Provided by: ants_2.4.3+dfsg-1_amd64 
NAME
Atropos - part of ANTS registration suite
DESCRIPTION
COMMAND:
Atropos
A finite mixture modeling (FMM) segmentation approach with possibilities for
specifying prior constraints. These prior constraints include the specification of
a prior label image, prior probability images (one for each class), and/or an MRF
prior to enforce spatial smoothing of the labels. All segmentation images including
priors and masks must be in the same voxel and physical space. Similar algorithms
include FAST and SPM. Reference: Avants BB, Tustison NJ, Wu J, Cook PA, Gee JC. An
open source multivariate framework for n-tissue segmentation with evaluation on
public data. Neuroinformatics. 2011 Dec;9(4):381-400.
OPTIONS:
-d, --image-dimensionality 2/3/4
This option forces the image to be treated as a specified-dimensional image. If not
specified, Atropos tries to infer the dimensionality from the first input image.
-a, --intensity-image [intensityImage,<adaptiveSmoothingWeight>]
One or more scalar images is specified for segmentation using the
-a/--intensity-image option. For segmentation scenarios with no prior information,
the first scalar image encountered on the command line is used to order labelings
such that the class with the smallest intensity signature is class '1' through
class 'N' which represents the voxels with the largest intensity values. The
optional adaptive smoothing weight parameter is applicable only when using prior
label or probability images. This scalar parameter is to be specified between [0,1]
which smooths each labeled region separately and modulates the intensity
measurement at each voxel in each intensity image between the original intensity
and its smoothed counterpart. The smoothness parameters are governed by the
-b/--bspline option.
-b, --bspline [<numberOfLevels=6>,<initialMeshResolution=1x1x...>,<splineOrder=3>]
If the adaptive smoothing weights are > 0, the intensity images are smoothed in
calculating the likelihood values. This is to account for subtle intensity
differences across the same tissue regions.
-i, --initialization Random[numberOfClasses]
Otsu[numberOfTissueClasses] KMeans[numberOfTissueClasses,<clusterCenters(in
ascending order and for first intensity image only)>]
PriorProbabilityImages[numberOfTissueClasses,fileSeriesFormat(index=1 to
numberOfClasses) or vectorImage,priorWeighting,<priorProbabilityThreshold>]
PriorLabelImage[numberOfTissueClasses,labelImage,priorWeighting]
To initialize the FMM parameters, one of the following options must be specified.
If one does not have prior label or probability images we recommend using kmeans as
it is typically faster than otsu and can be used with multivariate initialization.
However, since a Euclidean distance on the inter cluster distances is used, one
might have to appropriately scale the additional input images. Random
initialization is meant purely for intellectual curiosity. The prior weighting
(specified in the range [0,1]) is used to modulate the calculation of the posterior
probabilities between the likelihood*mrfprior and the likelihood*mrfprior*prior.
For specifying many prior probability images for a multi-label segmentation, we
offer a minimize usage option (see -m). With that option one can specify a prior
probability threshold in which only those pixels exceeding that threshold are
stored in memory.
-s, --partial-volume-label-set label1xlabel2xlabel3
The partial volume estimation option allows one to modelmixtures of classes within
single voxels. Atropos currently allows the user to model two class mixtures per
partial volume class. The user specifies a set of class labels per partial volume
class requested. For example, suppose the user was performing a classic 3-tissue
segmentation (csf, gm, wm) using kmeans initialization. Suppose the user also
wanted to model the partial voluming effects between csf/gm and gm/wm. The user
would specify it using -i kmeans[3] and -s 1x2 -s 2x3. So, for this example, there
would be 3 tissue classes and 2 partial volume classes. Optionally,the user can
limit partial volume handling to mrf considerations only whereby the output would
only be the three tissues.
--use-partial-volume-likelihoods 1/(0)
true/(false)
The user can specify whether or not to use the partial volume likelihoods, in which
case the partial volume class is considered separate from the tissue classes.
Alternatively, one can use the MRF only to handle partial volume in which case,
partial volume voxels are not considered as separate classes.
-p, --posterior-formulation
Socrates[<useMixtureModelProportions=1>,<initialAnnealingTemperature=1>,<annealingRate=1>,<minimumTemperature=0.1>]
Plato[<useMixtureModelProportions=1>,<initialAnnealingTemperature=1>,<annealingRate=1>,<minimumTemperature=0.1>]
Aristotle[<useMixtureModelProportions=1>,<initialAnnealingTemperature=1>,<annealingRate=1>,<minimumTemperature=0.1>]
Sigmoid[<useMixtureModelProportions=1>,<initialAnnealingTemperature=1>,<annealingRate=1>,<minimumTemperature=0.1>]]
Different posterior probability formulations are possible as are different update
options. To guarantee theoretical convergence properties, a proper formulation of
the well-known iterated conditional modes (ICM) uses an asynchronous update step
modulated by a specified annealing temperature. If one sets the
AnnealingTemperature > 1 in the posterior formulation a traditional code set for a
proper ICM update will be created. Otherwise, a synchronous update step will take
place at each iteration. The annealing temperature, T, converts the
posteriorProbability to posteriorProbability^(1/T) over the course of optimization.
-x, --mask-image maskImageFilename
The image mask (which is required) defines the region which is to be labeled by the
Atropos algorithm.
-c, --convergence numberOfIterations
[<numberOfIterations=5>,<convergenceThreshold=0.001>]
Convergence is determined by calculating the mean maximum posterior probability
over the region of interest at each iteration. When this value decreases or
increases less than the specified threshold from the previous iteration or the
maximum number of iterations is exceeded the program terminates.
-k, --likelihood-model Gaussian
HistogramParzenWindows[<sigma=1.0>,<numberOfBins=32>]
ManifoldParzenWindows[<pointSetSigma=1.0>,<evaluationKNeighborhood=50>,<CovarianceKNeighborhood=0>,<kernelSigma=0>]
JointShapeAndOrientationProbability[<shapeSigma=1.0>,<numberOfShapeBins=64>,
<orientationSigma=1.0>, <numberOfOrientationBins=32>] LogEuclideanGaussian
Both parametric and non-parametric options exist in Atropos. The Gaussian
parametric option is commonly used (e.g. SPM & FAST) where the mean and standard
deviation for the Gaussian of each class is calculated at each iteration. Other
groups use non-parametric approaches exemplified by option 2. We recommend using
options 1 or 2 as they are fairly standard and the default parameters work
adequately.
-m, --mrf [<smoothingFactor=0.3>,<radius=1x1x...>]
[<mrfCoefficientImage>,<radius=1x1x...>]
Markov random field (MRF) theory provides a general framework for enforcing
spatially contextual constraints on the segmentation solution. The default
smoothing factor of 0.3 provides a moderate amount of smoothing. Increasing this
number causes more smoothing whereas decreasing the number lessens the smoothing.
The radius parameter specifies the mrf neighborhood. Different update schemes are
possible but only the asynchronous updating has theoretical convergence properties.
-g, --icm [<useAsynchronousUpdate=1>,<maximumNumberOfICMIterations=1>,<icmCodeImage=''>]
Asynchronous updating requires the construction of an ICM code image which is a
label image (with labels in the range {1,..,MaximumICMCode}) constructed such that
no MRF neighborhood has duplicate ICM code labels. Thus, to update the voxel class
labels we iterate through the code labels and, for each code label, we iterate
through the image and update the voxel class label that has the corresponding ICM
code label. One can print out the ICM code image by specifying an ITK-compatible
image filename.
-r, --use-random-seed 0/(1)
Initialize internal random number generator with a random seed. Otherwise,
initialize with a constant seed number.
-o, --output [classifiedImage,<posteriorProbabilityImageFileNameFormat>]
The output consists of a labeled image where each voxel in the masked region is
assigned a label from 1, 2, ..., N. Optionally, one can also output the posterior
probability images specified in the same format as the prior probability images,
e.g. posterior%02d.nii.gz (C-style file name formatting).
-u, --minimize-memory-usage (0)/1
By default, memory usage is not minimized, however, if this is needed, the various
probability and distance images are calculated on the fly instead of being stored
in memory at each iteration. Also, if prior probability images are used, only the
non-negligible pixel values are stored in memory. <VALUES>: 0
-w, --winsorize-outliers
BoxPlot[<lowerPercentile=0.25>,<upperPercentile=0.75>,<whiskerLength=1.5>]
GrubbsRosner[<significanceLevel=0.05>,<winsorizingLevel=0.10>]
To remove the effects of outliers in calculating the weighted mean and weighted
covariance, the user can opt to remove the outliers through the options specified
below.
-e, --use-euclidean-distance (0)/1
Given prior label or probability images, the labels are propagated throughout the
masked region so that every voxel in the mask is labeled. Propagation is done by
using a signed distance transform of the label. Alternatively, propagation of the
labels with the fast marching filter respects the distance along the shape of the
mask (e.g. the sinuous sulci and gyri of the cortex). <VALUES>: 0
-l, --label-propagation whichLabel[lambda=0.0,<boundaryProbability=1.0>]
The propagation of each prior label can be controlled by the lambda and boundary
probability parameters. The latter parameter is the probability (in the range
[0,1]) of the label on the boundary which increases linearly to a maximum value of
1.0 in the interior of the labeled region. The former parameter dictates the
exponential decay of probability propagation outside the labeled region from the
boundary probability, i.e. boundaryProbability*exp( -lambda * distance ).
-v, --verbose (0)/1
Verbose output.
-h
Print the help menu (short version).
--help
Print the help menu. <VALUES>: 1