Provided by: ants_2.2.0-1ubuntu1_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. 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