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73 Cards in this Set

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Preprocessing
Before running FAST an image of a head should first be brain-extracted, using BET. The
resulting brain-only image can then be fed into FAST.
Number of input channels
if there is only 1 input image (ie you are not carrying out multi-channel segmentation) then leave this at 1 - otherwise set appropriately.
Image type
Aids the segmentation in identifying which classes are which tissue type. This option is not used for multi-channel segmentation.
Output image(s) basename
Output images will have filenames derived from this basename.
Binary segmentation: All classes in 1 imge will have filename
<basename>_seg.hdr
Number of classes
to be segmented. Normally 3 (GM, WM, CSF) but if there is very poor grey/white contrast you may want to reduce this to 2. Alternatively if there are strong lesions showing up as fourth class you may want to increase this. If you are segmenting T2s you may need to select 4 so dark non-brain matter is processed correctly (this is not a T1 problem as CSF and dark non-brain look similar).
5 Output images:
Partial volume maps, binary segmentation: single image, binary segmentation: one image per class, restored input and bias field.
Partial volume maps
A (non-binary) partial volume image for each class, where each
voxel contains a value in the range 0-1 that represents the proportion of that class's
tissue present in that voxel. This is the default output.
Binary segmentation: single image
This is the "hard" (binary) segmentation, where
each voxel is classified into only one class. A single image contains all the necessary
information, with the first class taking intensity value 1 in the image, etc.
Binary segmentation: one image per class
This is also a hard segmentation output;
the difference is that there is one output image per class, and values are only either 0 or 1.
Restored input:
This is the estimated restored input image after correction for bias
field.
Bias field:
This is the estimated bias field.
Bias field (definition)
spatial intensity variations, or RF inhomogeneities.
Use a-priori probability maps
tells FAST to start by registering the input image to standard
space and then use standard tissue-type probability maps (from the MNI152 dataset) instead of
the initial K-means segmentation, in order to estimate the initial parameters of the classes.
This can help in cases where there is very bad bias field. By default the a-priori probability
maps are only used to initialise the segmentation; however, you can also optionally tell FAST
to use these priors in the final segmentation - this can help, for example, with the
segmentation of deep grey structures.
Use file of initial tissue-type means
tells FAST to use a text file with mean intensity values
(separated by newlines) for the starting mean values of the different classes to be segmented.
This is then used instead of the automated K-means starting parameter estimation.
fast command-line
Type fast to get usage. This is used for both single-channel and multi-channel segmentation
program.
fast syntax
fast [options] file(s)
file(s)
image, or multi-channel set of images, to be segmented.
-S <n>
number of image channels
--channels+<n>
number of image channels
-t <n>
type of image (n=1 for t2, 2 for t2, 3 for PD)
--type=<n>
type of image (t1, t2, PD)
-o <base>
basename for outputs
--out=<base>
basename for outputs
-n <n>
number of tissue-type classes
--class=<n>
number of tissue-type classes
-B
output estimated bias field
-b
output restored image (bias-corrected image)
-H <v>
MRF beta value for main segmentation phase (increasing this
gives spatially smoother segmentations)
--Hyper=<v>
MRF beta value for main segmentation phase (increasing this
gives spatially smoother segmentations)
--Hyper=<v>
MRF beta value for main segmentation phase (increasing this
gives spatially smoother segmentations)
-R <v>
MRF beta value for mixeltype (increasing this gives spatially
smoother mixeltypes and hence PVEs - NB: mixeltype is the
classification of what tissue-types are non-zero in a voxel)
--mixel=<v>
MRF beta value for mixeltype (increasing this gives spatially
smoother mixeltypes and hence PVEs - NB: mixeltype is the
classification of what tissue-types are non-zero in a voxel)
-a <standard2input.mat>
use prior probability maps for initialisation (must specify FLIRT
transform to standard space)
-P <file>
use prior probabilitiy maps at all segmentation stages
--Prior
use prior probabilitiy maps at all segmentation stages
-s <file>
file specifying initial tissue -type means
--manualseg=<file>
file specifying initial tissue-type means
FAST can estimate the tissue volume for a given class. For the most accurate quantification...
...we recommend using the partial volume estimates.
How can the actual volume of tissue be calculated?
From the corresponding partial volume map by summing up all the values. This can be done using fslstats and then multiplying the mean value by the volume (in voxels)
For example, for an image called structural_bet that fast was run on, the tissue volume of tissue class 1 can be found by running the following:
vol=`$FSLDIR/bin/fslstats structural_bet_pve_1 -V | awk '{print $1}'`

mean=`$FSLDIR/bin/fslstats structural_bet_pve_1 -M`

tissuevol=`echo "$mean * $vol" | bc -l`

echo $tissuevol

This prints the total tissue volume in voxels (which is also stored in the variable tissuevol). Note that to get the volume in mm3 just replace the {print $1} with {print $2} in the first line above.
Type of image (eg T1, T2, PD) - note spaces in fast
fast3: -t<n>
fast4: -t <n> (note space: t 2 not t2 as in fast3)
or --type=<n>
Number of tissue-type classes to estimate
fast3: -c <n>
fast4: -n <n> or --class=<n>
Basename for all output files
fast3: -od <base>
fast4: -o <base> or --out=<base>
Output separate image for each (hard) segmentation class
fast3: -os
fast4: -g or --segments
output 1 probability map (of hard seg) per class
fast3: -op
fast4: -p
output bias corrected (restored) image
fast3: -or
fast4: -B
Output estimated bias field
fast3: no equivalent
fast4: -b
Output bias field correction image (reciprocal of the estimated bias field)
fast3: -ob
fast4: no equivalent
Suppress segmentation outputs
fast3: -n
fast4: no equivalent
Input file with manual starting values for class intensities
fast3: -m <file>
fast4: -s <file> or --manualseg=<file>
2D segmentation mode (for 3D images)
fast3: -2
fast4: no equivalent
number of main-loop iterations
fast3: -i <n>
fast4: -O <n> or --fixed=<n>
Bias field smoothing : n is iterations, m is FWHM in mm - rough guide is m = 2n^0.5
fast3: -l <n>
fast4: -l <m> or --lowpass=<m>
disable automatic parameter updating
fast3: -p
fast4: no equivalent
Main MRF beta value (initial segmentation calculation)
fast3: -b <v>
fast4: -H <v> or --Hyper=<v>
Output dilated bias correction field (extra n iterations of smoothing)
fast3: -oba <n>
fast4: no equivalent
Enable partial volume classification
fast3: -e
fast4: (default)
Do not do partial volume classification
fast3: (default)
fast4: --nopve
Output partial volume (PVE) images (one per class)
fast3: -ov
fast4: (default)
MRF beta value for PVE classification (fast3) or mixeltype in in PVE classification (fast4) - these are related but not exactly equivalent
fast3: --b <v>
fast4: -R <v> or --mixel=<v>
Disable parameter updating during PVE classification
fast3: --p
fast4: no equivalent
Turn on verbose/diagnostic output (fast3 has 6 levels, fast4 is binary)
fast3: -v<n>
fast4: -v or --verbose
Use prior probability maps for initialisation (fast4 requires a FLIRT matrix whereas fast3 requires extra argument -ap)
fast3: -a
fast4: -a <standard2input.mat>
Path and filename prefix for where to find pre-registered prior probability mpas (fast4 requires transformation matrix with -a instead)
fast3: -ap <prefix>
fast4: no equivalent
Use prior probability maps throughout calculations (must be used with -Ap for fast3 or -a for fast4)
fast3: -A
fast4: -P or --Prior
Path and filename prefix for where to find pre-registered prior probability maps
fast3: -Ap <prefix>
fast4: no equivalent
Path/filenames for alternative prior images
fast3: nearly equivalent to -ap or -Ap
fast4: -A <prior1> <prior2> <prior3>
Loop iterations during initial bias-field removal phase
fast3: no equivalent
fast4: -I <n> or --iter=<n>
MRF beta value during initial bias-field removal phase
fast3: no equivalent
fast4: -f <v> or --fHard=<v>
Number of segmentation-initialization iterations
fast3: no equivalent
fast4: -W <n> or --init=<n>
Do not remove bias field
fast3: no equivalent
fast4: -N or --nobias
Number of input channels (images)
Mfast: -s <n>
Fast4: -S <n> or --channels=<n>