 104:366-372.
 * [[attachment:TIV-Malone-NeuroImage-Jan2015-supl.pdf|Supplementary data]]

Our segmentation-based estimate of total intracranial volume, sbTIV, relies on structure segmentations (which includes CSF and other intra-cranial non-brain structures) obtained by the [[Samseg]] processing stream.

== Usage ==

[[Samseg]] computes and outputs the file samseg.stats, which contains the volumes of each labeled structure, e.g.:

{{{

# Measure Soft_Nonbrain_Tissue, 987441.150308, mm^3

}}}
sbTIV takes samseg.stats as an input together with an optional list of structure names that define intracranial volume. Names within the list must correspond to those used within the atlas used by samseg. sbTIV outputs sbtiv.stats, which contains the intra-cranial volume estimate, e.g.:
{{{

# Measure Intra_Cranial_Volume, 160000.123456, mm^3

}}}
Commandline arguments for sbtiv can be found by running sbtiv with the --help parameter.

Additionally...
{{{
mri_segstats --sbTIVin <sbtiv.stats> ...
}}}
When <sbtiv.stats> is provided to mri_segstats, the sbTIV measure is read and added as an additional measure of brain volume to its output summary file.  From its help file:
{{{
  5. sbTotalIntracranialVolume (sbTIV) - estimate of the intracranial volume
  based on the Samseg-based segmentation. See surfer.nmr.mgh.harvard.edu/fswiki/sbTIV
}}}
and as found in aseg.stats:
{{{
# Measure sbTotalIntracranialVolume, sbTIV, samseg-based estimated Total Intracranial Volume, 1667606.252292, mm^3
}}}


== Method ==

For each voxel in the image, Samseg computes the probability that it belongs to each of the labels governed by an underlying brain atlas used during segmentation. The full list of segmented labels include skull, non brain tissue and csf. This is in contrast to the older FreeSurfer .gca -based atlas, which was limited to brain structures which did not fully 'fill' the skull.  This means that an ICV estimate made from a samseg segmentation is in principle immune to shrinking brain structures (where the .gca -based structures do not contain labels allowing immunity to this effect).  This is a critical characteristic of an ICV estimate and is particularly important in measuring structures in an aging population, where ICV does not change with age, but many structures shrink.

Since the samseg processing stream supports specifying a custom atlas for segmentation, sbtiv correspondingly allows you to specify a list of labels to be used for ICV computation. If none is provided, the default list of labels is used and also printed to the console for inspection.
NOTE: Since you can specify your own, custom atlas for samseg to use, you could in principle use one without skull and non brain tissue. However, this makes the segmentation less robust and renders sbtiv unusable.

The ICV computation itself is trivial, and is the sum of intracranial label probabilities over all voxels multiplied by the volume of a voxel. This weighing accounts for voxels that is not entirely certain to be intracranial, e.g., due to partial volume effects. This is more accurate than summing over a hard segmentation. Since samseg already outputs the weighed volume for each label, sbtiv computes the sum of the label volumes within the ICV list. As such, you may chose to omit usage of sbtiv altogether and simply use the samseg output directly for ICV computations.

== Validation ==
The sbTIV measure was validated against these reference sources:

||'''dataset'''||'''description'''||
||SASHA||22 subjects, manual TIV from Buckner/OASIS T2 scans||
||ADNI30||30 subjects, all intracranial voxels labeled manually||

||Buckner40||40 subjects, eTIV from FS v6.0, and SPM12||
||ADNI60||60 subjects, eTIV from FS v6.0, and SPM12||
||IXI79||70 subjects, eTIV from FS v5.3, and SPM12||

[[https://www.jiscmail.ac.uk/cgi-bin/webadmin?A2=spm;a19cf780.1505|This link has notes on generating the ICV measure in SPM12.]]
[[http://www.fil.ion.ucl.ac.uk/~john/misc/VBMclass15.pdf|...and this paper.]]
[[https://github.com/neurodebian/spm12/blob/master/config/spm_run_tissue_volumes.m|...and this matlab file.]]

The ADNI_T2TCV dataset, [[attachment:ADNI_Total_Cranial_Vault_Segmentation_Method_20121108.pdf|33 subjects, verified Total Cranial Vault (TCV) segmentations from T2 scans]],[[attachment:TCV_DICT.csv|TCV_DICT.csv]], [[attachment:TCV.csv|TCV.csv]] was not used because the T1 images could not be located.

The test subjects and scripts are located here: /cluster/fsm/users/samseg/<dataset>.

The SASHA dataset is based on expert manual segmentation of T2 data. ADNI30 data consists of all intracranial voxels labeled in T1 image.
The other datasets compare against the freesurfer eTIV and SPM12 eTIV.

=== Manual segmentations ===

Pearsons correlation coefficient and the absolute % difference in ICV volume between each method and the 'gold truth' manual segmentations were computed, and the data points correspondingly scatter plotted.

 || {{attachment:sasha.png}} || {{attachment:adni30.png}} ||

=== Method agreement ===

As a secondary validation measure, correlations and absolute % difference in measured ICV were computed for a pairwise rotation of freesurfer 6.0, spm 12 and samseg, to explore to what extent the methods 'agree' with each other.
|| Dataset || FS 6 vs spm12 || samseg vs FS || samseg vs spm12 ||
|| ADNI30 || r=0.922, diff=6.588% || r=0.975, diff=2.697% || r=0.961, diff=4.131% ||
|| ADNI60 || r=0.913, diff=7.453% || r=0.968, diff=3.148% || r=0.952, diff=4.666% ||
|| Buckner40 || r=0.922, diff=6.632% || r=0.974, diff=3.467% || r=0.966, diff=3.397% ||
|| IXI79 || r=0.892, diff=9.236% || r=0.914, diff=4.011% || r=0.969, diff=6.234% ||
|| SASHA || r=0.754, diff=6.442% || r=0.949, diff=2.725% || r=0.870, diff=3.898% ||