bionic (1) antsJointFusion.1.gz

NAME

       antsJointFusion - part of ANTS registration suite

DESCRIPTION

   COMMAND:
              antsJointFusion

              antsJointFusion  is  an image fusion algorithm developed by Hongzhi Wang and Paul Yushkevich which
              won segmentation challenges at MICCAI 2012 and MICCAI 2013. The original  label  fusion  framework
              was  extended  to  accommodate intensities by Brian Avants. This implementation is based on Paul's
              original ITK-style implementation and Brian's ANTsR implementation. References include 1) H. Wang,
              J.  W. Suh, S.  Das, J. Pluta, C. Craige, P. Yushkevich, Multi-atlas segmentation with joint label
              fusion IEEE Trans. on Pattern Analysis and Machine Intelligence, 35(3), 611-623, 2013. and  2)  H.
              Wang  and  P.  A.  Yushkevich,  Multi-atlas  segmentation  with  joint label fusion and corrective
              learning--an open source implementation, Front. Neuroinform., 2013.

   OPTIONS:
       -d, --image-dimensionality 2/3/4

              This option forces the image to be treated as a specified-dimensional image. If not specified, the
              program tries to infer the dimensionality from the input image.

       -t, --target-image targetImage
              [targetImageModality0,targetImageModality1,...,targetImageModalityN]

              The target image (or multimodal target images) assumed to be aligned to a common image domain.

       -g, --atlas-image atlasImage
              [atlasImageModality0,atlasImageModality1,...,atlasImageModalityN]

              The atlas image (or multimodal atlas images) assumed to be aligned to a common image domain.

       -l, --atlas-segmentation atlasSegmentation

              The  atlas  segmentation images. For performing label fusion the number of specified segmentations
              should be identical to the number of atlas image sets.

       -a, --alpha 0.1

              Regularization term added to matrix Mx for calculating the inverse. Default = 0.1

       -b, --beta 2.0

              Exponent for mapping intensity difference to the joint error. Default = 2.0

       -c, --constrain-nonnegative (0)/1

              Constrain solution to non-negative weights.

       -p, --patch-radius 2
              2x2x2

              Patch radius for similarity measures. Default = 2x2x2

       -m, --patch-metric (PC)/MSQ

              Metric to be used in determining the most similar neighborhood patch.  Options  include  Pearson's
              correlation (PC) and mean squares (MSQ). Default = PC (Pearson correlation).

       -s, --search-radius 3
              3x3x3 searchRadiusMap.nii.gz

              Search  radius  for  similarity measures. Default = 3x3x3. One can also specify an image where the
              value at the voxel specifies the isotropic search radius at that voxel.

       -e, --exclusion-image label[exclusionImage]

              Specify an exclusion region for the given label.

       -x, --mask-image maskImageFilename

              If a mask image is specified, fusion is only performed in the mask region.

       -o, --output labelFusionImage

              intensityFusionImageFileNameFormat
              [labelFusionImage,intensityFusionImageFileNameFormat,<labelPosteriorProbabilityImageFileNameFormat>,<atlasVotingWeightImageFileNameFormat>]

              The output is the intensity and/or label fusion image. Additional  optional  outputs  include  the
              label posterior probability images and the atlas voting weight images.

       --version

              Get version information.

       -v, --verbose (0)/1

              Verbose output.

       -h

              Print the help menu (short version).

       --help

              Print the help menu.  <VALUES>: 1