def plot_volumes(volume_files):
"""
Use fslview to visualize image volume data.
Inputs
------
volume_files : list of strings
names of image volume files
Examples
--------
>>> import os
>>> from mindboggle.utils.plots import plot_volumes
>>> path = os.environ['MINDBOGGLE_DATA']
>>> volume_file1 = os.path.join(path, 'arno', 'mri', 't1weighted.nii.gz')
>>> volume_file2 = os.path.join(path, 'arno', 'mri', 't1weighted_brain.nii.gz')
>>> volume_files = [volume_file1, volume_file2]
>>> plot_volumes(volume_files)
"""
from mindboggle.utils.utils import execute
if isinstance(volume_files, str):
volume_files = [volume_files]
elif not isinstance(volume_files, list):
import sys
sys.error('plot_volumes() requires volume_files to be a list or string.')
cmd = ["fslview"]
cmd.extend(volume_files)
cmd.extend('&')
execute(cmd, 'os')
def ComposeMultiTransform(transform_files, inverse_Booleans,
output_transform_file='', ext='.txt'):
"""
Run ANTs ComposeMultiTransform function to create a single transform.
Parameters
----------
transform_files : list of strings
transform file names
inverse_Booleans : list of Booleans
Booleans to indicate which transforms to take the inverse of
output_transform_file : string
transform file name
ext : string
'.txt' to save transform file as text, '.mat' for data file
Returns
-------
output_transform_file : string
single composed transform file name
Examples
--------
>>> from mindboggle.utils.ants import ComposeMultiTransform
>>> transform_files = ['affine1.mat', 'affine2.mat']
>>> transform_files = ['/data/Brains/Mindboggle101/antsCorticalThickness/OASIS-TRT-20_volumes/OASIS-TRT-20-1/antsTemplateToSubject0GenericAffine.mat','/data/Brains/Mindboggle101/antsCorticalThickness/OASIS-TRT-20_volumes/OASIS-TRT-20-1/antsTemplateToSubject0GenericAffine.mat']
>>> inverse_Booleans = [False, False]
>>> output_transform_file = ''
>>> ext = '.txt'
>>> ComposeMultiTransform(transform_files, inverse_Booleans, output_transform_file, ext)
"""
import os
from mindboggle.utils.utils import execute
if not output_transform_file:
output_transform_file = os.path.join(os.getcwd(), 'affine' + ext)
xfms = []
for ixfm, xfm in enumerate(transform_files):
if inverse_Booleans[ixfm]:
xfms.append('-i')
xfms.append(xfm)
cmd = ['ComposeMultiTransform 3', output_transform_file, ' '.join(xfms)]
print(cmd)
execute(cmd, 'os')
#if not os.path.exists(output_transform_file):
# raise(IOError(output_transform_file + " not found"))
return output_transform_file
def ThresholdImage(volume, output_file='', threshlo=1, threshhi=10000):
"""
Use the ThresholdImage function in ANTS to threshold image volume::
Usage: ThresholdImage ImageDimension ImageIn.ext outImage.ext
threshlo threshhi <insideValue> <outsideValue>
Parameters
----------
volume : string
nibabel-readable image volume
output_file : string
nibabel-readable image volume
threshlo : integer
lower threshold
threshhi : integer
upper threshold
Returns
-------
output_file : string
name of output nibabel-readable image volume
Examples
--------
>>> import os
>>> from mindboggle.utils.ants import ThresholdImage
>>> from mindboggle.utils.plots import plot_volumes
>>> path = os.path.join(os.environ['MINDBOGGLE_DATA'])
>>> volume = os.path.join(path, 'arno', 'mri', 't1weighted.nii.gz')
>>> output_file = ''
>>> threshlo = 500
>>> threshhi = 10000
>>> output_file = ThresholdImage(volume, output_file, threshlo, threshhi)
>>> # View
>>> plot_volumes(output_file)
"""
import os
from mindboggle.utils.utils import execute
if not output_file:
output_file = os.path.join(os.getcwd(),
'threshold_' + os.path.basename(volume))
cmd = 'ThresholdImage 3 {0} {1} {2} {3}'.format(volume, output_file,
threshlo, threshhi)
execute(cmd, 'os')
if not os.path.exists(output_file):
raise(IOError(output_file + " not found"))
return output_file
def ThresholdImage(volume, output_file='', threshlo=1, threshhi=10000):
"""
Use the ThresholdImage function in ANTs to threshold image volume::
Usage: ThresholdImage ImageDimension ImageIn.ext outImage.ext
threshlo threshhi <insideValue> <outsideValue>
Parameters
----------
volume : string
nibabel-readable image volume
output_file : string
nibabel-readable image volume
threshlo : integer
lower threshold
threshhi : integer
upper threshold
Returns
-------
output_file : string
name of output nibabel-readable image volume
Examples
--------
>>> import os
>>> from mindboggle.utils.ants import ThresholdImage
>>> from mindboggle.utils.plots import plot_volumes
>>> path = os.path.join(os.environ['MINDBOGGLE_DATA'])
>>> volume = os.path.join(path, 'arno', 'mri', 't1weighted.nii.gz')
>>> output_file = ''
>>> threshlo = 500
>>> threshhi = 10000
>>> output_file = ThresholdImage(volume, output_file, threshlo, threshhi)
>>> # View
>>> plot_volumes(output_file)
"""
import os
from mindboggle.utils.utils import execute
if not output_file:
output_file = os.path.join(os.getcwd(),
'threshold_' + os.path.basename(volume))
cmd = 'ThresholdImage 3 {0} {1} {2} {3}'.format(volume, output_file,
threshlo, threshhi)
execute(cmd, 'os')
if not os.path.exists(output_file):
raise (IOError("ThresholdImage did not create " + output_file + "."))
return output_file
def plot_vtk(vtk_file, mask_file='', masked_output=''):
"""
Use mayavi2 to visualize VTK surface mesh data.
Inputs
------
vtk_file : string
name of VTK surface mesh file
Examples
--------
>>> import os
>>> from mindboggle.utils.plots import plot_vtk
>>> path = os.environ['MINDBOGGLE_DATA']
>>> vtk_file = os.path.join(path, 'arno', 'shapes', 'lh.pial.mean_curvature.vtk')
>>> mask_file = os.path.join(path, 'arno', 'features', 'folds.vtk')
>>> masked_output = ''
>>> plot_vtk(vtk_file, mask_file, masked_output)
"""
from mindboggle.utils.utils import execute
# Filter mesh with the non -1 values from a second (same-size) mesh:
if mask_file:
from mindboggle.utils.io_vtk import read_scalars, rewrite_scalars
scalars, name = read_scalars(vtk_file)
mask, name = read_scalars(mask_file)
if not masked_output:
masked_output = 'temp.vtk'
rewrite_scalars(vtk_file, masked_output, scalars, 'masked', mask)
cmd = ["mayavi2", "-d", masked_output, "-m", "Surface"]
else:
cmd = ["mayavi2", "-d", vtk_file, "-m", "Surface"]
cmd.extend('&')
execute(cmd, 'os')
def plot_volumes(volume_files, command='fslview'):
"""
Use fslview to visualize image volume data.
Parameters
----------
volume_files : list of strings
names of image volume files
command : string
plotting software command
Examples
--------
>>> import os
>>> from mindboggle.utils.plots import plot_volumes
>>> path = os.environ['MINDBOGGLE_DATA']
>>> volume_file1 = os.path.join(path, 'Twins-2-1', 'mri', 't1weighted.nii.gz')
>>> volume_file2 = os.path.join(path, 'Twins-2-1', 'mri', 't1weighted_brain.nii.gz')
>>> volume_files = [volume_file1, volume_file2]
>>> command = 'fslview'
>>> command = '/Applications/ITK-SNAP.app/Contents/MacOS/InsightSNAP'
>>> plot_volumes(volume_files, command=command)
"""
from mindboggle.utils.utils import execute
if isinstance(volume_files, str):
volume_files = [volume_files]
elif not isinstance(volume_files, list):
raise(IOError('plot_volumes() requires volume_files to be a list or string.'))
if not isinstance(command, str):
raise(IOError('plot_volumes() requires command to be a string.'))
else:
command = [command]
command.extend(volume_files)
command.extend('&')
execute(command, 'os')
def plot_mask_surface(vtk_file, mask_file='', nonmask_value=-1,
masked_output='', remove_nonmask=False,
program='vtkviewer',
use_colormap=False, colormap_file=''):
"""
Use vtkviewer or mayavi2 to visualize VTK surface mesh data.
If a mask_file is provided, a temporary masked file is saved,
and it is this file that is viewed.
If using vtkviewer, can optionally provide colormap file
or set $COLORMAP environment variable.
Parameters
----------
vtk_file : string
name of VTK surface mesh file
mask_file : string
name of VTK surface mesh file to mask vtk_file vertices
nonmask_value : integer
nonmask (usually background) value
masked_output : string
temporary masked output file name
remove_nonmask : Boolean
remove vertices that are not in mask? (otherwise assign nonmask_value)
program : string {'vtkviewer', 'mayavi2'}
program to visualize VTK file
use_colormap : Boolean
use Paraview-style XML colormap file set by $COLORMAP env variable?
colormap_file : string
use colormap in given file if use_colormap==True? if empty and
use_colormap==True, use file set by $COLORMAP environment variable
Examples
--------
>>> import os
>>> from mindboggle.utils.plots import plot_mask_surface
>>> path = os.environ['MINDBOGGLE_DATA']
>>> vtk_file = os.path.join(path, 'arno', 'labels', 'lh.labels.DKT31.manual.vtk')
>>> mask_file = os.path.join(path, 'test_one_label.vtk')
>>> nonmask_value = 0 #-1
>>> masked_output = ''
>>> remove_nonmask = True
>>> program = 'vtkviewer'
>>> use_colormap = True
>>> colormap_file = '' #'/software/mindboggle_tools/colormap.xml'
>>> plot_mask_surface(vtk_file, mask_file, nonmask_value, masked_output, remove_nonmask, program, use_colormap, colormap_file)
"""
import os
import numpy as np
from mindboggle.utils.mesh import remove_faces, reindex_faces_points
from mindboggle.utils.utils import execute
from mindboggle.utils.plots import plot_surfaces
from mindboggle.utils.io_vtk import read_scalars, rewrite_scalars, \
read_vtk, write_vtk
#-------------------------------------------------------------------------
# Filter mesh with non-background values from a second (same-size) mesh:
#-------------------------------------------------------------------------
if mask_file:
mask, name = read_scalars(mask_file, True, True)
if not masked_output:
masked_output = os.path.join(os.getcwd(), 'temp.vtk')
file_to_plot = masked_output
#---------------------------------------------------------------------
# Remove nonmask-valued vertices:
#---------------------------------------------------------------------
if remove_nonmask:
#-----------------------------------------------------------------
# Load VTK files:
#-----------------------------------------------------------------
faces, lines, indices, points, npoints, scalars, scalar_names, \
o1 = read_vtk(vtk_file, True, True)
#-----------------------------------------------------------------
# Find mask indices, remove nonmask faces, and reindex:
#-----------------------------------------------------------------
Imask = [i for i,x in enumerate(mask) if x != nonmask_value]
mask_faces = remove_faces(faces, Imask)
mask_faces, points, \
original_indices = reindex_faces_points(mask_faces, points)
#-----------------------------------------------------------------
# Write VTK file with scalar values:
#-----------------------------------------------------------------
if np.ndim(scalars) == 1:
scalar_type = type(scalars[0]).__name__
elif np.ndim(scalars) == 2:
scalar_type = type(scalars[0][0]).__name__
else:
print("Undefined scalar type!")
write_vtk(file_to_plot, points, [], [], mask_faces,
scalars[original_indices].tolist(), scalar_names,
scalar_type=scalar_type)
else:
scalars, name = read_scalars(vtk_file, True, True)
scalars[mask == nonmask_value] = nonmask_value
rewrite_scalars(vtk_file, file_to_plot, scalars)
else:
file_to_plot = vtk_file
#-------------------------------------------------------------------------
# Display with vtkviewer.py:
#-------------------------------------------------------------------------
if program == 'vtkviewer':
plot_surfaces(file_to_plot, use_colormap=use_colormap,
colormap_file=colormap_file)
#-------------------------------------------------------------------------
# Display with mayavi2:
#-------------------------------------------------------------------------
elif program == 'mayavi2':
cmd = ["mayavi2", "-d", file_to_plot, "-m", "Surface", "&"]
execute(cmd, 'os')
def thickinthehead(segmented_file, labeled_file, cortex_value=2,
noncortex_value=3, labels=[], names=[], resize=True,
propagate=True, output_dir='', save_table=False):
"""
Compute a simple thickness measure for each labeled cortex region.
Note::
- Cortex, noncortex, and labeled files are the same coregistered brain.
- Calls ANTs functions: ImageMath, Threshold, ResampleImageBySpacing
Example preprocessing steps ::
1. Run Freesurfer and antsCorticalThickness.sh on T1-weighted image.
2. Convert FreeSurfer volume labels (e.g., wmparc.mgz or aparc+aseg.mgz)
to cortex (2) and noncortex (3) segments using relabel_volume()
function [refer to LABELS.py or FreeSurferColorLUT labels file].
3. Convert ANTs Atropos-segmented volume (tmpBrainSegmentation.nii.gz)
to cortex and noncortex segments, by converting 1-labels to 0 and
4-labels to 3 with the relabel_volume() function
(the latter is to include deep-gray matter with noncortical tissues).
4. Combine FreeSurfer and ANTs segmentation volumes to obtain a single
cortex (2) and noncortex (3) segmentation file using the function
combine_2labels_in_2volumes(). This function takes the union of
cortex voxels from the segmentations, the union of the noncortex
voxels from the segmentations, and overwrites intersecting cortex
and noncortex voxels with noncortex (3) labels.
ANTs tends to include more cortical gray matter at the periphery of
the brain than Freesurfer, and FreeSurfer tends to include more white
matter that extends deep into gyral folds than ANTs, so the above
attempts to remedy their differences by overlaying ANTs cortical gray
with FreeSurfer white matter.
5. Optional, see Step 2 below:
Fill segmented cortex with cortex labels and noncortex with
noncortex labels using the PropagateLabelsThroughMask() function
(which calls ImageMath ... PropagateLabelsThroughMask in ANTs).
The labels can be initialized using FreeSurfer (e.g. wmparc.mgz)
or ANTs (by applying the nonlinear inverse transform generated by
antsCorticalThickness.sh to labels in the Atropos template space).
[Note: Any further labeling steps may be applied, such as
overwriting cerebrum with intersecting cerebellum labels.]
Steps ::
1. Extract noncortex and cortex.
2. Either mask labels with cortex or fill cortex with labels.
3. Resample cortex and noncortex files from 1x1x1 to 0.5x0.5x0.5
to better represent the contours of the boundaries of the cortex.
4. Extract outer and inner boundary voxels of the cortex,
by eroding 1 (resampled) voxel for cortex voxels (2) bordering
the outside of the brain (0) and bordering noncortex (3).
5. Estimate middle cortical surface area by the average volume
of the outer and inner boundary voxels of the cortex.
6. Compute the volume of a labeled region of cortex.
7. Estimate the thickness of the labeled cortical region as the
volume of the labeled region (#6) divided by the surface area (#5).
Parameters
----------
segmented_file : string
image volume with cortex and noncortex (and any other) labels
labeled_file : string
corresponding image volume with index labels
cortex_value : integer
cortex label value in segmented_file
noncortex_value : integer
noncortex label value in segmented_file
labels : list of integers
label indices
names : list of strings
label names
resize : Boolean
resize (2x) segmented_file for more accurate thickness estimates?
propagate : Boolean
propagate labels through cortex?
output_dir : string
output directory
save_table : Boolean
save output table file with labels and thickness values?
Returns
-------
label_volume_thickness : list of lists of integers and floats
label indices, volumes, and thickness values (default -1)
output_table : string
name of output thickness table file (if save_table==True)
Examples
--------
>>> from mindboggle.utils.ants import thickinthehead
>>> segmented_file = '/Users/arno/Data/antsCorticalThickness/OASIS-TRT-20-1/tmp23314/tmpBrainSegmentation.nii.gz'
>>> labeled_file = '/appsdir/freesurfer/subjects/OASIS-TRT-20-1/mri/labels.DKT31.manual.nii.gz'
>>> cortex_value = 2
>>> noncortex_value = 3
>>> #labels = [2]
>>> labels = range(1002,1036) + range(2002,2036)
>>> labels.remove(1004)
>>> labels.remove(2004)
>>> labels.remove(1032)
>>> labels.remove(2032)
>>> labels.remove(1033)
>>> labels.remove(2033)
>>> names = []
>>> resize = True
>>> propagate = False
>>> output_dir = ''
>>> save_table = True
>>> label_volume_thickness, output_table = thickinthehead(segmented_file, labeled_file, cortex_value, noncortex_value, labels, names, resize, propagate, output_dir, save_table)
"""
import os
import numpy as np
import nibabel as nb
from mindboggle.utils.utils import execute
#-------------------------------------------------------------------------
# Output files:
#-------------------------------------------------------------------------
if output_dir:
if not os.path.exists(output_dir):
os.mkdir(output_dir)
else:
output_dir = os.getcwd()
cortex = os.path.join(output_dir, 'cortex.nii.gz')
noncortex = os.path.join(output_dir, 'noncortex.nii.gz')
temp = os.path.join(output_dir, 'temp.nii.gz')
inner_edge = os.path.join(output_dir, 'cortex_inner_edge.nii.gz')
use_outer_edge = True
if use_outer_edge:
outer_edge = os.path.join(output_dir, 'cortex_outer_edge.nii.gz')
if save_table:
output_table = os.path.join(os.getcwd(), 'thickinthehead.csv')
fid = open(output_table, 'w')
if names:
fid.write("Label name, Label number, Volume, "
"Thickness (thickinthehead)\n")
else:
fid.write("Label number, Volume, Thickness (thickinthehead)\n")
else:
output_table = ''
#-------------------------------------------------------------------------
# Extract noncortex and cortex:
#-------------------------------------------------------------------------
cmd = ['ThresholdImage 3', segmented_file,
noncortex, str(noncortex_value), str(noncortex_value), '1 0']
execute(cmd)
cmd = ['ThresholdImage 3', segmented_file,
cortex, str(cortex_value), str(cortex_value), '1 0']
execute(cmd)
#-------------------------------------------------------------------------
# Either mask labels with cortex or fill cortex with labels:
#-------------------------------------------------------------------------
if propagate:
cmd = ['ImageMath', '3', cortex, 'PropagateLabelsThroughMask',
cortex, labeled_file]
execute(cmd)
else:
cmd = ['ImageMath 3', cortex, 'm', cortex, labeled_file]
execute(cmd)
#-------------------------------------------------------------------------
# Resample cortex and noncortex files from 1x1x1 to 0.5x0.5x0.5
# to better represent the contours of the boundaries of the cortex:
#-------------------------------------------------------------------------
if resize:
rescale = 2.0
dims = ' '.join([str(1/rescale), str(1/rescale), str(1/rescale)])
cmd = ['ResampleImageBySpacing 3', cortex, cortex, dims, '0 0 1']
execute(cmd)
cmd = ['ResampleImageBySpacing 3', noncortex, noncortex, dims, '0 0 1']
execute(cmd)
#-------------------------------------------------------------------------
# Extract outer and inner boundary voxels of the cortex,
# by eroding 1 (resampled) voxel for cortex voxels (2) bordering
# the outside of the brain (0) and bordering noncortex (3):
#-------------------------------------------------------------------------
cmd = ['ImageMath 3', inner_edge, 'MD', noncortex, '1']
execute(cmd)
cmd = ['ImageMath 3', inner_edge, 'm', cortex, inner_edge]
execute(cmd)
if use_outer_edge:
cmd = ['ThresholdImage 3', cortex, outer_edge, '1 10000 1 0']
execute(cmd)
cmd = ['ImageMath 3', outer_edge, 'ME', outer_edge, '1']
execute(cmd)
cmd = ['ThresholdImage 3', outer_edge, outer_edge, '1 1 0 1']
execute(cmd)
cmd = ['ImageMath 3', outer_edge, 'm', cortex, outer_edge]
execute(cmd)
cmd = ['ThresholdImage 3', inner_edge, temp, '1 10000 1 0']
execute(cmd)
cmd = ['ThresholdImage 3', temp, temp, '1 1 0 1']
execute(cmd)
cmd = ['ImageMath 3', outer_edge, 'm', temp, outer_edge]
execute(cmd)
#-------------------------------------------------------------------------
# Load data:
#-------------------------------------------------------------------------
compute_real_volume = True
if compute_real_volume:
img = nb.load(cortex)
hdr = img.get_header()
vv = np.prod(hdr.get_zooms())
cortex_data = img.get_data().ravel()
else:
vv = 1
cortex_data = nb.load(cortex).get_data().ravel()
inner_edge_data = nb.load(inner_edge).get_data().ravel()
if use_outer_edge:
outer_edge_data = nb.load(outer_edge).get_data().ravel()
#-------------------------------------------------------------------------
# Loop through labels:
#-------------------------------------------------------------------------
if not labels:
labeled_data = nb.load(labeled_file).get_data().ravel()
labels = np.unique(labeled_data)
labels = [int(x) for x in labels]
label_volume_thickness = -1 * np.ones((len(labels), 3))
label_volume_thickness[:, 0] = labels
for ilabel, label in enumerate(labels):
if names:
name = names[ilabel]
#---------------------------------------------------------------------
# Compute thickness as a ratio of label volume and edge volume:
# - Estimate middle cortical surface area by the average volume
# of the outer and inner boundary voxels of the cortex.
# - Compute the volume of a labeled region of cortex.
# - Estimate the thickness of the labeled cortical region as the
# volume of the labeled region divided by the surface area.
#---------------------------------------------------------------------
label_cortex_volume = vv * len(np.where(cortex_data==label)[0])
label_inner_edge_volume = vv * len(np.where(inner_edge_data==label)[0])
if label_inner_edge_volume:
if use_outer_edge:
label_outer_edge_volume = \
vv * len(np.where(outer_edge_data==label)[0])
label_area = (label_inner_edge_volume +
label_outer_edge_volume) / 2.0
else:
label_area = label_inner_edge_volume
thickness = label_cortex_volume / label_area
label_volume_thickness[ilabel, 1] = label_cortex_volume
label_volume_thickness[ilabel, 2] = thickness
#print('label {0} volume: cortex={1:2.2f}, inner={2:2.2f}, '
# 'outer={3:2.2f}, area51={4:2.2f}, thickness={5:2.2f}mm'.
# format(name, label, label_cortex_volume, label_inner_edge_volume,
# label_outer_edge_volume, label_area, thickness))
if names:
print('{0} ({1}) volume={2:2.2f}, thickness={3:2.2f}mm'.
format(name, label, label_cortex_volume, thickness))
else:
print('{0}, volume={2:2.2f}, thickness={3:2.2f}mm'.
format(label, label_cortex_volume, thickness))
if save_table:
if names:
fid.write('{0}, {1}, {2:2.4f}, {3:2.4f}\n'.format(name,
label, label_cortex_volume, thickness))
else:
fid.write('{0}, {1:2.4f}, {2:2.4f}\n'.format(label,
label_cortex_volume, thickness))
label_volume_thickness = label_volume_thickness.transpose().tolist()
return label_volume_thickness, output_table
def PropagateLabelsThroughMask(mask, labels, mask_index=None,
output_file='', binarize=True, stopvalue=''):
"""
Use ANTs to fill a binary volume mask with initial labels.
This program uses ThresholdImage and the ImageMath
PropagateLabelsThroughMask functions in ANTS.
ThresholdImage ImageDimension ImageIn.ext outImage.ext
threshlo threshhi <insideValue> <outsideValue>
PropagateLabelsThroughMask: Final output is the propagated label image.
ImageMath ImageDimension Out.ext PropagateLabelsThroughMask
speed/binaryimagemask.nii.gz initiallabelimage.nii.gz ...
Parameters
----------
mask : string
nibabel-readable image volume
labels : string
nibabel-readable image volume with integer labels
mask_index : integer (optional)
mask with just voxels having this value
output_file : string
nibabel-readable labeled image volume
binarize : Boolean
binarize mask?
stopvalue : integer
stopping value
Returns
-------
output_file : string
name of labeled output nibabel-readable image volume
Examples
--------
>>> import os
>>> from mindboggle.utils.ants import PropagateLabelsThroughMask
>>> from mindboggle.utils.plots import plot_volumes
>>> path = os.path.join(os.environ['MINDBOGGLE_DATA'])
>>> labels = os.path.join(path, 'arno', 'labels', 'labels.DKT25.manual.nii.gz')
>>> mask = os.path.join(path, 'arno', 'mri', 't1weighted_brain.nii.gz')
>>> mask_index = None
>>> output_file = ''
>>> binarize = True
>>> stopvalue = None
>>> output_file = PropagateLabelsThroughMask(mask, labels, mask_index, output_file, binarize, stopvalue)
>>> # View
>>> plot_volumes(output_file)
"""
import os
from mindboggle.utils.utils import execute
if not output_file:
output_file = os.path.join(os.getcwd(),
'PropagateLabelsThroughMask.nii.gz')
output_file = os.path.join(os.getcwd(),
os.path.basename(labels) + '_through_' +
os.path.basename(mask))
print('mask: {0}, labels: {1}'.format(mask, labels))
# Binarize image volume:
if binarize:
temp_file = os.path.join(os.getcwd(),
'PropagateLabelsThroughMask.nii.gz')
cmd = ['ThresholdImage', '3', mask, temp_file, '0 1 0 1']
execute(cmd, 'os')
mask = temp_file
# Mask with just voxels having mask_index value:
if mask_index:
mask2 = os.path.join(os.getcwd(), 'temp.nii.gz')
cmd = 'ThresholdImage 3 {0} {1} {2} {3} 1 0'.format(mask, mask2,
mask_index, mask_index)
execute(cmd)
else:
mask2 = mask
# Propagate labels:
cmd = ['ImageMath', '3', output_file, 'PropagateLabelsThroughMask',
mask2, labels]
if stopvalue:
cmd.extend(stopvalue)
execute(cmd, 'os')
if not os.path.exists(output_file):
raise(IOError(output_file + " not found"))
return output_file
def ImageMath(volume1, volume2, operator='m', output_file=''):
"""
Use the ImageMath function in ANTs to perform operation on two volumes::
m : Multiply --- use vm for vector multiply
+ : Add --- use v+ for vector add
- : Subtract --- use v- for vector subtract
/ : Divide
^ : Power
exp : Take exponent exp(imagevalue*value)
addtozero : add image-b to image-a only over points where image-a has zero values
overadd : replace image-a pixel with image-b pixel if image-b pixel is non-zero
abs : absolute value
total : Sums up values in an image or in image1*image2 (img2 is the probability mask)
mean : Average of values in an image or in image1*image2 (img2 is the probability mask)
vtotal : Sums up volumetrically weighted values in an image or in image1*image2 (img2 is the probability mask)
Decision : Computes result=1./(1.+exp(-1.0*( pix1-0.25)/pix2))
Neg : Produce image negative
Parameters
----------
volume1 : string
nibabel-readable image volume
volume2 : string
nibabel-readable image volume
operator : string
ImageMath string corresponding to mathematical operator
output_file : string
nibabel-readable image volume
Returns
-------
output_file : string
name of output nibabel-readable image volume
Examples
--------
>>> import os
>>> from mindboggle.utils.ants import ImageMath
>>> from mindboggle.utils.plots import plot_volumes
>>> path = os.path.join(os.environ['MINDBOGGLE_DATA'])
>>> volume1 = os.path.join(path, 'arno', 'mri', 't1weighted.nii.gz')
>>> volume2 = os.path.join(path, 'arno', 'mri', 'mask.nii.gz')
>>> operator = 'm'
>>> output_file = ''
>>> output_file = ImageMath(volume1, volume2, operator, output_file)
>>> # View
>>> plot_volumes(output_file)
"""
import os
from mindboggle.utils.utils import execute
if not output_file:
output_file = os.path.join(
os.getcwd(),
os.path.basename(volume1) + '_' + os.path.basename(volume2))
cmd = ['ImageMath', '3', output_file, operator, volume1, volume2]
execute(cmd, 'os')
if not os.path.exists(output_file):
raise (IOError("ImageMath did not create " + output_file + "."))
return output_file
def ImageMath(volume1, volume2, operator='m', output_file=''):
"""
Use the ImageMath function in ANTS to perform operation on two volumes::
m : Multiply --- use vm for vector multiply
+ : Add --- use v+ for vector add
- : Subtract --- use v- for vector subtract
/ : Divide
^ : Power
exp : Take exponent exp(imagevalue*value)
addtozero : add image-b to image-a only over points where image-a has zero values
overadd : replace image-a pixel with image-b pixel if image-b pixel is non-zero
abs : absolute value
total : Sums up values in an image or in image1*image2 (img2 is the probability mask)
mean : Average of values in an image or in image1*image2 (img2 is the probability mask)
vtotal : Sums up volumetrically weighted values in an image or in image1*image2 (img2 is the probability mask)
Decision : Computes result=1./(1.+exp(-1.0*( pix1-0.25)/pix2))
Neg : Produce image negative
Parameters
----------
volume1 : string
nibabel-readable image volume
volume2 : string
nibabel-readable image volume
operator : string
ImageMath string corresponding to mathematical operator
output_file : string
nibabel-readable image volume
Returns
-------
output_file : string
name of output nibabel-readable image volume
Examples
--------
>>> import os
>>> from mindboggle.utils.ants import ImageMath
>>> from mindboggle.utils.plots import plot_volumes
>>> path = os.path.join(os.environ['MINDBOGGLE_DATA'])
>>> volume1 = os.path.join(path, 'arno', 'mri', 't1weighted.nii.gz')
>>> volume2 = os.path.join(path, 'arno', 'mri', 'mask.nii.gz')
>>> operator = 'm'
>>> output_file = ''
>>> output_file = ImageMath(volume1, volume2, operator, output_file)
>>> # View
>>> plot_volumes(output_file)
"""
import os
from mindboggle.utils.utils import execute
if not output_file:
output_file = os.path.join(os.getcwd(),
os.path.basename(volume1) + '_' +
os.path.basename(volume2))
cmd = ['ImageMath', '3', output_file, operator, volume1, volume2]
execute(cmd, 'os')
if not os.path.exists(output_file):
raise(IOError(output_file + " not found"))
return output_file
def antsApplyTransformsToPoints(points, transform_files, inverse_booleans=[0]):
"""
Run ANTs antsApplyTransformsToPoints function to transform points.
(Creates pre- and post-transformed .csv points files for ANTs.)
Parameters
----------
points : list of lists of three integers
point coordinate data
transform_files : list
transform file names
inverse_booleans : list
for each transform, one to apply inverse of transform (otherwise zero)
Returns
-------
transformed_points : list of lists of three integers
transformed point coordinate data
Examples
--------
>>> from mindboggle.utils.ants import antsApplyTransformsToPoints
>>> from mindboggle.utils.io_vtk import read_vtk
>>> transform_files = ['/Users/arno/mindboggle_working/OASIS-TRT-20-1/Mindboggle/Compose_affine_transform/affine.txt']
>>> vtk_file = '/Users/arno/mindboggle_working/OASIS-TRT-20-1/Mindboggle/_hemi_lh/Surface_to_vtk/lh.pial.vtk'
>>> faces, lines, indices, points, npoints, scalars, name, foo1 = read_vtk(vtk_file)
>>> inverse_booleans = [0]
>>> transformed_points = antsApplyTransformsToPoints(points, transform_files, inverse_booleans)
"""
import os
from mindboggle.utils.utils import execute
#-------------------------------------------------------------------------
# Write points (x,y,z,1) to a .csv file:
#-------------------------------------------------------------------------
points_file = os.path.join(os.getcwd(), 'points.csv')
fid = open(points_file, 'wa')
fid.write('x,y,z\n')
for point in points:
fid.write(','.join([str(x) for x in point]) + '\n')
fid.close()
#-------------------------------------------------------------------------
# Apply transforms to points in .csv file:
#-------------------------------------------------------------------------
transformed_points_file = os.path.join(os.getcwd(),
'transformed_points.csv')
transform_string = ''
for ixfm, transform_file in enumerate(transform_files):
transform_string += "--t [{0},{1}]".format(transform_file,
str(inverse_booleans[ixfm]))
cmd = ['antsApplyTransformsToPoints', '-d', '3', '-i', points_file,
'-o', transformed_points_file, transform_string]
execute(cmd, 'os')
if not os.path.exists(transformed_points_file):
raise(IOError(transformed_points_file + " not found"))
#-------------------------------------------------------------------------
# Return transformed points:
#-------------------------------------------------------------------------
fid = open(transformed_points_file, 'r')
lines = fid.readlines()
fid.close()
transformed_points = []
for iline, line in enumerate(lines):
if iline > 0:
point_xyz1 = [float(x) for x in line.split(',')]
transformed_points.append(point_xyz1[0:3])
return transformed_points
def PropagateLabelsThroughMask(mask_volume, label_volume, output_file='',
binarize=True):
"""
Use ANTs to fill a binary volume mask with initial labels.
This program uses ThresholdImage and the ImageMath
PropagateLabelsThroughMask functions in ANTS.
ThresholdImage ImageDimension ImageIn.ext outImage.ext
threshlo threshhi <insideValue> <outsideValue>
PropagateLabelsThroughMask: Final output is the propagated label image.
ImageMath ImageDimension Out.ext PropagateLabelsThroughMask
speed/binaryimagemask.nii.gz initiallabelimage.nii.gz ...
Parameters
----------
label_volume : string
nibabel-readable image volume with integer labels
mask_volume : string
nibabel-readable image volume
output_file : string
nibabel-readable labeled image volume
binarize : Boolean
binarize mask_volume?
Returns
-------
output_file : string
name of labeled output nibabel-readable image volume
Examples
--------
>>> import os
>>> from mindboggle.utils.ants import PropagateLabelsThroughMask
>>> from mindboggle.utils.plots import plot_volumes
>>> path = os.path.join(os.environ['MINDBOGGLE_DATA'])
>>> label_volume = os.path.join(path, 'arno', 'labels', 'labels.DKT25.manual.nii.gz')
>>> mask_volume = os.path.join(path, 'arno', 'mri', 't1weighted_brain.nii.gz')
>>> output_file = ''
>>> binarize = True
>>> output_file = PropagateLabelsThroughMask(mask_volume, label_volume,
>>> output_file, binarize)
>>> # View
>>> plot_volumes(output_file)
"""
import os
from mindboggle.utils.utils import execute
if not output_file:
output_file = os.path.join(os.getcwd(), 'propagated_labels.nii.gz')
# Binarize image volume:
if binarize:
temp_file = os.path.join(os.getcwd(), 'propagated_labels.nii.gz')
cmd = ['ThresholdImage', '3', mask_volume, temp_file, '0 1 0 1']
execute(cmd, 'os')
mask_volume = temp_file
# Propagate labels:
cmd = ['ImageMath', '3', output_file, 'PropagateLabelsThroughMask',
mask_volume, label_volume]
execute(cmd, 'os')
if not os.path.exists(output_file):
raise(IOError(output_file + " not found"))
return output_file
def thickinthehead(segmented_file,
labeled_file,
cortex_value=2,
noncortex_value=3,
labels=[],
names=[],
resize=True,
propagate=True,
output_dir='',
save_table=False,
output_table=''):
"""
Compute a simple thickness measure for each labeled cortex region.
Note::
- Cortex, noncortex, & label files are from the same coregistered brain.
- Calls ANTs functions: ImageMath, Threshold, ResampleImageBySpacing
- There may be slight discrepancies between volumes computed by
thickinthehead() and volumes computed by volume_per_label();
in 31 of 600+ ADNI 1.5T images, some volume_per_label() volumes
were slightly larger (in the third decimal place), presumably due to
label propagation through the cortex.
Example preprocessing steps ::
1. Run Freesurfer and antsCorticalThickness.sh on T1-weighted image.
2. Convert FreeSurfer volume labels (e.g., wmparc.mgz or aparc+aseg.mgz)
to cortex (2) and noncortex (3) segments using relabel_volume()
function [refer to LABELS.py or FreeSurferColorLUT labels file].
3. Convert ANTs Atropos-segmented volume (tmpBrainSegmentation.nii.gz)
to cortex and noncortex segments, by converting 1-labels to 0 and
4-labels to 3 with the relabel_volume() function
(the latter is to include deep-gray matter with noncortical tissues).
4. Combine FreeSurfer and ANTs segmentation volumes to obtain a single
cortex (2) and noncortex (3) segmentation file using the function
combine_2labels_in_2volumes(). This function takes the union of
cortex voxels from the segmentations, the union of the noncortex
voxels from the segmentations, and overwrites intersecting cortex
and noncortex voxels with noncortex (3) labels.
ANTs tends to include more cortical gray matter at the periphery of
the brain than Freesurfer, and FreeSurfer tends to include more white
matter that extends deep into gyral folds than ANTs, so the above
attempts to remedy their differences by overlaying ANTs cortical gray
with FreeSurfer white matter.
5. Optional, see Step 2 below:
Fill segmented cortex with cortex labels and noncortex with
noncortex labels using the PropagateLabelsThroughMask() function
(which calls ImageMath ... PropagateLabelsThroughMask in ANTs).
The labels can be initialized using FreeSurfer (e.g. wmparc.mgz)
or ANTs (by applying the nonlinear inverse transform generated by
antsCorticalThickness.sh to labels in the Atropos template space).
[Note: Any further labeling steps may be applied, such as
overwriting cerebrum with intersecting cerebellum labels.]
Steps ::
1. Extract noncortex and cortex.
2. Either mask labels with cortex or fill cortex with labels.
3. Resample cortex and noncortex files from 1x1x1 to 0.5x0.5x0.5
to better represent the contours of the boundaries of the cortex.
4. Extract outer and inner boundary voxels of the cortex,
by eroding 1 (resampled) voxel for cortex voxels (2) bordering
the outside of the brain (0) and bordering noncortex (3).
5. Estimate middle cortical surface area by the average volume
of the outer and inner boundary voxels of the cortex.
6. Compute the volume of a labeled region of cortex.
7. Estimate the thickness of the labeled cortical region as the
volume of the labeled region (#6) divided by the surface area (#5).
Parameters
----------
segmented_file : string
image volume with cortex and noncortex (and any other) labels
labeled_file : string
corresponding image volume with index labels
cortex_value : integer
cortex label value in segmented_file
noncortex_value : integer
noncortex label value in segmented_file
labels : list of integers
label indices
names : list of strings
label names
resize : Boolean
resize (2x) segmented_file for more accurate thickness estimates?
propagate : Boolean
propagate labels through cortex?
output_dir : string
output directory
save_table : Boolean
save output table file with label volumes and thickness values?
output_table : string
name of output table file with label volumes and thickness values
Returns
-------
label_volume_thickness : list of lists of integers and floats
label indices, volumes, and thickness values (default -1)
output_table : string
name of output table file with label volumes and thickness values
Examples
--------
>>> from mindboggle.utils.ants import thickinthehead
>>> segmented_file = '/Users/arno/Data/antsCorticalThickness/OASIS-TRT-20-1/antsBrainSegmentation.nii.gz'
>>> labeled_file = '/appsdir/freesurfer/subjects/OASIS-TRT-20-1/mri/labels.DKT31.manual.nii.gz'
>>> cortex_value = 2
>>> noncortex_value = 3
>>> #labels = [2]
>>> labels = range(1002,1036) + range(2002,2036)
>>> labels.remove(1004)
>>> labels.remove(2004)
>>> labels.remove(1032)
>>> labels.remove(2032)
>>> labels.remove(1033)
>>> labels.remove(2033)
>>> names = []
>>> resize = True
>>> propagate = False
>>> output_dir = ''
>>> save_table = True
>>> output_table = ''
>>> label_volume_thickness, output_table = thickinthehead(segmented_file, labeled_file, cortex_value, noncortex_value, labels, names, resize, propagate, output_dir, save_table, output_table)
"""
import os
import numpy as np
import nibabel as nb
from mindboggle.utils.utils import execute
#-------------------------------------------------------------------------
# Output files:
#-------------------------------------------------------------------------
if output_dir:
if not os.path.exists(output_dir):
os.mkdir(output_dir)
else:
output_dir = os.getcwd()
cortex = os.path.join(output_dir, 'cortex.nii.gz')
noncortex = os.path.join(output_dir, 'noncortex.nii.gz')
temp = os.path.join(output_dir, 'temp.nii.gz')
inner_edge = os.path.join(output_dir, 'cortex_inner_edge.nii.gz')
use_outer_edge = True
if use_outer_edge:
outer_edge = os.path.join(output_dir, 'cortex_outer_edge.nii.gz')
if save_table:
if output_table:
output_table = os.path.join(os.getcwd(), output_table)
else:
output_table = os.path.join(os.getcwd(),
'thickinthehead_per_label.csv')
fid = open(output_table, 'w')
if names:
fid.write(
"Label name,Label number,Volume,Thickness (thickinthehead)\n")
else:
fid.write("Label number,Volume,Thickness (thickinthehead)\n")
else:
output_table = ''
#-------------------------------------------------------------------------
# Extract noncortex and cortex:
#-------------------------------------------------------------------------
cmd = [
'ThresholdImage 3', segmented_file, noncortex,
str(noncortex_value),
str(noncortex_value), '1 0'
]
execute(cmd)
cmd = [
'ThresholdImage 3', segmented_file, cortex,
str(cortex_value),
str(cortex_value), '1 0'
]
execute(cmd)
#-------------------------------------------------------------------------
# Either mask labels with cortex or fill cortex with labels:
#-------------------------------------------------------------------------
if propagate:
cmd = [
'ImageMath', '3', cortex, 'PropagateLabelsThroughMask', cortex,
labeled_file
]
execute(cmd)
else:
cmd = ['ImageMath 3', cortex, 'm', cortex, labeled_file]
execute(cmd)
#-------------------------------------------------------------------------
# Load data and dimensions:
#-------------------------------------------------------------------------
if resize:
rescale = 2.0
else:
rescale = 1.0
compute_real_volume = True
if compute_real_volume:
img = nb.load(cortex)
hdr = img.get_header()
vv_orig = np.prod(hdr.get_zooms())
vv = np.prod([x / rescale for x in hdr.get_zooms()])
cortex_data = img.get_data().ravel()
else:
vv = 1 / rescale
cortex_data = nb.load(cortex).get_data().ravel()
#-------------------------------------------------------------------------
# Resample cortex and noncortex files from 1x1x1 to 0.5x0.5x0.5
# to better represent the contours of the boundaries of the cortex:
#-------------------------------------------------------------------------
if resize:
dims = ' '.join([str(1 / rescale), str(1 / rescale), str(1 / rescale)])
cmd = ['ResampleImageBySpacing 3', cortex, cortex, dims, '0 0 1']
execute(cmd)
cmd = ['ResampleImageBySpacing 3', noncortex, noncortex, dims, '0 0 1']
execute(cmd)
#-------------------------------------------------------------------------
# Extract outer and inner boundary voxels of the cortex,
# by eroding 1 (resampled) voxel for cortex voxels (2) bordering
# the outside of the brain (0) and bordering noncortex (3):
#-------------------------------------------------------------------------
cmd = ['ImageMath 3', inner_edge, 'MD', noncortex, '1']
execute(cmd)
cmd = ['ImageMath 3', inner_edge, 'm', cortex, inner_edge]
execute(cmd)
if use_outer_edge:
cmd = ['ThresholdImage 3', cortex, outer_edge, '1 10000 1 0']
execute(cmd)
cmd = ['ImageMath 3', outer_edge, 'ME', outer_edge, '1']
execute(cmd)
cmd = ['ThresholdImage 3', outer_edge, outer_edge, '1 1 0 1']
execute(cmd)
cmd = ['ImageMath 3', outer_edge, 'm', cortex, outer_edge]
execute(cmd)
cmd = ['ThresholdImage 3', inner_edge, temp, '1 10000 1 0']
execute(cmd)
cmd = ['ThresholdImage 3', temp, temp, '1 1 0 1']
execute(cmd)
cmd = ['ImageMath 3', outer_edge, 'm', temp, outer_edge]
execute(cmd)
#-------------------------------------------------------------------------
# Load data:
#-------------------------------------------------------------------------
inner_edge_data = nb.load(inner_edge).get_data().ravel()
if use_outer_edge:
outer_edge_data = nb.load(outer_edge).get_data().ravel()
#-------------------------------------------------------------------------
# Loop through labels:
#-------------------------------------------------------------------------
if not labels:
labeled_data = nb.load(labeled_file).get_data().ravel()
labels = np.unique(labeled_data)
labels = [int(x) for x in labels]
label_volume_thickness = -1 * np.ones((len(labels), 3))
label_volume_thickness[:, 0] = labels
for ilabel, label in enumerate(labels):
if names:
name = names[ilabel]
#---------------------------------------------------------------------
# Compute thickness as a ratio of label volume and edge volume:
# - Estimate middle cortical surface area by the average volume
# of the outer and inner boundary voxels of the cortex.
# - Compute the volume of a labeled region of cortex.
# - Estimate the thickness of the labeled cortical region as the
# volume of the labeled region divided by the surface area.
#---------------------------------------------------------------------
label_cortex_volume = vv_orig * len(np.where(cortex_data == label)[0])
label_inner_edge_volume = vv * len(
np.where(inner_edge_data == label)[0])
if label_inner_edge_volume:
if use_outer_edge:
label_outer_edge_volume = \
vv * len(np.where(outer_edge_data==label)[0])
label_area = (label_inner_edge_volume +
label_outer_edge_volume) / 2.0
else:
label_area = label_inner_edge_volume
thickness = label_cortex_volume / label_area
label_volume_thickness[ilabel, 1] = label_cortex_volume
label_volume_thickness[ilabel, 2] = thickness
#print('label {0} volume: cortex={1:2.2f}, inner={2:2.2f}, '
# 'outer={3:2.2f}, area51={4:2.2f}, thickness={5:2.2f}mm'.
# format(name, label, label_cortex_volume, label_inner_edge_volume,
# label_outer_edge_volume, label_area, thickness))
if names:
print('{0} ({1}) volume={2:2.2f}, thickness={3:2.2f}mm'.format(
name, label, label_cortex_volume, thickness))
else:
print('{0}, volume={2:2.2f}, thickness={3:2.2f}mm'.format(
label, label_cortex_volume, thickness))
if save_table:
if names:
fid.write('{0}, {1}, {2:2.4f}, {3:2.4f}\n'.format(
name, label, label_cortex_volume, thickness))
else:
fid.write('{0}, {1:2.4f}, {2:2.4f}\n'.format(
label, label_cortex_volume, thickness))
label_volume_thickness = label_volume_thickness.transpose().tolist()
return label_volume_thickness, output_table
def PropagateLabelsThroughMask(mask,
labels,
mask_index=None,
output_file='',
binarize=True,
stopvalue=''):
"""
Use ANTs to fill a binary volume mask with initial labels.
This program uses ThresholdImage and the ImageMath
PropagateLabelsThroughMask functions in ANTs.
ThresholdImage ImageDimension ImageIn.ext outImage.ext
threshlo threshhi <insideValue> <outsideValue>
PropagateLabelsThroughMask: Final output is the propagated label image.
ImageMath ImageDimension Out.ext PropagateLabelsThroughMask
speed/binaryimagemask.nii.gz initiallabelimage.nii.gz ...
Parameters
----------
mask : string
nibabel-readable image volume
labels : string
nibabel-readable image volume with integer labels
mask_index : integer (optional)
mask with just voxels having this value
output_file : string
nibabel-readable labeled image volume
binarize : Boolean
binarize mask?
stopvalue : integer
stopping value
Returns
-------
output_file : string
name of labeled output nibabel-readable image volume
Examples
--------
>>> import os
>>> from mindboggle.utils.ants import PropagateLabelsThroughMask
>>> from mindboggle.utils.plots import plot_volumes
>>> path = os.path.join(os.environ['MINDBOGGLE_DATA'])
>>> labels = os.path.join(path, 'arno', 'labels', 'labels.DKT25.manual.nii.gz')
>>> mask = os.path.join(path, 'arno', 'mri', 't1weighted_brain.nii.gz')
>>> mask_index = None
>>> output_file = ''
>>> binarize = True
>>> stopvalue = None
>>> output_file = PropagateLabelsThroughMask(mask, labels, mask_index, output_file, binarize, stopvalue)
>>> # View
>>> plot_volumes(output_file)
"""
import os
from mindboggle.utils.utils import execute
if not output_file:
output_file = os.path.join(os.getcwd(),
'PropagateLabelsThroughMask.nii.gz')
output_file = os.path.join(
os.getcwd(),
os.path.basename(labels) + '_through_' + os.path.basename(mask))
print('mask: {0}, labels: {1}'.format(mask, labels))
# Binarize image volume:
if binarize:
temp_file = os.path.join(os.getcwd(),
'PropagateLabelsThroughMask.nii.gz')
cmd = ['ThresholdImage', '3', mask, temp_file, '0 1 0 1']
execute(cmd, 'os')
mask = temp_file
# Mask with just voxels having mask_index value:
if mask_index:
mask2 = os.path.join(os.getcwd(), 'temp.nii.gz')
cmd = 'ThresholdImage 3 {0} {1} {2} {3} 1 0'.format(
mask, mask2, mask_index, mask_index)
execute(cmd)
else:
mask2 = mask
# Propagate labels:
cmd = [
'ImageMath', '3', output_file, 'PropagateLabelsThroughMask', mask2,
labels
]
if stopvalue:
cmd.extend(stopvalue)
execute(cmd, 'os')
if not os.path.exists(output_file):
raise (IOError("ImageMath did not create " + output_file + "."))
return output_file
def ANTS(source, target, iterations='30x99x11', output_stem=''):
"""
Use ANTs to register a source image volume to a target image volume.
This program uses the ANTs SyN registration method.
Parameters
----------
source : string
file name of source image volume
target : string
file name of target image volume
iterations : string
number of iterations ("0" for affine, "30x99x11" default)
output_stem : string
file name stem for output transform matrix
Returns
-------
affine_transform : string
file name for affine transform matrix
nonlinear_transform : string
file name for nonlinear transform nifti file
nonlinear_inverse_transform : string
file name for nonlinear inverse transform nifti file
output_stem : string
file name stem for output transform matrix
Examples
--------
>>> import os
>>> from mindboggle.utils.ants import ANTS
>>> path = os.environ['MINDBOGGLE_DATA']
>>> source = os.path.join(path, 'arno', 'mri', 't1weighted_brain.nii.gz')
>>> target = os.path.join(path, 'atlases', 'MNI152_T1_1mm_brain.nii.gz')
>>> iterations = "0"
>>> output_stem = ""
>>> #
>>> ANTS(source, target, iterations, output_stem)
"""
import os
from mindboggle.utils.utils import execute
if not output_stem:
src = os.path.basename(source).split('.')[0]
tgt = os.path.basename(target).split('.')[0]
output_stem = os.path.join(os.getcwd(), src+'_to_'+tgt)
cmd = ['ANTS', '3', '-m CC[' + target + ',' + source + ',1,2]',
'-r Gauss[2,0]', '-t SyN[0.5] -i', iterations,
'-o', output_stem, '--use-Histogram-Matching',
'--number-of-affine-iterations 10000x10000x10000x10000x10000']
execute(cmd, 'os')
affine_transform = output_stem + 'Affine.txt'
nonlinear_transform = output_stem + 'Warp.nii.gz'
nonlinear_inverse_transform = output_stem + 'InverseWarp.nii.gz'
if not os.path.exists(affine_transform):
raise(IOError(affine_transform + " not found"))
if not os.path.exists(nonlinear_transform):
raise(IOError(nonlinear_transform + " not found"))
if not os.path.exists(nonlinear_inverse_transform):
raise(IOError(nonlinear_inverse_transform + " not found"))
return affine_transform, nonlinear_transform,\
nonlinear_inverse_transform, output_stem
def label_with_classifier(subject, hemi, left_classifier='',
right_classifier='', annot_file=''):
"""
Label a brain with the DKT atlas using FreeSurfer's mris_ca_label
FreeSurfer documentation ::
SYNOPSIS
mris_ca_label [options] <subject> <hemi> <surf> <classifier> <output>
DESCRIPTION
For a single subject, produces an annotation file, in which each
cortical surface vertex is assigned a neuroanatomical label.
This automatic procedure employs data from a previously-prepared atlas
file. An atlas file is created from a training set, capturing region
data manually drawn by neuroanatomists combined with statistics on
variability correlated to geometric information derived from the
cortical model (sulcus and curvature). Besides the atlases provided
with FreeSurfer, new ones can be prepared using mris_ca_train).
Notes regarding creation and use of FreeSurfer Gaussian classifier atlas:
Create the DKT classifier atlas (?h.DKTatlas40.gcs) --NEED TO VERIFY THIS:
$ mris_ca_train -t $FREESURFERHOME/average/colortable_desikan_killiany.txt \
$hemi sphere.reg aparcNMMjt.annot $SCANS ./$hemi.DKTatlas40.gcs
Label a brain with the DKT atlas (surface annotation file ?h.DKTatlas40.annot):
$ mris_ca_label -l ./$x/label/$hemi.cortex.label $x/ $hemi sphere.reg \
./$hemi.DKTatlas40.gcs ./$x/label/$hemi.DKTatlas40.annot
Label the cortex of a subject's segmented volume according
to the edited surface labels (?h.aparcNMMjt.annot):
$ mri_aparc2aseg --s ./x --volmask --annot aparcNMMjt
Label a brain with the DKT atlas using FreeSurfer's mris_ca_label:
$ mris_ca_label MMRR-21-1 lh lh.sphere.reg ../lh.DKTatlas40.gcs ../out.annot
Parameters
----------
subject : string
subject corresponding to FreeSurfer subject directory
hemi : string
hemisphere ['lh' or 'rh']
left_classifier : string
name of left hemisphere FreeSurfer classifier atlas (full path)
right_classifier : string
name of right hemisphere FreeSurfer classifier atlas (full path)
annot_file : string
name of output .annot file
Returns
-------
annot_file : string
name of output .annot file
Examples
--------
>>> # This example requires a FreeSurfer subjects/<subject> subdirectory
>>> import os
>>> from mindboggle.labels.label_free import label_with_classifier
>>> subject = 'Twins-2-1'
>>> hemi = 'lh'
>>> left_classifier = '/homedir/mindboggle_cache/b28a600a713c269f4c561f66f64337b2/lh.DKTatlas40.gcs'
>>> right_classifier = ''
>>> annot_file = './lh.classifier.annot'
>>> label_with_classifier(subject, hemi, left_classifier, right_classifier, annot_file)
>>> #
>>> # View:
>>> from mindboggle.utils.io_free import annot_to_vtk
>>> from mindboggle.utils.plots import plot_vtk
>>> path = os.environ['MINDBOGGLE_DATA']
>>> vtk_file = os.path.join(path, 'arno', 'freesurfer', 'lh.pial.vtk')
>>> output_vtk = './lh.classifier.vtk'
>>> #
>>> labels, output_vtk = annot_to_vtk(annot_file, vtk_file, output_vtk)
>>> plot_vtk(output_vtk)
"""
import os
from mindboggle.utils.utils import execute
if not annot_file:
annot_file = os.path.join(os.getcwd(), hemi + '.classifier.annot')
if hemi == 'lh':
classifier = left_classifier
elif hemi == 'rh':
classifier = right_classifier
else:
print("label_with_classifier()'s hemi should be 'lh' or 'rh'")
cmd = ['mris_ca_label', subject, hemi, hemi+'.sphere.reg', classifier,
annot_file]
execute(cmd)
if not os.path.exists(annot_file):
raise(IOError(annot_file + " not found"))
return annot_file
def WarpImageMultiTransform(source, target, output='',
interp='--use-NN', xfm_stem='',
affine_transform='', nonlinear_transform='',
inverse=False, affine_only=False):
"""
Use ANTs to transform a source image volume to a target image volume.
This program uses the ANTs WarpImageMultiTransform function.
Parameters
----------
source : string
file name of source image volume
target : string
file name of target (reference) image volume
output : string
file name of output image volume
interp : string
interpolation type ("--use-NN" for nearest neighbor)
xfm_stem : string
file name stem for output transform
affine_transform : string
file containing affine transform
nonlinear_transform : string
file containing nonlinear transform
inverse : Boolean
apply inverse transform?
affine_only : Boolean
apply only affine transform?
Returns
-------
output : string
output label file name
"""
import os
import sys
from mindboggle.utils.utils import execute
if xfm_stem:
affine_transform = xfm_stem + 'Affine.txt'
if inverse:
nonlinear_transform = xfm_stem + 'InverseWarp.nii.gz'
else:
nonlinear_transform = xfm_stem + 'Warp.nii.gz'
elif not affine_transform and not nonlinear_transform:
sys.exit('Provide either xfm_stem or affine_transform and '
'nonlinear_transform.')
if not output:
output = os.path.join(os.getcwd(), 'WarpImageMultiTransform.nii.gz')
if not os.path.exists(nonlinear_transform):
affine_only = True
if affine_only:
if inverse:
cmd = ['WarpImageMultiTransform', '3', source, output, '-R',
target, interp, '-i', affine_transform]
else:
cmd = ['WarpImageMultiTransform', '3', source, output, '-R',
target, interp, affine_transform]
else:
if inverse:
cmd = ['WarpImageMultiTransform', '3', source, output, '-R',
target, interp, '-i', affine_transform, nonlinear_transform]
else:
cmd = ['WarpImageMultiTransform', '3', source, output, '-R',
target, interp, nonlinear_transform, affine_transform]
execute(cmd, 'os')
if not os.path.exists(output):
raise(IOError(output + " not found"))
return output
def plot_mask_surface(vtk_file, mask_file='', nonmask_value=-1,
masked_output='', remove_nonmask=False,
program='vtkviewer',
use_colormap=False, colormap_file=''):
"""
Use vtkviewer or mayavi2 to visualize VTK surface mesh data.
If a mask_file is provided, a temporary masked file is saved,
and it is this file that is viewed.
If using vtkviewer, can optionally provide colormap file
or set $COLORMAP environment variable.
Parameters
----------
vtk_file : string
name of VTK surface mesh file
mask_file : string
name of VTK surface mesh file to mask vtk_file vertices
nonmask_value : integer
nonmask (usually background) value
masked_output : string
temporary masked output file name
remove_nonmask : Boolean
remove vertices that are not in mask? (otherwise assign nonmask_value)
program : string {'vtkviewer', 'mayavi2'}
program to visualize VTK file
use_colormap : Boolean
use Paraview-style XML colormap file set by $COLORMAP env variable?
colormap_file : string
use colormap in given file if use_colormap==True? if empty and
use_colormap==True, use file set by $COLORMAP environment variable
Examples
--------
>>> import os
>>> from mindboggle.utils.plots import plot_mask_surface
>>> path = os.environ['MINDBOGGLE_DATA']
>>> vtk_file = os.path.join(path, 'arno', 'labels', 'lh.labels.DKT31.manual.vtk')
>>> mask_file = os.path.join(path, 'test_one_label.vtk')
>>> nonmask_value = 0 #-1
>>> masked_output = ''
>>> remove_nonmask = True
>>> program = 'vtkviewer'
>>> use_colormap = True
>>> colormap_file = '' #'/software/mindboggle_tools/colormap.xml'
>>> plot_mask_surface(vtk_file, mask_file, nonmask_value, masked_output, remove_nonmask, program, use_colormap, colormap_file)
"""
import os
import numpy as np
from mindboggle.utils.mesh import remove_faces, reindex_faces_points
from mindboggle.utils.utils import execute
from mindboggle.utils.plots import plot_surfaces
from mindboggle.utils.io_vtk import read_scalars, rewrite_scalars, \
read_vtk, write_vtk
#-------------------------------------------------------------------------
# Filter mesh with non-background values from a second (same-size) mesh:
#-------------------------------------------------------------------------
if mask_file:
mask, name = read_scalars(mask_file, True, True)
if not masked_output:
masked_output = os.path.join(os.getcwd(), 'temp.vtk')
file_to_plot = masked_output
#---------------------------------------------------------------------
# Remove nonmask-valued vertices:
#---------------------------------------------------------------------
if remove_nonmask:
#-----------------------------------------------------------------
# Load VTK files:
#-----------------------------------------------------------------
faces, lines, indices, points, npoints, scalars, scalar_names, \
o1 = read_vtk(vtk_file, True, True)
#-----------------------------------------------------------------
# Find mask indices, remove nonmask faces, and reindex:
#-----------------------------------------------------------------
Imask = [i for i,x in enumerate(mask) if x != nonmask_value]
mask_faces = remove_faces(faces, Imask)
mask_faces, points, \
original_indices = reindex_faces_points(mask_faces, points)
#-----------------------------------------------------------------
# Write VTK file with scalar values:
#-----------------------------------------------------------------
if np.ndim(scalars) == 1:
scalar_type = type(scalars[0]).__name__
elif np.ndim(scalars) == 2:
scalar_type = type(scalars[0][0]).__name__
else:
print("Undefined scalar type!")
write_vtk(file_to_plot, points, [], [], mask_faces,
scalars[original_indices].tolist(), scalar_names,
scalar_type=scalar_type)
else:
scalars, name = read_scalars(vtk_file, True, True)
scalars[mask == nonmask_value] = nonmask_value
rewrite_scalars(vtk_file, file_to_plot, scalars)
else:
file_to_plot = vtk_file
#-------------------------------------------------------------------------
# Display with vtkviewer.py:
#-------------------------------------------------------------------------
if program == 'vtkviewer':
plot_surfaces(file_to_plot, use_colormap=use_colormap,
colormap_file=colormap_file)
#-------------------------------------------------------------------------
# Display with mayavi2:
#-------------------------------------------------------------------------
elif program == 'mayavi2':
cmd = ["mayavi2", "-d", file_to_plot, "-m", "Surface", "&"]
execute(cmd, 'os')
def antsApplyTransformsToPoints(points, transform_files, inverse_booleans=[0]):
"""
Run ANTs antsApplyTransformsToPoints function to transform points.
(Creates pre- and post-transformed .csv points files for ANTs.)
Parameters
----------
points : list of lists of three integers
point coordinate data
transform_files : list
transform file names
inverse_booleans : list
for each transform, one to apply inverse of transform (otherwise zero)
Returns
-------
transformed_points : list of lists of three integers
transformed point coordinate data
Examples
--------
>>> from mindboggle.utils.ants import antsApplyTransformsToPoints
>>> from mindboggle.utils.io_vtk import read_vtk
>>> transform_files = ['/Users/arno/Data/antsCorticalThickness/Twins-2-1/antsTemplateToSubject1GenericAffine.mat','/Users/arno/Data/antsCorticalThickness/Twins-2-1/antsTemplateToSubject0Warp.nii.gz','/Users/arno/Data/antsCorticalThickness/Twins-2-1/antsSubjectToTemplate0GenericAffine.mat','/Users/arno/Data/antsCorticalThickness/Twins-2-1/antsSubjectToTemplate1Warp.nii.gz']
>>> transform_files = [transform_files[0],transform_files[1],'/Users/arno/Data/mindboggle_cache/f36e3d5d99f7c4a9bb70e2494ed7340b/OASIS-30_Atropos_template_to_MNI152_affine.txt']
>>> vtk_file = '/Users/arno/mindboggle_working/Twins-2-1/Mindboggle/_hemi_lh/Surface_to_vtk/lh.pial.vtk'
>>> faces, lines, indices, points, npoints, scalars, name, foo1 = read_vtk(vtk_file)
>>> inverse_booleans = [0,0,1]
>>> transformed_points = antsApplyTransformsToPoints(points, transform_files, inverse_booleans)
"""
import os
from mindboggle.utils.utils import execute
#-------------------------------------------------------------------------
# Write points (x,y,z,1) to a .csv file:
#-------------------------------------------------------------------------
points_file = os.path.join(os.getcwd(), 'points.csv')
fid = open(points_file, 'wa')
fid.write('x,y,z,t\n')
for point in points:
string_of_zeros = (4 - len(point)) * ',0'
fid.write(','.join([str(x) for x in point]) + string_of_zeros + '\n')
fid.close()
#-------------------------------------------------------------------------
# Apply transforms to points in .csv file:
#-------------------------------------------------------------------------
transformed_points_file = os.path.join(os.getcwd(),
'transformed_points.csv')
transform_string = ''
for ixfm, transform_file in enumerate(transform_files):
transform_string += " --t [{0},{1}]".\
format(transform_file, str(inverse_booleans[ixfm]))
cmd = [
'antsApplyTransformsToPoints', '-d', '3', '-i', points_file, '-o',
transformed_points_file, transform_string
]
execute(cmd, 'os')
if not os.path.exists(transformed_points_file):
str1 = "antsApplyTransformsToPoints did not create "
raise (IOError(str1 + transformed_points_file + "."))
#-------------------------------------------------------------------------
# Return transformed points:
#-------------------------------------------------------------------------
fid = open(transformed_points_file, 'r')
lines = fid.readlines()
fid.close()
transformed_points = []
for iline, line in enumerate(lines):
if iline > 0:
point_xyz1 = [float(x) for x in line.split(',')]
transformed_points.append(point_xyz1[0:3])
return transformed_points