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# nipype.interfaces.niftyseg.label_fusion module

The fusion module provides higher-level interfaces to some of the operations that can be performed with the seg_LabFusion command-line program.

## CalcTopNCC

Link to code

> Bases: NiftySegCommand

> Wrapped executable: `seg_CalcTopNCC`.

> Interface for executable seg_CalcTopNCC from NiftySeg platform.

> Examples

>     >>> from nipype.interfaces import niftyseg
>     >>> node = niftyseg.CalcTopNCC()
>     >>> node.inputs.in_file = 'im1.nii'
>     >>> node.inputs.num_templates = 2
>     >>> node.inputs.in_templates = ['im2.nii', 'im3.nii']
>     >>> node.inputs.top_templates = 1
>     >>> node.cmdline
>     'seg_CalcTopNCC -target im1.nii -templates 2 im2.nii im3.nii -n 1'

> in_filea pathlike object or string representing an existing file

>   Target file. Maps to a command-line argument: `-target %s` (position: 1).

> in_templatesa list of items which are a pathlike object or string representing an existing file

>   Maps to a command-line argument: `%s` (position: 3).

> num_templatesan integer (int or long)

>   Number of Templates. Maps to a command-line argument: `-templates %s` (position: 2).

> top_templatesan integer (int or long)

>   Number of Top Templates. Maps to a command-line argument: `-n %s` (position: 4).

> argsa unicode string

>   Additional parameters to the command. Maps to a command-line argument: `%s`.

> environa dictionary with keys which are a bytes or None or a value of class ‘str’ and with values which are a bytes or None or a value of class ‘str’

>   Environment variables. (Nipype default value: `{}`)

> mask_filea pathlike object or string representing an existing file

>   Filename of the ROI for label fusion. Maps to a command-line argument: `-mask %s`.

> out_files : any value

> CalcTopNCC.aggregate_outputs(runtime=None, needed_outputs=None)

>   Collate expected outputs and apply output traits validation.

## LabelFusion

Link to code

> Bases: NiftySegCommand

> Wrapped executable: `seg_LabFusion`.

> Interface for executable seg_LabelFusion from NiftySeg platform using type STEPS as classifier Fusion.

> This executable implements 4 fusion strategies (-STEPS, -STAPLE, -MV or - SBA), all of them using either a global (-GNCC), ROI-based (-ROINCC), local (-LNCC) or no image similarity (-ALL). Combinations of fusion algorithms and similarity metrics give rise to different variants of known algorithms. As an example, using LNCC and MV as options will run a locally weighted voting strategy with LNCC derived weights, while using STAPLE and LNCC is equivalent to running STEPS as per its original formulation. A few other options pertaining the use of an MRF (-MRF beta), the initial sensitivity and specificity estimates and the use of only non-consensus voxels (-unc) for the STAPLE and STEPS algorithm. All processing can be masked (-mask), greatly reducing memory consumption.

> As an example, the command to use STEPS should be: seg_LabFusion -in 4D_Propragated_Labels_to_fuse.nii -out FusedSegmentation.nii -STEPS 2 15 TargetImage.nii 4D_Propagated_Intensities.nii

> Source code | Documentation

> Examples

>     >>> from nipype.interfaces import niftyseg
>     >>> node = niftyseg.LabelFusion()
>     >>> node.inputs.in_file = 'im1.nii'
>     >>> node.inputs.kernel_size = 2.0
>     >>> node.inputs.file_to_seg = 'im2.nii'
>     >>> node.inputs.template_file = 'im3.nii'
>     >>> node.inputs.template_num = 2
>     >>> node.inputs.classifier_type = 'STEPS'
>     >>> node.cmdline
>     'seg_LabFusion -in im1.nii -STEPS 2.000000 2 im2.nii im3.nii -out im1_steps.nii'

> classifier_type‘STEPS’ or ‘STAPLE’ or ‘MV’ or ‘SBA’

>   Type of Classifier Fusion. Maps to a command-line argument: `-%s` (position: 2).

> file_to_sega pathlike object or string representing an existing file

>   Original image to segment (3D Image).

> in_filea pathlike object or string representing an existing file

>   Filename of the 4D integer label image. Maps to a command-line argument: `-in %s` (position: 1).

> argsa unicode string

>   Additional parameters to the command. Maps to a command-line argument: `%s`.

> conva float

>   Ratio for convergence (default epsilon = 10^-5). Maps to a command-line argument: `-conv %f`.

> dilation_roian integer (int or long)

>   Dilation of the ROI ( <int> d>=1 ).

> environa dictionary with keys which are a bytes or None or a value of class ‘str’ and with values which are a bytes or None or a value of class ‘str’

>   Environment variables. (Nipype default value: `{}`)

> kernel_sizea float

>   Gaussian kernel size in mm to compute the local similarity.

> mask_filea pathlike object or string representing an existing file

>   Filename of the ROI for label fusion. Maps to a command-line argument: `-mask %s`.

> max_iteran integer (int or long)

>   Maximum number of iterations (default = 15). Maps to a command-line argument: `-max_iter %d`.

> mrf_valuea float

>   MRF prior strength (between 0 and 5). Maps to a command-line argument: `-MRF_beta %f`.

> out_filea pathlike object or string representing a file

>   Output consensus segmentation. Maps to a command-line argument: `-out %s`.

> prob_flaga boolean

>   Probabilistic/Fuzzy segmented image. Maps to a command-line argument: `-outProb`.

> prob_update_flaga boolean

>   Update label proportions at each iteration. Maps to a command-line argument: `-prop_update`.

> proportiona float

>   Proportion of the label (only for single labels). Maps to a command-line argument: `-prop %s`.

> set_pqa tuple of the form: (a float, a float)

>   Value of P and Q [ 0 < (P,Q) < 1 ] (default = 0.99 0.99). Maps to a command-line argument: `-setPQ %f %f`.

> sm_ranking‘ALL’ or ‘GNCC’ or ‘ROINCC’ or ‘LNCC’

>   Ranking for STAPLE and MV. Maps to a command-line argument: `-%s` (position: 3). (Nipype default value: `ALL`)

> template_filea pathlike object or string representing an existing file

>   Registered templates (4D Image).

> template_numan integer (int or long)

>   Number of labels to use.

> unca boolean

>   Only consider non-consensus voxels to calculate statistics. Maps to a command-line argument: `-unc`.

> unc_thresha float

>   If <float> percent of labels agree, then area is not uncertain. Maps to a command-line argument: `-uncthres %f`.

> verbose‘0’ or ‘1’ or ‘2’

>   Verbose level [0 = off, 1 = on, 2 = debug] (default = 0). Maps to a command-line argument: `-v %s`.

> out_filea pathlike object or string representing an existing file

>   Image written after calculations.

> LabelFusion.get_staple_args(ranking)
> LabelFusion.get_steps_args()