interfaces.niftyseg.label_fusion¶
CalcTopNCC¶
Wraps command 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'
Inputs:
[Mandatory]
in_file: (an existing file name)
Target file
flag: -target %s, position: 1
in_templates: (a list of items which are an existing file name)
flag: %s, position: 3
num_templates: (an integer (int or long))
Number of Templates
flag: -templates %s, position: 2
top_templates: (an integer (int or long))
Number of Top Templates
flag: -n %s, position: 4
[Optional]
args: (a unicode string)
Additional parameters to the command
flag: %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', 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
mask_file: (an existing file name)
Filename of the ROI for label fusion
flag: -mask %s
terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
Control terminal output: `stream` - displays to terminal immediately
(default), `allatonce` - waits till command is finished to display
output, `file` - writes output to file, `none` - output is ignored
Outputs:
out_files: (any value)
LabelFusion¶
Wraps command 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
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'
Inputs:
[Mandatory]
classifier_type: ('STEPS' or 'STAPLE' or 'MV' or 'SBA')
Type of Classifier Fusion.
flag: -%s, position: 2
file_to_seg: (an existing file name)
Original image to segment (3D Image)
in_file: (an existing file name)
Filename of the 4D integer label image.
flag: -in %s, position: 1
[Optional]
args: (a unicode string)
Additional parameters to the command
flag: %s
conv: (a float)
Ratio for convergence (default epsilon = 10^-5).
flag: -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', 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
kernel_size: (a float)
Gaussian kernel size in mm to compute the local similarity
mask_file: (an existing file name)
Filename of the ROI for label fusion
flag: -mask %s
max_iter: (an integer (int or long))
Maximum number of iterations (default = 15).
flag: -max_iter %d
mrf_value: (a float)
MRF prior strength (between 0 and 5)
flag: -MRF_beta %f
out_file: (a file name)
Output consensus segmentation
flag: -out %s
prob_flag: (a boolean)
Probabilistic/Fuzzy segmented image
flag: -outProb
prob_update_flag: (a boolean)
Update label proportions at each iteration
flag: -prop_update
proportion: (a float)
Proportion of the label (only for single labels).
flag: -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)
flag: -setPQ %f %f
sm_ranking: ('ALL' or 'GNCC' or 'ROINCC' or 'LNCC', nipype default
value: ALL)
Ranking for STAPLE and MV
flag: -%s, position: 3
template_file: (an existing file name)
Registered templates (4D Image)
template_num: (an integer (int or long))
Number of labels to use
terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
Control terminal output: `stream` - displays to terminal immediately
(default), `allatonce` - waits till command is finished to display
output, `file` - writes output to file, `none` - output is ignored
unc: (a boolean)
Only consider non-consensus voxels to calculate statistics
flag: -unc
unc_thresh: (a float)
If <float> percent of labels agree, then area is not uncertain.
flag: -uncthres %f
verbose: ('0' or '1' or '2')
Verbose level [0 = off, 1 = on, 2 = debug] (default = 0)
flag: -v %s
Outputs:
out_file: (an existing file name)
image written after calculations