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_file : a pathlike object or string representing an existing file
Target file. Maps to a command-line argument: -target %s (position: 1).
in_templates : a list of items which are a pathlike object or string representing an existing file
Maps to a command-line argument: %s (position: 3).
num_templates : an integer (int or long)
Number of Templates. Maps to a command-line argument: -templates %s (position: 2).
top_templates : an integer (int or long)
Number of Top Templates. Maps to a command-line argument: -n %s (position: 4).
args : a unicode string
Additional parameters to the command. Maps to a command-line argument: %s.
environ : a 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_file : a 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_seg : a pathlike object or string representing an existing file
Original image to segment (3D Image).
in_file : a 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).
args : a unicode string
Additional parameters to the command. Maps to a command-line argument: %s.
conv : a float
Ratio for convergence (default epsilon = 10^-5). Maps to a command-line argument: -conv %f.
dilation_roi : an integer (int or long)
Dilation of the ROI ( <int> d>=1 ).
environ : a 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_size : a float
Gaussian kernel size in mm to compute the local similarity.
mask_file : a pathlike object or string representing an existing file
Filename of the ROI for label fusion. Maps to a command-line argument: -mask %s.
max_iter : an integer (int or long)
Maximum number of iterations (default = 15). Maps to a command-line argument: -max_iter %d.
mrf_value : a float
MRF prior strength (between 0 and 5). Maps to a command-line argument: -MRF_beta %f.
out_file : a pathlike object or string representing a file
Output consensus segmentation. Maps to a command-line argument: -out %s.
prob_flag : a boolean
Probabilistic/Fuzzy segmented image. Maps to a command-line argument: -outProb.
prob_update_flag : a boolean
Update label proportions at each iteration. Maps to a command-line argument: -prop_update.
proportion : a float
Proportion of the label (only for single labels). Maps to a command-line argument: -prop %s.
set_pq : a 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_file : a pathlike object or string representing an existing file
Registered templates (4D Image).
template_num : an integer (int or long)
Number of labels to use.
unc : a boolean
Only consider non-consensus voxels to calculate statistics. Maps to a command-line argument: -unc.
unc_thresh : a 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_file : a pathlike object or string representing an existing file
Image written after calculations.
LabelFusion.get_staple_args(ranking)
LabelFusion.get_steps_args()