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       Atropos - part of ANTS registration suite



              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.

       -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>]

              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)

              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

              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

              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
              <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

       -m, --mrf [<smoothingFactor=0.3>,<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

              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

       -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.


              Print the help menu (short version).


              Print the help menu.  <VALUES>: 1