bionic (1) antsJointTensorFusion.1.gz

NAME

       antsJointTensorFusion - part of ANTS registration suite

DESCRIPTION

   COMMAND:
              antsJointTensorFusion

              antsJointTensorFusion  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

       -r, --retain-label-posterior-images (0)/1

              Retain label posterior probability images. Requires atlas segmentations to be specified. Default =
              false

       -f, --retain-atlas-voting-images (0)/1

              Retain atlas voting images. Default = false

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

              Constrain solution to non-negative weights.

       -u, --log-euclidean (0)/1

              Use log Euclidean space for tensor math

       -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

              Search radius for similarity measures. Default = 3x3x3

       -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