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Atropos --help

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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.

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 -t 1x2 -t 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>]
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=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.

-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 ).

-h
Print the help menu (short version).
<VALUES>: 0

--help
Print the help menu.
<VALUES>: 1, 0

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