.. AUTO-GENERATED FILE -- DO NOT EDIT!

interfaces.slicer.segmentation.specialized
==========================================


.. _nipype.interfaces.slicer.segmentation.specialized.BRAINSROIAuto:


.. index:: BRAINSROIAuto

BRAINSROIAuto
-------------

`Link to code <http://github.com/nipy/nipype/tree/99796c15f2e157774a3f54f878fdd06ad981a80b/nipype/interfaces/slicer/segmentation/specialized.py#L122>`_

Wraps command **/home/raid3/gorgolewski/software/slicer/Slicer --launch BRAINSROIAuto **

title: Foreground masking (BRAINS)

category: Segmentation.Specialized

description: This tool uses a combination of otsu thresholding and a closing operations to identify the most prominant foreground region in an image.


version: 2.4.1

license: https://www.nitrc.org/svn/brains/BuildScripts/trunk/License.txt

contributor: Hans J. Johnson, hans-johnson -at- uiowa.edu, http://wwww.psychiatry.uiowa.edu

acknowledgements: Hans Johnson(1,3,4); Kent Williams(1); Gregory Harris(1), Vincent Magnotta(1,2,3);  Andriy Fedorov(5), fedorov -at- bwh.harvard.edu (Slicer integration); (1=University of Iowa Department of Psychiatry, 2=University of Iowa Department of Radiology, 3=University of Iowa Department of Biomedical Engineering, 4=University of Iowa Department of Electrical and Computer Engineering, 5=Surgical Planning Lab, Harvard)

Inputs::

        [Mandatory]

        [Optional]
        ROIAutoDilateSize: (a float)
                This flag is only relavent when using ROIAUTO mode for initializing masks.  It defines
                the final dilation size to capture a bit of background outside the tissue region.  At
                setting of 10mm has been shown to help regularize a BSpline registration type so that
                there is some background constraints to match the edges of the head better.
        args: (a string)
                Additional parameters to the command
        closingSize: (a float)
                The Closing Size (in millimeters) for largest connected filled mask.  This value is
                divided by image spacing and rounded to the next largest voxel number.
        environ: (a dictionary with keys which are a value of type 'str' and with values which
                 are a value of type 'str', nipype default value: {})
                Environment variables
        ignore_exception: (a boolean, nipype default value: False)
                Print an error message instead of throwing an exception in case the interface fails to
                run
        inputVolume: (an existing file name)
                The input image for finding the largest region filled mask.
        numberOfThreads: (an integer)
                Explicitly specify the maximum number of threads to use.
        otsuPercentileThreshold: (a float)
                Parameter to the Otsu threshold algorithm.
        outputClippedVolumeROI: (a boolean or a file name)
                The inputVolume clipped to the region of the brain mask.
        outputROIMaskVolume: (a boolean or a file name)
                The ROI automatically found from the input image.
        outputVolumePixelType: ('float' or 'short' or 'ushort' or 'int' or 'uint' or 'uchar')
                The output image Pixel Type is the scalar datatype for representation of the Output
                Volume.
        thresholdCorrectionFactor: (a float)
                A factor to scale the Otsu algorithm's result threshold, in case clipping mangles the
                image.

Outputs::

        outputClippedVolumeROI: (an existing file name)
                The inputVolume clipped to the region of the brain mask.
        outputROIMaskVolume: (an existing file name)
                The ROI automatically found from the input image.

.. _nipype.interfaces.slicer.segmentation.specialized.EMSegmentCommandLine:


.. index:: EMSegmentCommandLine

EMSegmentCommandLine
--------------------

`Link to code <http://github.com/nipy/nipype/tree/99796c15f2e157774a3f54f878fdd06ad981a80b/nipype/interfaces/slicer/segmentation/specialized.py#L77>`_

Wraps command **/home/raid3/gorgolewski/software/slicer/Slicer --launch EMSegmentCommandLine **

title:
  EMSegment Command-line


category:
  Segmentation.Specialized


description:
  This module is used to simplify the process of segmenting large collections of images by providing a command line interface to the EMSegment algorithm for script and batch processing.


documentation-url: http://www.slicer.org/slicerWiki/index.php/Documentation/4.0/EMSegment_Command-line

contributor: Sebastien Barre, Brad Davis, Kilian Pohl, Polina Golland, Yumin Yuan, Daniel Haehn

acknowledgements: Many people and organizations have contributed to the funding, design, and development of the EMSegment algorithm and its various implementations.

Inputs::

        [Mandatory]

        [Optional]
        args: (a string)
                Additional parameters to the command
        atlasVolumeFileNames: (an existing file name)
                Use an alternative atlas to the one that is specified by the mrml file - note the order
                matters !
        disableCompression: (a boolean)
                Don't use compression when writing result image to disk.
        disableMultithreading: (an integer)
                Disable multithreading for the EMSegmenter algorithm only! Preprocessing might still run
                in multi-threaded mode. -1: Do not overwrite default value. 0: Disable. 1: Enable.
        dontUpdateIntermediateData: (an integer)
                Disable update of intermediate results.  -1: Do not overwrite default value. 0: Disable.
                1: Enable.
        dontWriteResults: (a boolean)
                Used for testing.  Don't actually write the resulting labelmap to disk.
        environ: (a dictionary with keys which are a value of type 'str' and with values which
                 are a value of type 'str', nipype default value: {})
                Environment variables
        generateEmptyMRMLSceneAndQuit: (a boolean or a file name)
                Used for testing.  Only write a scene with default mrml parameters.
        ignore_exception: (a boolean, nipype default value: False)
                Print an error message instead of throwing an exception in case the interface fails to
                run
        intermediateResultsDirectory: (an existing directory name)
                Directory where EMSegmenter will write intermediate data (e.g., aligned atlas data).
        keepTempFiles: (a boolean)
                If flag is set then at the end of command the temporary files are not removed
        loadAtlasNonCentered: (a boolean)
                Read atlas files non-centered.
        loadTargetCentered: (a boolean)
                Read target files centered.
        mrmlSceneFileName: (an existing file name)
                Active MRML scene that contains EMSegment algorithm parameters.
        parametersMRMLNodeName: (a string)
                The name of the EMSegment parameters node within the active MRML scene.  Leave blank for
                default.
        registrationAffineType: (an integer)
                specify the accuracy of the affine registration. -2: Do not overwrite default, -1: Test,
                0: Disable, 1: Fast, 2: Accurate
        registrationDeformableType: (an integer)
                specify the accuracy of the deformable registration. -2: Do not overwrite default, -1:
                Test, 0: Disable, 1: Fast, 2: Accurate
        registrationPackage: (a string)
                specify the registration package for preprocessing (CMTK or BRAINS or PLASTIMATCH or
                DEMONS)
        resultMRMLSceneFileName: (a boolean or a file name)
                Write out the MRML scene after command line substitutions have been made.
        resultStandardVolumeFileName: (an existing file name)
                Used for testing.  Compare segmentation results to this image and return EXIT_FAILURE if
                they do not match.
        resultVolumeFileName: (a boolean or a file name)
                The file name that the segmentation result volume will be written to.
        targetVolumeFileNames: (an existing file name)
                File names of target volumes (to be segmented).  The number of target images must be
                equal to the number of target images specified in the parameter set, and these images
                must be spatially aligned.
        taskPreProcessingSetting: (a string)
                Specifies the different task parameter. Leave blank for default.
        verbose: (a boolean)
                Enable verbose output.

Outputs::

        generateEmptyMRMLSceneAndQuit: (an existing file name)
                Used for testing.  Only write a scene with default mrml parameters.
        resultMRMLSceneFileName: (an existing file name)
                Write out the MRML scene after command line substitutions have been made.
        resultVolumeFileName: (an existing file name)
                The file name that the segmentation result volume will be written to.

.. _nipype.interfaces.slicer.segmentation.specialized.RobustStatisticsSegmenter:


.. index:: RobustStatisticsSegmenter

RobustStatisticsSegmenter
-------------------------

`Link to code <http://github.com/nipy/nipype/tree/99796c15f2e157774a3f54f878fdd06ad981a80b/nipype/interfaces/slicer/segmentation/specialized.py#L24>`_

Wraps command **/home/raid3/gorgolewski/software/slicer/Slicer --launch RobustStatisticsSegmenter **

title: Robust Statistics Segmenter

category: Segmentation.Specialized

description: Active contour segmentation using robust statistic.

version: 1.0

documentation-url: http://wiki.slicer.org/slicerWiki/index.php/Documentation/4.1/Modules/RobustStatisticsSegmenter

contributor: Yi Gao (gatech), Allen Tannenbaum (gatech), Ron Kikinis (SPL, BWH)

acknowledgements: This work is part of the National Alliance for Medical Image Computing (NAMIC), funded by the National Institutes of Health

Inputs::

        [Mandatory]

        [Optional]
        args: (a string)
                Additional parameters to the command
        curvatureWeight: (a float)
                Given sphere 1.0 score and extreme rough bounday/surface 0 score, what is the expected
                smoothness of the object?
        environ: (a dictionary with keys which are a value of type 'str' and with values which
                 are a value of type 'str', nipype default value: {})
                Environment variables
        expectedVolume: (a float)
                The approximate volume of the object, in mL.
        ignore_exception: (a boolean, nipype default value: False)
                Print an error message instead of throwing an exception in case the interface fails to
                run
        intensityHomogeneity: (a float)
                What is the homogeneity of intensity within the object? Given constant intensity at 1.0
                score and extreme fluctuating intensity at 0.
        labelImageFileName: (an existing file name)
                Label image for initialization
        labelValue: (an integer)
                Label value of the output image
        maxRunningTime: (a float)
                The program will stop if this time is reached.
        originalImageFileName: (an existing file name)
                Original image to be segmented
        segmentedImageFileName: (a boolean or a file name)
                Segmented image

Outputs::

        segmentedImageFileName: (an existing file name)
                Segmented image
