Atropos --help

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.

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