def propagate_fundus_lines(surf_file, fundus_lines_file, thickness_file):
"""Propagate fundus lines to tile the surface.
Parameters
----------
surf_file: file containing the surface geometry in vtk format
fundus_lines_file: file containing scalars representing fundus lines
thickness_file: file containing cortical thickness scalar data
(for masking out the medial wall only)
Returns
-------
scalars indicating whether each vertex is part of the closed
fundus lines or not
"""
from mindboggle.mio.vtks import read_vtk, read_scalars
faces, _, _, points, num_points, fundus_lines, _, _ = read_vtk(
surf_file, return_first=True, return_array=True)
fundus_lines, _ = read_scalars(fundus_lines_file)
fundus_line_indices = [i for i, x in enumerate(fundus_lines) if x > 0.5]
thickness, _ = read_scalars(thickness_file,
return_first=True, return_array=True)
return propagate_fundus_lines(
points, faces, fundus_line_indices, thickness)
def propagate_fundus_lines(surf_file, fundus_lines_file, thickness_file):
"""Propagate fundus lines to tile the surface.
Parameters
----------
surf_file: file containing the surface geometry in vtk format
fundus_lines_file: file containing scalars representing fundus lines
thickness_file: file containing cortical thickness scalar data
(for masking out the medial wall only)
Returns
-------
scalars indicating whether each vertex is part of the closed
fundus lines or not
"""
from mindboggle.mio.vtks import read_vtk, read_scalars
points, indices, lines, faces, fundus_lines, scalar_names, num_points, \
input_vtk = read_vtk(surf_file, return_first=True, return_array=True)
fundus_lines, _ = read_scalars(fundus_lines_file)
fundus_line_indices = [i for i, x in enumerate(fundus_lines) if x > 0.5]
thickness, _ = read_scalars(thickness_file,
return_first=True, return_array=True)
return propagate_fundus_lines(
points, faces, fundus_line_indices, thickness)
def histogram_of_vtk_scalars(vtk_file, nbins=100):
"""
Plot histogram of VTK surface mesh scalar values.
Parameters
----------
vtk_file : string
name of VTK file with scalar values to plot
nbins : integer
number of histogram bins
Examples
--------
>>> import os
>>> from mindboggle.mio.plots import histogram_of_vtk_scalars
>>> from mindboggle.mio.fetch_data import prep_tests
>>> urls, fetch_data = prep_tests()
>>> vtk_file = fetch_data(urls['left_mean_curvature'], '', '.vtk')
>>> os.rename(vtk_file, vtk_file + '.nii.gz')
>>> vtk_file = vtk_file + '.nii.gz'
>>> histogram_of_vtk_scalars(vtk_file, nbins=500) # doctest: +SKIP
"""
import matplotlib.pyplot as plt
from mindboggle.mio.vtks import read_scalars
# Load values:
values, name = read_scalars(vtk_file)
# Histogram:
fig = plt.figure()
ax = fig.add_subplot(1,1,1)
ax.hist(values, nbins, normed=False, facecolor='gray', alpha=0.5)
plt.show()
def histogram_of_vtk_scalars(vtk_file, nbins=100):
"""
Plot histogram of VTK surface mesh scalar values.
Parameters
----------
vtk_file : string
name of VTK file with scalar values to plot
nbins : integer
number of histogram bins
Examples
--------
>>> import os
>>> from mindboggle.mio.plots import histogram_of_vtk_scalars
>>> path = os.environ['MINDBOGGLE_DATA']
>>> vtk_file = os.path.join(path, 'arno', 'shapes', 'lh.pial.mean_curvature.vtk')
>>> histogram_of_vtk_scalars(vtk_file, nbins=500)
"""
import matplotlib.pyplot as plt
from mindboggle.mio.vtks import read_scalars
# Load values:
values, name = read_scalars(vtk_file)
# Histogram:
fig = plt.figure()
ax = fig.add_subplot(1,1,1)
ax.hist(values, nbins, normed=False, facecolor='gray', alpha=0.5)
plt.show()
def histogram_of_vtk_scalars(vtk_file, nbins=100):
"""
Plot histogram of VTK surface mesh scalar values.
Parameters
----------
vtk_file : string
name of VTK file with scalar values to plot
nbins : integer
number of histogram bins
Examples
--------
>>> import os
>>> from mindboggle.mio.plots import histogram_of_vtk_scalars
>>> from mindboggle.mio.fetch_data import prep_tests
>>> urls, fetch_data = prep_tests()
>>> vtk_file = fetch_data(urls['left_mean_curvature'], '', '.vtk')
>>> os.rename(vtk_file, vtk_file + '.nii.gz')
>>> vtk_file = vtk_file + '.nii.gz'
>>> histogram_of_vtk_scalars(vtk_file, nbins=500) # doctest: +SKIP
"""
import matplotlib.pyplot as plt
from mindboggle.mio.vtks import read_scalars
# Load values:
values, name = read_scalars(vtk_file)
# Histogram:
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
ax.hist(values, bins=nbins, density=False, facecolor='gray', alpha=0.5)
plt.show()
def spectrum_from_file(vtk_file, spectrum_size=10, exclude_labels=[-1],
normalization=None, area_file='', verbose=False):
"""
Compute Laplace-Beltrami spectrum of a 3D shape in a VTK file.
Parameters
----------
vtk_file : string
the input vtk file
spectrum_size : integer
number of eigenvalues to be computed (the length of the spectrum)
exclude_labels : list of integers
labels to be excluded
normalization : string
the method used to normalize eigenvalues ('area' or None)
if "area", use area of the 2D structure as in Reuter et al. 2006
area_file : string
name of VTK file with surface area scalar values
verbose : bool
print statements?
Returns
-------
spectrum : list of floats
first spectrum_size of Laplace-Beltrami spectrum
Examples
--------
>>> # Spectrum for entire left hemisphere of Twins-2-1:
>>> import numpy as np
>>> from mindboggle.shapes.laplace_beltrami import spectrum_from_file
>>> from mindboggle.shapes.laplace_beltrami import spectrum_per_label
>>> from mindboggle.mio.fetch_data import prep_tests
>>> urls, fetch_data = prep_tests()
>>> vtk_file = fetch_data(urls['left_freesurfer_labels'], '', '.vtk')
>>> spectrum = spectrum_from_file(vtk_file, spectrum_size=6)
>>> print(np.array_str(np.array(spectrum[1::]),
... precision=5, suppress_small=True))
[ 0.00013 0.00027 0.00032 0.00047 0.00058]
"""
from mindboggle.mio.vtks import read_vtk, read_scalars
from mindboggle.shapes.laplace_beltrami import spectrum_of_largest
points, indices, lines, faces, scalars, scalar_names, npoints, \
input_vtk = read_vtk(vtk_file)
# Area file:
if area_file:
areas, u1 = read_scalars(area_file)
else:
areas = None
spectrum = spectrum_of_largest(points, faces, spectrum_size,
exclude_labels, normalization, areas,
verbose)
return spectrum
def get_freesurfer_curvatures(mean_curv_file, max_curv_file, min_curv_file,
curvatures_file):
"""
Gets FreeSurfer curvatures from VTK files to a CSV file.
Args:
mean_curv_file (str): input mean curvature files path
max_curv_file (str): input maximum curvature files path
min_curv_file (str): input minimum curvature files path
curvatures_file (str): output CSV file with the curvature values
Returns:
None
"""
# Get the curvatures from VTK files
# [[x1, y1, z1], [x2, y2, z2], ...]]
points = np.array(read_points(mean_curv_file))
xyz = points.T # transposed: [[x1, x2, ...], [y1, y2, ...], [z1, z2, ...]]
mean_curv, scalar_name = read_scalars(mean_curv_file,
return_first=True,
return_array=True)
try:
assert (xyz.shape[1] == mean_curv.size)
except AssertionError:
print(
"number of points={} is not equal to number of scalars={}".format(
xyz.shape[1], mean_curv.size))
max_curv, _ = read_scalars(max_curv_file,
return_first=True,
return_array=True)
min_curv, _ = read_scalars(min_curv_file,
return_first=True,
return_array=True)
# Write the curvatures to a CSV file
df = pd.DataFrame()
df['x'] = xyz[0]
df['y'] = xyz[1]
df['z'] = xyz[2]
df['mean_curvature'] = mean_curv
df['kappa1'] = max_curv
df['kappa2'] = min_curv
df.to_csv(curvatures_file, sep=';')
def spectrum_from_file(vtk_file, spectrum_size=10, exclude_labels=[-1],
normalization="areaindex", area_file='', verbose=False):
"""
Compute Laplace-Beltrami spectrum of a 3D shape in a VTK file.
Parameters
----------
vtk_file : string
the input vtk file
spectrum_size : integer
number of eigenvalues to be computed (the length of the spectrum)
exclude_labels : list of integers
labels to be excluded
normalization : string
the method used to normalize eigenvalues
if None, no normalization is used
if "area", use area of the 2D structure as in Reuter et al. 2006
if "index", divide eigenvalue by index to account for linear trend
if "areaindex", do both (default)
area_file : string
name of VTK file with surface area scalar values
verbose : bool
print statements?
Returns
-------
spectrum : list of floats
first spectrum_size of Laplace-Beltrami spectrum
Examples
--------
>>> # Spectrum for entire left hemisphere of Twins-2-1:
>>> import numpy as np
>>> from mindboggle.shapes.laplace_beltrami import spectrum_from_file
>>> from mindboggle.shapes.laplace_beltrami import spectrum_per_label
>>> from mindboggle.mio.fetch_data import prep_tests
>>> urls, fetch_data = prep_tests()
>>> vtk_file = fetch_data(urls['left_freesurfer_labels'], '', '.vtk')
>>> spectrum = spectrum_from_file(vtk_file, spectrum_size=6,
... exclude_labels=[-1], normalization=None, area_file="", verbose=False)
>>> [np.float("{0:.{1}f}".format(x, 5)) for x in spectrum[1::]]
[0.00013, 0.00027, 0.00032, 0.00047, 0.00058]
>>> spectrum = spectrum_from_file(vtk_file, spectrum_size=6,
... exclude_labels=[-1], normalization="areaindex", area_file="",
... verbose=False)
>>> [np.float("{0:.{1}f}".format(x, 5)) for x in spectrum[1::]]
[14.12801, 14.93573, 11.75397, 12.93141, 12.69348]
"""
from mindboggle.mio.vtks import read_vtk, read_scalars
from mindboggle.shapes.laplace_beltrami import spectrum_of_largest
points, indices, lines, faces, scalars, scalar_names, npoints, \
input_vtk = read_vtk(vtk_file)
# Area file:
if area_file:
areas, u1 = read_scalars(area_file)
else:
areas = None
spectrum = spectrum_of_largest(points, faces, spectrum_size,
exclude_labels, normalization, areas,
verbose)
return spectrum
def rescale_by_neighborhood(input_vtk,
indices=[],
nedges=10,
p=99,
set_max_to_1=True,
save_file=False,
output_filestring='rescaled_scalars',
background_value=-1):
"""
Rescale the scalar values of a VTK file by a percentile value
in each vertex's surface mesh neighborhood.
Parameters
----------
input_vtk : string
name of VTK file with a scalar value for each vertex
indices : list of integers (optional)
indices of scalars to normalize
nedges : integer
number or edges from vertex, defining the size of its neighborhood
p : float in range of [0,100]
percentile used to normalize each scalar
set_max_to_1 : Boolean
set all rescaled values greater than 1 to 1.0?
save_file : Boolean
save output VTK file?
output_filestring : string (if save_file)
name of output file
background_value : integer
background value
Returns
-------
rescaled_scalars : list of floats
rescaled scalar values
rescaled_scalars_file : string (if save_file)
name of output VTK file with rescaled scalar values
Examples
--------
>>> import os
>>> from mindboggle.guts.mesh import rescale_by_neighborhood
>>> from mindboggle.mio.vtks import read_scalars, rewrite_scalars
>>> from mindboggle.mio.plots import plot_surfaces
>>> path = os.environ['MINDBOGGLE_DATA']
>>> input_vtk = os.path.join(path, 'arno', 'shapes', 'lh.pial.travel_depth.vtk')
>>> indices = []
>>> nedges = 10
>>> p = 99
>>> set_max_to_1 = True
>>> save_file = True
>>> output_filestring = 'rescaled_scalars'
>>> background_value = -1
>>> #
>>> rescaled_scalars, rescaled_scalars_file = rescale_by_neighborhood(input_vtk,
>>> indices, nedges, p, set_max_to_1, save_file, output_filestring, background_value)
>>> #
>>> # View rescaled scalar values per fold:
>>> folds_file = os.path.join(path, 'arno', 'features', 'folds.vtk')
>>> folds, name = read_scalars(folds_file)
>>> #
>>> rewrite_scalars(rescaled_scalars_file, rescaled_scalars_file,
>>> rescaled_scalars, 'rescaled_depths', folds)
>>> plot_surfaces(rescaled_scalars_file)
"""
import os
import numpy as np
from mindboggle.mio.vtks import read_scalars, rewrite_scalars
from mindboggle.guts.mesh import find_neighbors_from_file, find_neighborhood
# Load scalars and vertex neighbor lists:
scalars, name = read_scalars(input_vtk, True, True)
if not indices:
indices = [i for i, x in enumerate(scalars) if x != background_value]
print(" Rescaling {0} scalar values by neighborhood...".format(
len(indices)))
neighbor_lists = find_neighbors_from_file(input_vtk)
# Loop through vertices:
rescaled_scalars = scalars.copy()
for index in indices:
# Determine the scalars in the vertex's neighborhood:
neighborhood = find_neighborhood(neighbor_lists, [index], nedges)
# Compute a high neighborhood percentile to normalize vertex's value:
normalization_factor = np.percentile(scalars[neighborhood], p)
rescaled_scalar = scalars[index] / normalization_factor
rescaled_scalars[index] = rescaled_scalar
# Make any rescaled value greater than 1 equal to 1:
if set_max_to_1:
rescaled_scalars[[x for x in indices if rescaled_scalars[x] > 1.0]] = 1
rescaled_scalars = rescaled_scalars.tolist()
#-------------------------------------------------------------------------
# Return rescaled scalars and file name
#-------------------------------------------------------------------------
if save_file:
rescaled_scalars_file = os.path.join(os.getcwd(),
output_filestring + '.vtk')
rewrite_scalars(input_vtk, rescaled_scalars_file, rescaled_scalars,
'rescaled_scalars')
if not os.path.exists(rescaled_scalars_file):
raise (IOError(rescaled_scalars_file + " not found"))
else:
rescaled_scalars_file = None
return rescaled_scalars, rescaled_scalars_file
def evaluate_deep_features(features_file,
labels_file,
sulci_file='',
hemi='',
excludeIDs=[-1],
output_vtk_name='',
verbose=True):
"""
Evaluate deep surface features by computing the minimum distance from each
label border vertex to all of the feature vertices in the same sulcus,
and from each feature vertex to all of the label border vertices in the
same sulcus. The label borders run along the deepest parts of sulci
and correspond to fundi in the DKT cortical labeling protocol.
Parameters
----------
features_file : string
VTK surface file with feature numbers for vertex scalars
labels_file : string
VTK surface file with label numbers for vertex scalars
sulci_file : string
VTK surface file with sulcus numbers for vertex scalars
excludeIDs : list of integers
feature/sulcus/label IDs to exclude (background set to -1)
output_vtk_name : Boolean
if not empty, output a VTK file beginning with output_vtk_name that
contains a surface with mean distances as scalars
verbose : Boolean
print mean distances to standard output?
Returns
-------
feature_to_border_mean_distances : numpy array [number of features x 1]
mean distance from each feature to sulcus label border
feature_to_border_sd_distances : numpy array [number of features x 1]
standard deviations of feature-to-border distances
feature_to_border_distances_vtk : string
VTK surface file containing feature-to-border distances
border_to_feature_mean_distances : numpy array [number of features x 1]
mean distances from each sulcus label border to feature
border_to_feature_sd_distances : numpy array [number of features x 1]
standard deviations of border-to-feature distances
border_to_feature_distances_vtk : string
VTK surface file containing border-to-feature distances
"""
import os
import sys
import numpy as np
from mindboggle.mio.vtks import read_vtk, read_scalars, write_vtk
from mindboggle.guts.mesh import find_neighbors, remove_faces
from mindboggle.guts.segment import extract_borders
from mindboggle.guts.compute import source_to_target_distances
from mindboggle.mio.labels import DKTprotocol
dkt = DKTprotocol()
#-------------------------------------------------------------------------
# Load labels, features, and sulci:
#-------------------------------------------------------------------------
faces, lines, indices, points, npoints, labels, scalar_names, \
input_vtk = read_vtk(labels_file, True, True)
features, name = read_scalars(features_file, True, True)
if sulci_file:
sulci, name = read_scalars(sulci_file, True, True)
# List of indices to sulcus vertices:
sulcus_indices = [i for i, x in enumerate(sulci) if x != -1]
segmentIDs = sulci
sulcus_faces = remove_faces(faces, sulcus_indices)
else:
sulcus_indices = range(len(labels))
segmentIDs = []
sulcus_faces = faces
#-------------------------------------------------------------------------
# Prepare neighbors, label pairs, border IDs, and outputs:
#-------------------------------------------------------------------------
# Calculate neighbor lists for all points:
print('Find neighbors for all vertices...')
neighbor_lists = find_neighbors(faces, npoints)
# Find label border points in any of the sulci:
print('Find label border points in any of the sulci...')
border_indices, border_label_tuples, unique_border_label_tuples = \
extract_borders(sulcus_indices, labels, neighbor_lists,
ignore_values=[], return_label_pairs=True)
if not len(border_indices):
sys.exit('There are no label border points!')
# Initialize an array of label border IDs
# (label border vertices that define sulci in the labeling protocol):
print('Build an array of label border IDs...')
label_borders = -1 * np.ones(npoints)
if hemi == 'lh':
nsulcus_lists = len(dkt.left_sulcus_label_pair_lists)
else:
nsulcus_lists = len(dkt.right_sulcus_label_pair_lists)
feature_to_border_mean_distances = -1 * np.ones(nsulcus_lists)
feature_to_border_sd_distances = -1 * np.ones(nsulcus_lists)
border_to_feature_mean_distances = -1 * np.ones(nsulcus_lists)
border_to_feature_sd_distances = -1 * np.ones(nsulcus_lists)
feature_to_border_distances_vtk = ''
border_to_feature_distances_vtk = ''
#-------------------------------------------------------------------------
# Loop through sulci:
#-------------------------------------------------------------------------
# For each list of sorted label pairs (corresponding to a sulcus):
for isulcus, label_pairs in enumerate(dkt.sulcus_label_pair_lists):
# Keep the border points with label pair labels:
label_pair_border_indices = [
x for i, x in enumerate(border_indices)
if np.unique(border_label_tuples[i]).tolist() in label_pairs
]
# Store the points as sulcus IDs in the border IDs array:
if label_pair_border_indices:
label_borders[label_pair_border_indices] = isulcus
if len(np.unique(label_borders)) > 1:
#---------------------------------------------------------------------
# Construct a feature-to-border distance matrix and VTK file:
#---------------------------------------------------------------------
# Construct a distance matrix:
print('Construct a feature-to-border distance matrix...')
sourceIDs = features
targetIDs = label_borders
distances, distance_matrix = source_to_target_distances(
sourceIDs, targetIDs, points, segmentIDs, excludeIDs)
# Compute mean distances for each feature:
nfeatures = min(np.shape(distance_matrix)[1], nsulcus_lists)
for ifeature in range(nfeatures):
feature_distances = [
x for x in distance_matrix[:, ifeature] if x != -1
]
feature_to_border_mean_distances[ifeature] = \
np.mean(feature_distances)
feature_to_border_sd_distances[ifeature] = \
np.std(feature_distances)
if verbose:
print('Feature-to-border mean distances:')
print(feature_to_border_mean_distances)
print('Feature-to-border standard deviations of distances:')
print(feature_to_border_sd_distances)
# Write resulting feature-label border distances to VTK file:
if output_vtk_name:
feature_to_border_distances_vtk = os.path.join(
os.getcwd(),
output_vtk_name + '_feature_to_border_mean_distances.vtk')
print('Write feature-to-border distances to {0}...'.format(
feature_to_border_distances_vtk))
write_vtk(feature_to_border_distances_vtk, points, [], [],
sulcus_faces, [distances],
['feature-to-border_distances'], 'float')
#---------------------------------------------------------------------
# Construct a border-to-feature distance matrix and VTK file:
#---------------------------------------------------------------------
# Construct a distance matrix:
print('Construct a border-to-feature distance matrix...')
sourceIDs = label_borders
targetIDs = features
distances, distance_matrix = source_to_target_distances(
sourceIDs, targetIDs, points, segmentIDs, excludeIDs)
# Compute mean distances for each feature:
nfeatures = min(np.shape(distance_matrix)[1], nsulcus_lists)
for ifeature in range(nfeatures):
border_distances = [
x for x in distance_matrix[:, ifeature] if x != -1
]
border_to_feature_mean_distances[ifeature] = \
np.mean(border_distances)
border_to_feature_sd_distances[ifeature] = \
np.std(border_distances)
if verbose:
print('border-to-feature mean distances:')
print(border_to_feature_mean_distances)
print('border-to-feature standard deviations of distances:')
print(border_to_feature_sd_distances)
# Write resulting feature-label border distances to VTK file:
if output_vtk_name:
border_to_feature_distances_vtk = os.path.join(
os.getcwd(),
output_vtk_name + '_border_to_feature_mean_distances.vtk')
print('Write border-to-feature distances to {0}...'.format(
border_to_feature_distances_vtk))
write_vtk(border_to_feature_distances_vtk, points, [], [],
sulcus_faces, [distances],
['border-to-feature_distances'], 'float')
#-------------------------------------------------------------------------
# Return outputs:
#-------------------------------------------------------------------------
return feature_to_border_mean_distances, feature_to_border_sd_distances,\
feature_to_border_distances_vtk,\
border_to_feature_mean_distances, border_to_feature_sd_distances,\
border_to_feature_distances_vtk
def write_face_vertex_averages(input_file, output_table='', area_file=''):
"""
Make table of average vertex values per face
(divided by face area if area_file provided).
Parameters
----------
input_file : string
name of VTK file with scalars to average
area_file : string
name of VTK file with surface area scalar values
output_table : string
output table filename
Returns
-------
output_table : string
output table filename
Examples
--------
>>> import os
>>> from mindboggle.mio.tables import write_face_vertex_averages
>>> path = '/homedir/mindboggled'
>>> input_file = os.path.join(path, 'Twins-2-1', 'shapes', 'left_cortical_surface', 'freesurfer_thickness.vtk')
>>> area_file = os.path.join(path, 'Twins-2-1', 'shapes', 'left_cortical_surface', 'area.vtk')
>>> output_table = ''
>>> #
>>> write_face_vertex_averages(input_file, output_table, area_file)
"""
import os
import numpy as np
import pandas as pd
from mindboggle.mio.vtks import read_vtk, read_scalars
faces, lines, indices, points, npoints, scalars, name, \
input_vtk = read_vtk(input_file, True, True)
if area_file:
area_scalars, name = read_scalars(area_file, True, True)
#---------------------------------------------------------------------
# For each face, average vertex values:
#---------------------------------------------------------------------
columns = []
for face in faces:
values = []
for index in face:
if area_file:
values.append(scalars[index] / area_scalars[index])
else:
values.append(scalars[index])
columns.append(np.mean(values))
#-----------------------------------------------------------------
# Write to table:
#-----------------------------------------------------------------
if not output_table:
output_table = os.path.join(os.getcwd(), 'average_face_values.csv')
df = pd.DataFrame({'': columns})
df.to_csv(output_table, index=False)
if not os.path.exists(output_table):
raise (IOError(output_table + " not found"))
return output_table
def write_shape_stats(labels_or_file=[],
sulci=[],
fundi=[],
affine_transform_files=[],
inverse_booleans=[],
transform_format='itk',
area_file='',
normalize_by_area=False,
mean_curvature_file='',
travel_depth_file='',
geodesic_depth_file='',
freesurfer_thickness_file='',
freesurfer_curvature_file='',
freesurfer_sulc_file='',
labels_spectra=[],
labels_spectra_IDs=[],
sulci_spectra=[],
sulci_spectra_IDs=[],
labels_zernike=[],
labels_zernike_IDs=[],
sulci_zernike=[],
sulci_zernike_IDs=[],
exclude_labels=[-1],
verbose=False):
"""
Make tables of shape statistics per label, sulcus, and/or fundus.
There can be thousands of vertices in a single feature such as a gyrus,
sulcus, or fundus, and for per-vertex shape measures, it makes sense to
characterize their collective shape as a distribution of shape values.
Mindboggle's stats_per_label function generates tables of summary
statistical measures for these distributions, and includes the shape
measures computed on cortical features as well.
Note ::
This function is tailored for Mindboggle outputs.
Parameters
----------
labels_or_file : list or string
label number for each vertex or name of VTK file with index scalars
sulci : list of integers
indices to sulci, one per vertex, with -1 indicating no sulcus
fundi : list of integers
indices to fundi, one per vertex, with -1 indicating no fundus
affine_transform_files : list of strings
affine transform files to standard space
inverse_booleans : list of of zeros and ones
for each transform, 1 to take the inverse, else 0
transform_format : string
format for transform file
Ex: 'txt' for text, 'itk' for ITK, and 'mat' for Matlab format
area_file : string
name of VTK file with surface area scalar values
normalize_by_area : bool
normalize all shape measures by area of label/feature? (UNTESTED)
mean_curvature_file : string
name of VTK file with mean curvature scalar values
travel_depth_file : string
name of VTK file with travel depth scalar values
geodesic_depth_file : string
name of VTK file with geodesic depth scalar values
freesurfer_thickness_file : string
name of VTK file with FreeSurfer thickness scalar values
freesurfer_curvature_file : string
name of VTK file with FreeSurfer curvature (curv) scalar values
freesurfer_sulc_file : string
name of VTK file with FreeSurfer convexity (sulc) scalar values
labels_spectra : list of lists of floats
Laplace-Beltrami spectra for each labeled region
labels_spectra_IDs : list of integers
unique labels for labels_spectra
sulci_spectra : list of lists of floats
Laplace-Beltrami spectra for each sulcus
sulci_spectra_IDs : list of integers
unique sulcus IDs for sulci_spectra
labels_zernike : list of lists of floats
Zernike moments for each labeled region
labels_zernike_IDs : list of integers
unique labels for labels_zernike
sulci_zernike : list of lists of floats
Zernike moments for each sulcus
sulci_zernike_IDs : list of integers
unique sulcus IDs for sulci_zernike
exclude_labels : list of lists of integers
indices to be excluded (in addition to -1)
verbose : bool
print statements?
Returns
-------
label_table : string
output table filename for label shapes
sulcus_table : string
output table filename for sulcus shapes
fundus_table : string
output table filename for fundus shapes
Examples
--------
>>> from mindboggle.mio.tables import write_shape_stats
>>> from mindboggle.mio.vtks import read_scalars
>>> from mindboggle.mio.fetch_data import prep_tests
>>> urls, fetch_data = prep_tests()
>>> label_file = fetch_data(urls['left_freesurfer_labels'], '', '.vtk')
>>> sulci_file = fetch_data(urls['left_sulci'], '', '.vtk')
>>> fundi_file = fetch_data(urls['left_fundus_per_sulcus'], '', '.vtk')
>>> mean_curvature_file = fetch_data(urls['left_mean_curvature'], '', '.vtk')
>>> travel_depth_file = fetch_data(urls['left_travel_depth'], '', '.vtk')
>>> geodesic_depth_file = fetch_data(urls['left_geodesic_depth'], '', '.vtk')
>>> area_file = fetch_data(urls['left_area'], '', '.vtk')
>>> freesurfer_thickness_file = ''
>>> freesurfer_curvature_file = ''
>>> freesurfer_sulc_file = ''
>>> sulci, name = read_scalars(sulci_file)
>>> fundi, name = read_scalars(fundi_file)
>>> affine_transform_files = []
>>> inverse_booleans = []
>>> transform_format = 'itk'
>>> normalize_by_area = False
>>> labels, name = read_scalars(label_file)
>>> labels_spectra = []
>>> labels_spectra_IDs = []
>>> sulci_spectra = []
>>> sulci_spectra_IDs = []
>>> labels_zernike = []
>>> labels_zernike_IDs = []
>>> sulci_zernike = []
>>> sulci_zernike_IDs = []
>>> exclude_labels = [-1]
>>> verbose = False
>>> label_table, sulcus_table, fundus_table = write_shape_stats(label_file,
... sulci, fundi, affine_transform_files, inverse_booleans,
... transform_format, area_file, normalize_by_area,
... mean_curvature_file, travel_depth_file, geodesic_depth_file,
... freesurfer_thickness_file, freesurfer_curvature_file,
... freesurfer_sulc_file, labels_spectra, labels_spectra_IDs,
... sulci_spectra, sulci_spectra_IDs, labels_zernike,
... labels_zernike_IDs, sulci_zernike, sulci_zernike_IDs,
... exclude_labels, verbose)
"""
import os
import numpy as np
import pandas as pd
from mindboggle.guts.compute import stats_per_label
from mindboggle.guts.compute import means_per_label
from mindboggle.guts.compute import sum_per_label
from mindboggle.mio.vtks import read_scalars, read_vtk
from mindboggle.mio.vtks import apply_affine_transforms
from mindboggle.mio.labels import DKTprotocol
dkt = DKTprotocol()
# Make sure inputs are lists:
if isinstance(labels_or_file, np.ndarray):
labels = [int(x) for x in labels_or_file]
elif isinstance(labels_or_file, list):
labels = labels_or_file
elif isinstance(labels_or_file, str):
labels, name = read_scalars(labels_or_file)
if isinstance(sulci, np.ndarray):
sulci = [int(x) for x in sulci]
if isinstance(fundi, np.ndarray):
fundi = [int(x) for x in fundi]
if not labels and not sulci and not fundi:
raise IOError('No feature data to tabulate in write_shape_stats().')
spectrum_start = 1 # Store all columns of spectral components (0),
# or start from higher frequency components (>=1)
# ------------------------------------------------------------------------
# Feature lists, shape names, and shape files:
# ------------------------------------------------------------------------
# Feature lists:
feature_lists = [labels, sulci, fundi]
feature_names = ['label', 'sulcus', 'fundus']
spectra_lists = [labels_spectra, sulci_spectra]
spectra_ID_lists = [labels_spectra_IDs, sulci_spectra_IDs]
zernike_lists = [labels_zernike, sulci_zernike]
zernike_ID_lists = [labels_zernike_IDs, sulci_zernike_IDs]
table_names = [
'label_shapes.csv', 'sulcus_shapes.csv', 'fundus_shapes.csv'
]
# Shape names corresponding to shape files below:
shape_names = [
'area', 'travel depth', 'geodesic depth', 'mean curvature',
'freesurfer curvature', 'freesurfer thickness',
'freesurfer convexity (sulc)'
]
# Load shape files as a list of numpy arrays of per-vertex shape values:
shape_files = [
area_file, travel_depth_file, geodesic_depth_file, mean_curvature_file,
freesurfer_curvature_file, freesurfer_thickness_file,
freesurfer_sulc_file
]
shape_arrays = []
first_pass = True
area_array = []
for ishape, shape_file in enumerate(shape_files):
if os.path.exists(shape_file):
if first_pass:
points, indices, lines, faces, scalars_array, scalar_names, \
npoints, input_vtk = read_vtk(shape_file, True, True)
points = np.array(points)
first_pass = False
if affine_transform_files and transform_format:
affine_points, \
foo1 = apply_affine_transforms(affine_transform_files,
inverse_booleans, transform_format,
points, vtk_file_stem='')
else:
scalars_array, name = read_scalars(shape_file, True, True)
if scalars_array.size:
shape_arrays.append(scalars_array)
# Store area array:
if ishape == 0:
area_array = scalars_array.copy()
if normalize_by_area:
use_area = area_array
else:
use_area = []
# Initialize table file names:
label_table = ''
sulcus_table = ''
fundus_table = ''
# Loop through features / tables:
for itable, feature_list in enumerate(feature_lists):
column_names = []
# ----------------------------------------------------------------
# Label names:
# ----------------------------------------------------------------
label_title = 'name'
if itable == 0:
label_numbers = dkt.cerebrum_cortex_DKT31_numbers
label_names = dkt.cerebrum_cortex_DKT31_names
elif itable in [1, 2]:
label_numbers = dkt.sulcus_numbers
label_names = dkt.sulcus_names
else:
label_numbers = []
label_names = []
include_labels = label_numbers
nlabels = len(label_numbers)
# --------------------------------------------------------------------
# For each feature, construct a table of average shape values:
# --------------------------------------------------------------------
if feature_list:
feature_name = feature_names[itable]
columns = []
# ----------------------------------------------------------------
# Loop through shape measures:
# ----------------------------------------------------------------
column_names.extend(column_names[:])
for ishape, shape_array in enumerate(shape_arrays):
shape = shape_names[ishape]
if verbose:
print(' Compute statistics on {0} {1}...'.format(
feature_name, shape))
# ------------------------------------------------------------
# Append feature areas to columns:
# ------------------------------------------------------------
if ishape == 0 and np.size(area_array):
sums, label_list = sum_per_label(shape_array, feature_list,
include_labels,
exclude_labels)
column_names.append(shape)
columns.append(sums)
# ------------------------------------------------------------
# Append feature shape statistics to columns:
# ------------------------------------------------------------
else:
medians, mads, means, sdevs, skews, kurts, \
lower_quarts, upper_quarts, \
label_list = stats_per_label(shape_array, feature_list,
include_labels, exclude_labels,
area_array, precision=1)
column_names.append(shape + ': median')
column_names.append(shape + ': MAD')
column_names.append(shape + ': mean')
column_names.append(shape + ': SD')
column_names.append(shape + ': skew')
column_names.append(shape + ': kurtosis')
column_names.append(shape + ': 25%')
column_names.append(shape + ': 75%')
columns.append(medians)
columns.append(mads)
columns.append(means)
columns.append(sdevs)
columns.append(skews)
columns.append(kurts)
columns.append(lower_quarts)
columns.append(upper_quarts)
# ----------------------------------------------------------------
# Mean positions in the original space:
# ----------------------------------------------------------------
# Compute mean position per feature:
positions, sdevs, label_list, foo = means_per_label(
points, feature_list, include_labels, exclude_labels, use_area)
# Append mean x,y,z position per feature to columns:
xyz_positions = np.asarray(positions)
for ixyz, xyz in enumerate(['x', 'y', 'z']):
column_names.append('mean position: {0}'.format(xyz))
columns.append(xyz_positions[:, ixyz].tolist())
# ----------------------------------------------------------------
# Mean positions in standard space:
# ----------------------------------------------------------------
if affine_transform_files and transform_format:
# Compute standard space mean position per feature:
standard_positions, sdevs, label_list, \
foo = means_per_label(affine_points,
feature_list, include_labels, exclude_labels, use_area)
# Append standard space x,y,z position per feature to columns:
xyz_std_positions = np.asarray(standard_positions)
for ixyz, xyz in enumerate(['x', 'y', 'z']):
column_names.append('mean position in standard space:'
' {0}'.format(xyz))
columns.append(xyz_std_positions[:, ixyz].tolist())
# ----------------------------------------------------------------
# Laplace-Beltrami spectra:
# ----------------------------------------------------------------
if itable in [0, 1]:
spectra = spectra_lists[itable]
if spectra:
spectra_IDs = spectra_ID_lists[itable]
# Construct a matrix of spectra:
len_spectrum = len(spectra[0])
spectrum_matrix = np.zeros((nlabels, len_spectrum))
for ilabel, label in enumerate(include_labels):
if label in spectra_IDs:
spectrum = spectra[spectra_IDs.index(label)]
spectrum_matrix[ilabel, 0:len_spectrum] = spectrum
# Append spectral shape name and values to columns:
for ispec in range(spectrum_start, len_spectrum):
columns.append(spectrum_matrix[:, ispec].tolist())
column_names.append('Laplace-Beltrami spectrum:'
' component {0}'.format(ispec + 1))
# ----------------------------------------------------------------
# Zernike moments:
# ----------------------------------------------------------------
if itable in [0, 1]:
zernike = zernike_lists[itable]
if zernike:
zernike_IDs = zernike_ID_lists[itable]
# Construct a matrix of Zernike moments:
len_moments = len(zernike[0])
moments_matrix = np.zeros((nlabels, len_moments))
for ilabel, label in enumerate(include_labels):
if label in zernike_IDs:
moments = zernike[zernike_IDs.index(label)]
moments_matrix[ilabel, 0:len_moments] = moments
# Append Zernike shape name and values to columns:
for imoment in range(0, len_moments):
columns.append(moments_matrix[:, imoment].tolist())
column_names.append(
'Zernike moments: component {0}'.format(imoment +
1))
# ----------------------------------------------------------------
# Write labels/IDs and values to table:
# ----------------------------------------------------------------
# Write labels/IDs to table:
output_table = os.path.join(os.getcwd(), table_names[itable])
if columns:
df1 = pd.DataFrame({'ID': label_numbers})
df2 = pd.DataFrame(np.transpose(columns), columns=column_names)
df = pd.concat([df1, df2], axis=1)
if label_names:
df0 = pd.DataFrame({'name': label_names})
df = pd.concat([df0, df], axis=1)
df.to_csv(output_table, index=False, encoding='utf-8')
if not os.path.exists(output_table):
raise IOError(output_table + " not found")
# ----------------------------------------------------------------
# Return correct table file name:
# ----------------------------------------------------------------
if itable == 0:
label_table = output_table
elif itable == 1:
sulcus_table = output_table
elif itable == 2:
fundus_table = output_table
return label_table, sulcus_table, fundus_table
def compute_likelihood(trained_file,
depth_file,
curvature_file,
folds,
save_file=False):
"""
Compute likelihoods based on input values, folds, and estimated parameters.
Compute likelihood values for a given VTK surface mesh file, after training
on distributions of depth and curvature values from multiple files.
Parameters
----------
trained_file : pickle compressed file
contains the following dictionaries containing lists of floats
(estimates of depth or curvature means, sigmas, and weights
trained on fold vertices either on or off sulcus label borders)
depth_border, curv_border, depth_nonborder, curv_nonborder
depth_file : string
VTK surface mesh file with depth values in [0,1] for all vertices
curvature_file : string
VTK surface mesh file with curvature values in [-1,1] for all vertices
folds : list of integers
fold number for all vertices (-1 for non-fold vertices)
save_file : Boolean
save output VTK file?
Returns
-------
likelihoods : list of floats
likelihood values for all vertices (0 for non-fold vertices)
likelihoods_file : string (if save_file)
name of output VTK file with likelihood scalars
(-1 for non-fold vertices)
Examples
--------
>>> import os
>>> from mindboggle.mio.vtks import read_scalars, rewrite_scalars
>>> from mindboggle.shapes.likelihood import compute_likelihood
>>> from mindboggle.mio.plots import plot_surfaces
>>> path = os.environ['MINDBOGGLE_DATA']
>>> trained_file = os.path.join(path, 'atlases', 'depth_curv_border_nonborder_parameters.pkl')
>>> #depth_file = os.path.join(path, 'arno', 'shapes', 'travel_depth_rescaled.vtk')
>>> depth_file = os.path.join(path, 'arno', 'shapes', 'lh.pial.travel_depth.vtk')
>>> curvature_file = os.path.join(path, 'arno', 'shapes', 'lh.pial.mean_curvature.vtk')
>>> folds_file = os.path.join(path, 'arno', 'features', 'folds.vtk')
>>> folds, name = read_scalars(folds_file)
>>> save_file = True
>>> #
>>> compute_likelihood(trained_file, depth_file, curvature_file, folds, save_file)
>>> # View:
>>> plot_surfaces('likelihoods.vtk', folds_file)
"""
import os
import numpy as np
from math import pi
import cPickle as pickle
from mindboggle.mio.vtks import read_scalars, rewrite_scalars
# Initialize variables:
tiny = 0.000000001
L = np.zeros(len(folds))
probs_border = np.zeros(len(folds))
probs_nonborder = np.zeros(len(folds))
# Load estimated depth and curvature distribution parameters:
depth_border, curv_border, depth_nonborder, curv_nonborder = pickle.load(
open(trained_file, "r"))
# Load depths, curvatures:
depths, name = read_scalars(depth_file, True, True)
curvatures, name = read_scalars(curvature_file, True, True)
# Prep for below:
n = 2
twopiexp = (2 * pi)**(n / 2)
border_sigmas = depth_border['sigmas'] * curv_border['sigmas']
nonborder_sigmas = depth_nonborder['sigmas'] * curv_nonborder['sigmas']
norm_border = 1 / (twopiexp * border_sigmas + tiny)
norm_nonborder = 1 / (twopiexp * nonborder_sigmas + tiny)
I = [i for i, x in enumerate(folds) if x != -1]
N = depth_border['sigmas'].shape[0]
for j in range(N):
# Border:
expB = depth_border['weights'][j] * \
((depths[I]-depth_border['means'][j])**2) / \
depth_border['sigmas'][j]**2
expB += curv_border['weights'][j] * \
((curvatures[I]-curv_border['means'][j])**2) / \
curv_border['sigmas'][j]**2
expB = -expB / 2
probs_border[I] = probs_border[I] + norm_border[j] * np.exp(expB)
# Non-border:
expNB = depth_nonborder['weights'][j] * \
((depths[I]-depth_nonborder['means'][j])**2) / \
depth_nonborder['sigmas'][j]**2
expNB += curv_nonborder['weights'][j] * \
((curvatures[I]-curv_nonborder['means'][j])**2) / \
curv_nonborder['sigmas'][j]**2
expNB = -expNB / 2
probs_nonborder[
I] = probs_nonborder[I] + norm_nonborder[j] * np.exp(expNB)
likelihoods = probs_border / (probs_nonborder + probs_border + tiny)
likelihoods = likelihoods.tolist()
#-------------------------------------------------------------------------
# Return likelihoods and output file name
#-------------------------------------------------------------------------
if save_file:
likelihoods_file = os.path.join(os.getcwd(), 'likelihoods.vtk')
rewrite_scalars(depth_file, likelihoods_file, likelihoods,
'likelihoods', likelihoods)
if not os.path.exists(likelihoods_file):
raise (IOError(likelihoods_file + " not found"))
else:
likelihoods_file = None
return likelihoods, likelihoods_file
def concatenate_sulcus_scalars(scalar_files, fold_files, label_files):
"""
Prepare data for estimating scalar distributions along and outside fundi.
Extract (e.g., depth, curvature) scalar values in folds, along sulcus
label boundaries as well as outside the sulcus label boundaries.
Concatenate these scalar values across multiple files.
Parameters
----------
scalar_files : list of strings
names of surface mesh VTK files with scalar values to concatenate
fold_files : list of strings (corr. to each list in scalar_files)
VTK files with fold numbers as scalars (-1 for non-fold vertices)
label_files : list of strings (corr. to fold_files)
VTK files with label numbers (-1 for unlabeled vertices)
Returns
-------
border_scalars : list of floats
concatenated scalar values within folds along sulcus label boundaries
nonborder_scalars : list of floats
concatenated scalar values within folds outside sulcus label boundaries
Examples
--------
>>> # Concatenate (duplicate) depth scalars:
>>> import os
>>> from mindboggle.shapes.likelihood import concatenate_sulcus_scalars
>>> path = os.environ['MINDBOGGLE_DATA']
>>> depth_file = os.path.join(path, 'arno', 'shapes', 'depth_rescaled.vtk')
>>> folds_file = os.path.join(path, 'arno', 'features', 'folds.vtk')
>>> labels_file = os.path.join(path, 'arno', 'labels', 'lh.labels.DKT25.manual.vtk')
>>> scalar_files = [depth_file, depth_file]
>>> fold_files = [folds_file, folds_file]
>>> label_files = [labels_file, labels_file]
>>> #
>>> S = concatenate_sulcus_scalars(scalar_files, fold_files, label_files)
"""
import numpy as np
from mindboggle.mio.vtks import read_scalars
from mindboggle.guts.mesh import find_neighbors_from_file
from mindboggle.guts.segment import extract_borders
from mindboggle.mio.labels import DKTprotocol
dkt = DKTprotocol()
# Prepare (non-unique) list of sulcus label pairs:
protocol_label_pairs = [x for lst in dkt.sulcus_label_pair_lists
for x in lst]
border_scalars = []
nonborder_scalars = []
# Loop through files with the scalar values:
for ifile, scalar_file in enumerate(scalar_files):
print(scalar_file)
# Load scalars, folds, and labels:
folds_file = fold_files[ifile]
labels_file = label_files[ifile]
scalars, name = read_scalars(scalar_file, True, True)
if scalars.shape:
folds, name = read_scalars(folds_file)
labels, name = read_scalars(labels_file)
indices_folds = [i for i,x in enumerate(folds) if x != -1]
neighbor_lists = find_neighbors_from_file(labels_file)
# Find all label border pairs within the folds:
indices_label_pairs, label_pairs, unique_pairs = extract_borders(
indices_folds, labels, neighbor_lists, ignore_values=[-1],
return_label_pairs=True)
indices_label_pairs = np.array(indices_label_pairs)
# Find vertices with label pairs in the sulcus labeling protocol:
Ipairs_in_protocol = [i for i,x in enumerate(label_pairs)
if x in protocol_label_pairs]
indices_label_pairs = indices_label_pairs[Ipairs_in_protocol]
indices_outside_pairs = list(frozenset(indices_folds).difference(
indices_label_pairs))
# Store scalar values in folds along label border pairs:
border_scalars.extend(scalars[indices_label_pairs].tolist())
# Store scalar values in folds outside label border pairs:
nonborder_scalars.extend(scalars[indices_outside_pairs].tolist())
return border_scalars, nonborder_scalars
def compute_likelihood(trained_file, depth_file, curvature_file, folds,
save_file=False, background_value=-1):
"""
Compute likelihoods based on input values, folds, estimated parameters.
Compute likelihood values for a given VTK surface mesh file, after
training on distributions of depth and curvature values from multiple
files.
Parameters
----------
trained_file : pickle compressed file
depth_border, curv_border, depth_nonborder, and curv_nonborder are
dictionaries containing lists of floats
(estimates of depth or curvature means, sigmas, and weights
trained on fold vertices either on or off sulcus label borders)
depth_file : string
VTK surface mesh file with depth values in [0,1] for all vertices
curvature_file : string
VTK surface mesh file with curvature values in [-1,1] for all vertices
folds : list of integers
fold number for all vertices (-1 for non-fold vertices)
save_file : bool
save output VTK file?
background_value : integer or float
background value
Returns
-------
likelihoods : list of floats
likelihood values for all vertices (0 for non-fold vertices)
likelihoods_file : string (if save_file)
name of output VTK file with likelihood scalars
(-1 for non-fold vertices)
Notes
-----
The depth_curv_border_nonborder_parameters.pkl file needs to be updated.
Examples
--------
>>> from mindboggle.mio.vtks import read_scalars
>>> from mindboggle.shapes.likelihood import compute_likelihood
>>> from mindboggle.mio.fetch_data import prep_tests
>>> urls, fetch_data = prep_tests()
>>> depth_file = fetch_data(urls['left_travel_depth'], '', '.vtk')
>>> curvature_file = fetch_data(urls['left_mean_curvature'], '', '.vtk')
>>> folds_file = fetch_data(urls['left_folds'], '', '.vtk')
>>> trained_file = fetch_data(urls[
... 'depth_curv_border_nonborder_parameters'], '', '.pkl') # doctest: +SKIP
>>> folds, name = read_scalars(folds_file)
>>> save_file = True
>>> background_value = -1
>>> likelihoods, likelihoods_file = compute_likelihood(trained_file,
... depth_file, curvature_file, folds, save_file, background_value) # doctest: +SKIP
View result (skip test):
>>> from mindboggle.mio.plots import plot_surfaces # doctest: +SKIP
>>> plot_surfaces('likelihoods.vtk', folds_file) # doctest: +SKIP
"""
import os
import numpy as np
from math import pi
import pickle
from io import open
from mindboggle.mio.vtks import read_scalars, rewrite_scalars
# Initialize variables:
tiny = 0.000000001
L = np.zeros(len(folds))
probs_border = np.zeros(len(folds))
probs_nonborder = np.zeros(len(folds))
# Load estimated depth and curvature distribution parameters:
depth_border, curv_border, depth_nonborder, \
curv_nonborder = pickle.load(open(trained_file, "rb"))
# Load depths, curvatures:
depths, name = read_scalars(depth_file, True, True)
curvatures, name = read_scalars(curvature_file, True, True)
# Prep for below:
n = 2
twopiexp = (2*pi)**(n/2)
border_sigmas = depth_border['sigmas'] * curv_border['sigmas']
nonborder_sigmas = depth_nonborder['sigmas'] * curv_nonborder['sigmas']
norm_border = 1 / (twopiexp * border_sigmas + tiny)
norm_nonborder = 1 / (twopiexp * nonborder_sigmas + tiny)
I = [i for i,x in enumerate(folds) if x != background_value]
N = depth_border['sigmas'].shape[0]
for j in range(N):
# Border:
expB = depth_border['weights'][j] * \
((depths[I]-depth_border['means'][j])**2) / \
depth_border['sigmas'][j]**2
expB += curv_border['weights'][j] * \
((curvatures[I]-curv_border['means'][j])**2) / \
curv_border['sigmas'][j]**2
expB = -expB / 2
probs_border[I] = probs_border[I] + norm_border[j] * np.exp(expB)
# Non-border:
expNB = depth_nonborder['weights'][j] * \
((depths[I]-depth_nonborder['means'][j])**2) / \
depth_nonborder['sigmas'][j]**2
expNB += curv_nonborder['weights'][j] * \
((curvatures[I]-curv_nonborder['means'][j])**2) / \
curv_nonborder['sigmas'][j]**2
expNB = -expNB / 2
probs_nonborder[I] = probs_nonborder[I] + \
norm_nonborder[j] * np.exp(expNB)
likelihoods = probs_border / (probs_nonborder + probs_border + tiny)
likelihoods = likelihoods.tolist()
# ------------------------------------------------------------------------
# Return likelihoods and output file name
# ------------------------------------------------------------------------
if save_file:
likelihoods_file = os.path.join(os.getcwd(), 'likelihoods.vtk')
rewrite_scalars(depth_file, likelihoods_file, likelihoods,
'likelihoods', likelihoods, background_value)
if not os.path.exists(likelihoods_file):
raise IOError(likelihoods_file + " not found")
else:
likelihoods_file = None
return likelihoods, likelihoods_file
verbose = True
curvatures_file = '{}_curvatures.csv'.format(stem)
# Run the method
default_mean_curv_file, _, _, _, _ = curvature(
command, method, arguments, surface_file, verbose)
# rename mean curvature output file
os.rename(default_mean_curv_file, mean_curv_file)
# remove unneeded output file
os.remove(os.path.join(os.getcwd(), 'output.nipype'))
# Get the curvatures from VTK files
# [[x1, y1, z1], [x2, y2, z2], ...]
points = np.array(read_points(mean_curv_file))
xyz = points.T # transposed: [[x1, x2, ...], [y1, y2, ...], [z1, z2, ...]]
mean_curv, _ = read_scalars(
mean_curv_file, return_first=True, return_array=True)
print('number of points: {}'.format(mean_curv.size))
if method == 0 or method == 1:
gauss_curv, _ = read_scalars(
gauss_curv_file, return_first=True, return_array=True)
if method == 0:
max_curv, _ = read_scalars(
max_curv_file, return_first=True, return_array=True)
min_curv, _ = read_scalars(
min_curv_file, return_first=True, return_array=True)
curvedness = np.sqrt((max_curv ** 2 + min_curv ** 2) / 2)
rewrite_scalars(surface_file, curvedness_file, curvedness, "curvedness")
# Write the curvatures to a CSV file
df = pd.DataFrame()
df['x'] = xyz[0]
def extract_sulci(
labels_file,
folds_or_file,
hemi,
min_boundary=1,
sulcus_names=[],
save_file=False,
output_file="",
background_value=-1,
verbose=False,
):
"""
Identify sulci from folds in a brain surface according to a labeling
protocol that includes a list of label pairs defining each sulcus.
Since folds are defined as deep, connected areas of a surface, and since
folds may be connected to each other in ways that differ across brains,
there usually does not exist a one-to-one mapping between folds of one
brain and those of another. To address the correspondence problem then,
we need to find just those portions of the folds that correspond across
brains. To accomplish this, Mindboggle segments folds into sulci, which
do have a one-to-one correspondence across non-pathological brains.
Mindboggle defines a sulcus as a folded portion of cortex whose opposing
banks are labeled with one or more sulcus label pairs in the DKT labeling
protocol, where each label pair is unique to one sulcus and represents
a boundary between two adjacent gyri, and each vertex has one gyrus label.
This function assigns vertices in a fold to a sulcus in one of two cases.
In the first case, vertices whose labels are in only one label pair in
the fold are assigned to the label pair’s sulcus if they are connected
through similarly labeled vertices to the boundary between the two labels.
In the second case, the segment_regions function propagates labels from
label borders to vertices whose labels are in multiple label pairs in the
fold.
Steps for each fold ::
1. Remove fold if it has fewer than two labels.
2. Remove fold if its labels do not contain a sulcus label pair.
3. Find vertices with labels that are in only one of the fold's
label boundary pairs. Assign the vertices the sulcus with the label
pair if they are connected to the label boundary for that pair.
4. If there are remaining vertices, segment into sets of vertices
connected to label boundaries, and assign a unique ID to each set.
Parameters
----------
labels_file : string
file name for surface mesh VTK containing labels for all vertices
folds_or_file : numpy array, list or string
fold number for each vertex / name of VTK file containing fold scalars
hemi : string
hemisphere abbreviation in {'lh', 'rh'} for sulcus labels
min_boundary : integer
minimum number of vertices for a sulcus label boundary segment
sulcus_names : list of strings
names of sulci
save_file : bool
save output VTK file?
output_file : string
name of output file in VTK format
background_value : integer or float
background value
verbose : bool
print statements?
Returns
-------
sulci : list of integers
sulcus numbers for all vertices (-1 for non-sulcus vertices)
n_sulci : integers
number of sulci
sulci_file : string
output VTK file with sulcus numbers (-1 for non-sulcus vertices)
Examples
--------
>>> # Example 1: Extract sulcus from a fold with one sulcus label pair:
>>> import numpy as np
>>> from mindboggle.features.sulci import extract_sulci
>>> from mindboggle.mio.vtks import read_scalars
>>> from mindboggle.mio.fetch_data import prep_tests
>>> urls, fetch_data = prep_tests()
>>> # Load labels, folds, neighbor lists, and sulcus names and label pairs
>>> labels_file = fetch_data(urls['left_freesurfer_labels'], '', '.vtk')
>>> folds_file = fetch_data(urls['left_folds'], '', '.vtk')
>>> folds_or_file, name = read_scalars(folds_file, True, True)
>>> save_file = True
>>> output_file = 'extract_sulci_fold4_1sulcus.vtk'
>>> background_value = -1
>>> # Limit number of folds to speed up the test:
>>> limit_folds = True
>>> if limit_folds:
... fold_numbers = [4] #[4, 6]
... i0 = [i for i,x in enumerate(folds_or_file) if x not in fold_numbers]
... folds_or_file[i0] = background_value
>>> hemi = 'lh'
>>> min_boundary = 10
>>> sulcus_names = []
>>> verbose = False
>>> sulci, n_sulci, sulci_file = extract_sulci(labels_file, folds_or_file,
... hemi, min_boundary, sulcus_names, save_file, output_file,
... background_value, verbose)
>>> n_sulci # 23 # (if not limit_folds)
1
>>> lens = [len([x for x in sulci if x==y])
... for y in np.unique(sulci) if y != -1]
>>> lens[0:10] # [6358, 3288, 7612, 5205, 4414, 6251, 3493, 2566, 4436, 739] # (if not limit_folds)
[1151]
View result without background (skip test):
>>> from mindboggle.mio.plots import plot_surfaces # doctest: +SKIP
>>> from mindboggle.mio.vtks import rewrite_scalars # doctest: +SKIP
>>> output = 'extract_sulci_fold4_1sulcus_no_background.vtk'
>>> rewrite_scalars(sulci_file, output, sulci,
... 'sulci', sulci) # doctest: +SKIP
>>> plot_surfaces(output) # doctest: +SKIP
Example 2: Extract sulcus from a fold with multiple sulcus label pairs:
>>> folds_or_file, name = read_scalars(folds_file, True, True)
>>> output_file = 'extract_sulci_fold7_2sulci.vtk'
>>> # Limit number of folds to speed up the test:
>>> limit_folds = True
>>> if limit_folds:
... fold_numbers = [7] #[4, 6]
... i0 = [i for i,x in enumerate(folds_or_file) if x not in fold_numbers]
... folds_or_file[i0] = background_value
>>> sulci, n_sulci, sulci_file = extract_sulci(labels_file, folds_or_file,
... hemi, min_boundary, sulcus_names, save_file, output_file,
... background_value, verbose)
>>> n_sulci # 23 # (if not limit_folds)
2
>>> lens = [len([x for x in sulci if x==y])
... for y in np.unique(sulci) if y != -1]
>>> lens[0:10] # [6358, 3288, 7612, 5205, 4414, 6251, 3493, 2566, 4436, 739] # (if not limit_folds)
[369, 93]
View result without background (skip test):
>>> from mindboggle.mio.plots import plot_surfaces # doctest: +SKIP
>>> from mindboggle.mio.vtks import rewrite_scalars # doctest: +SKIP
>>> output = 'extract_sulci_fold7_2sulci_no_background.vtk'
>>> rewrite_scalars(sulci_file, output, sulci,
... 'sulci', sulci) # doctest: +SKIP
>>> plot_surfaces(output) # doctest: +SKIP
"""
import os
from time import time
import numpy as np
from mindboggle.mio.vtks import read_scalars, read_vtk, rewrite_scalars
from mindboggle.guts.mesh import find_neighbors
from mindboggle.guts.segment import extract_borders, propagate, segment_regions
from mindboggle.mio.labels import DKTprotocol
# Load fold numbers if folds_or_file is a string:
if isinstance(folds_or_file, str):
folds, name = read_scalars(folds_or_file)
elif isinstance(folds_or_file, list):
folds = folds_or_file
elif isinstance(folds_or_file, np.ndarray):
folds = folds_or_file.tolist()
dkt = DKTprotocol()
if hemi == "lh":
pair_lists = dkt.left_sulcus_label_pair_lists
elif hemi == "rh":
pair_lists = dkt.right_sulcus_label_pair_lists
else:
raise IOError("Warning: hemisphere not properly specified ('lh' or 'rh').")
# Load points, faces, and neighbors:
points, indices, lines, faces, labels, scalar_names, npoints, input_vtk = read_vtk(labels_file)
neighbor_lists = find_neighbors(faces, npoints)
# Array of sulcus IDs for fold vertices, initialized as -1.
# Since we do not touch gyral vertices and vertices whose labels
# are not in the label list, or vertices having only one label,
# their sulcus IDs will remain -1:
sulci = background_value * np.ones(npoints)
# ------------------------------------------------------------------------
# Loop through folds
# ------------------------------------------------------------------------
fold_numbers = [int(x) for x in np.unique(folds) if x != background_value]
n_folds = len(fold_numbers)
if verbose:
print("Extract sulci from {0} folds...".format(n_folds))
t0 = time()
for n_fold in fold_numbers:
fold_indices = [i for i, x in enumerate(folds) if x == n_fold]
len_fold = len(fold_indices)
# List the labels in this fold:
fold_labels = [labels[x] for x in fold_indices]
unique_fold_labels = [int(x) for x in np.unique(fold_labels) if x != background_value]
# --------------------------------------------------------------------
# NO MATCH -- fold has fewer than two labels
# --------------------------------------------------------------------
if verbose and len(unique_fold_labels) < 2:
# Ignore: sulci already initialized with -1 values:
if not unique_fold_labels:
print(" Fold {0} ({1} vertices): " "NO MATCH -- fold has no labels".format(n_fold, len_fold))
else:
print(
" Fold {0} ({1} vertices): "
"NO MATCH -- fold has only one label ({2})".format(n_fold, len_fold, unique_fold_labels[0])
)
# Ignore: sulci already initialized with -1 values
else:
# Find all label boundary pairs within the fold:
indices_fold_pairs, fold_pairs, unique_fold_pairs = extract_borders(
fold_indices, labels, neighbor_lists, ignore_values=[], return_label_pairs=True
)
# Find fold label pairs in the protocol (pairs are already sorted):
fold_pairs_in_protocol = [x for x in unique_fold_pairs if x in dkt.unique_sulcus_label_pairs]
if verbose and unique_fold_labels:
print(
" Fold {0} labels: {1} ({2} vertices)".format(
n_fold, ", ".join([str(x) for x in unique_fold_labels]), len_fold
)
)
# ----------------------------------------------------------------
# NO MATCH -- fold has no sulcus label pair
# ----------------------------------------------------------------
if verbose and not fold_pairs_in_protocol:
print(" Fold {0}: NO MATCH -- fold has no sulcus label pair".format(n_fold, len_fold))
# ----------------------------------------------------------------
# Possible matches
# ----------------------------------------------------------------
else:
if verbose:
print(
" Fold {0} label pairs in protocol: {1}".format(
n_fold, ", ".join([str(x) for x in fold_pairs_in_protocol])
)
)
# Labels in the protocol (includes repeats across label pairs):
labels_in_pairs = [x for lst in fold_pairs_in_protocol for x in lst]
# Labels that appear in one or more sulcus label boundary:
unique_labels = []
nonunique_labels = []
for label in np.unique(labels_in_pairs):
if len([x for x in labels_in_pairs if x == label]) == 1:
unique_labels.append(label)
else:
nonunique_labels.append(label)
# ------------------------------------------------------------
# Vertices whose labels are in only one sulcus label pair
# ------------------------------------------------------------
# Find vertices with a label that is in only one of the fold's
# label pairs (the other label in the pair can exist in other
# pairs). Assign the vertices the sulcus with the label pair
# if they are connected to the label boundary for that pair.
# ------------------------------------------------------------
if unique_labels:
for pair in fold_pairs_in_protocol:
# If one or both labels in label pair is/are unique:
unique_labels_in_pair = [x for x in pair if x in unique_labels]
n_unique = len(unique_labels_in_pair)
if n_unique:
ID = None
for i, pair_list in enumerate(pair_lists):
if not isinstance(pair_list, list):
pair_list = [pair_list]
if pair in pair_list:
ID = i
break
if ID:
# Seeds from label boundary vertices
# (fold_pairs and pair already sorted):
indices_pair = [x for i, x in enumerate(indices_fold_pairs) if fold_pairs[i] == pair]
# Vertices with unique label(s) in pair:
indices_unique_labels = [
fold_indices[i] for i, x in enumerate(fold_labels) if x in unique_labels_in_pair
]
# dkt.unique_sulcus_label_pairs]
# Propagate sulcus ID from seeds to vertices
# with "unique" labels (only exist in one
# label pair in a fold); propagation ensures
# that sulci consist of contiguous vertices
# for each label boundary:
sulci2 = segment_regions(
indices_unique_labels,
neighbor_lists,
min_region_size=1,
seed_lists=[indices_pair],
keep_seeding=False,
spread_within_labels=True,
labels=labels,
label_lists=[],
values=[],
max_steps="",
background_value=background_value,
verbose=False,
)
sulci[sulci2 != background_value] = ID
# Print statement:
if verbose:
if n_unique == 1:
ps1 = "One label"
else:
ps1 = "Both labels"
if len(sulcus_names):
ps2 = sulcus_names[ID]
else:
ps2 = ""
print(
" {0} unique to one fold pair: "
"{1} {2}".format(ps1, ps2, unique_labels_in_pair)
)
# ------------------------------------------------------------
# Vertex labels shared by multiple label pairs
# ------------------------------------------------------------
# Propagate labels from label borders to vertices with labels
# that are shared by multiple label pairs in the fold.
# ------------------------------------------------------------
if len(nonunique_labels):
# For each label shared by different label pairs:
for label in nonunique_labels:
# Print statement:
if verbose:
print(" Propagate sulcus borders with label {0}".format(int(label)))
# Construct seeds from label boundary vertices:
seeds = background_value * np.ones(npoints)
for ID, pair_list in enumerate(pair_lists):
if not isinstance(pair_list, list):
pair_list = [pair_list]
label_pairs = [x for x in pair_list if label in x]
for label_pair in label_pairs:
indices_pair = [
x
for i, x in enumerate(indices_fold_pairs)
if np.sort(fold_pairs[i]).tolist() == label_pair
]
if indices_pair:
# Do not include short boundary segments:
if min_boundary > 1:
indices_pair2 = []
seeds2 = segment_regions(
indices_pair,
neighbor_lists,
1,
[],
False,
False,
[],
[],
[],
"",
background_value,
verbose,
)
useeds2 = [x for x in np.unique(seeds2) if x != background_value]
for seed2 in useeds2:
iseed2 = [i for i, x in enumerate(seeds2) if x == seed2]
if len(iseed2) >= min_boundary:
indices_pair2.extend(iseed2)
elif verbose:
if len(iseed2) == 1:
print(
" Remove "
"assignment "
"of ID {0} from "
"1 vertex".format(seed2)
)
else:
print(
" Remove "
"assignment "
"of ID {0} from "
"{1} vertices".format(seed2, len(iseed2))
)
indices_pair = indices_pair2
# Assign sulcus IDs to seeds:
seeds[indices_pair] = ID
# Identify vertices with the label:
indices_label = [fold_indices[i] for i, x in enumerate(fold_labels) if x == label]
if len(indices_label):
# Propagate sulcus ID from seeds to vertices
# with a given shared label:
seg_vs_prop = False
if seg_vs_prop:
indices_seeds = []
for seed in [x for x in np.unique(seeds) if x != background_value]:
indices_seeds.append([i for i, x in enumerate(seeds) if x == seed])
sulci2 = segment_regions(
indices_label,
neighbor_lists,
50,
indices_seeds,
False,
True,
labels,
[],
[],
"",
background_value,
verbose,
)
else:
label_array = background_value * np.ones(npoints)
label_array[indices_label] = 1
sulci2 = propagate(
points,
faces,
label_array,
seeds,
sulci,
max_iters=10000,
tol=0.001,
sigma=5,
background_value=background_value,
verbose=verbose,
)
sulci[sulci2 != background_value] = sulci2[sulci2 != background_value]
sulcus_numbers = [int(x) for x in np.unique(sulci) if x != background_value]
n_sulci = len(sulcus_numbers)
# ------------------------------------------------------------------------
# Print statements
# ------------------------------------------------------------------------
if verbose:
if n_sulci == 1:
sulcus_str = "sulcus"
else:
sulcus_str = "sulci"
if n_folds == 1:
folds_str = "fold"
else:
folds_str = "folds"
print("Extracted {0} {1} from {2} {3} ({4:.1f}s):".format(n_sulci, sulcus_str, n_folds, folds_str, time() - t0))
if sulcus_names:
for sulcus_number in sulcus_numbers:
print(" {0}: {1}".format(sulcus_number, sulcus_names[sulcus_number]))
elif sulcus_numbers:
print(" " + ", ".join([str(x) for x in sulcus_numbers]))
unresolved = [i for i in range(len(pair_lists)) if i not in sulcus_numbers]
if len(unresolved) == 1:
print("The following sulcus is unaccounted for:")
else:
print("The following {0} sulci are unaccounted for:".format(len(unresolved)))
if sulcus_names:
for sulcus_number in unresolved:
print(" {0}: {1}".format(sulcus_number, sulcus_names[sulcus_number]))
else:
print(" " + ", ".join([str(x) for x in unresolved]))
# ------------------------------------------------------------------------
# Return sulci, number of sulci, and file name
# ------------------------------------------------------------------------
sulci = [int(x) for x in sulci]
sulci_file = os.path.join(os.getcwd(), "sulci.vtk")
rewrite_scalars(labels_file, sulci_file, sulci, "sulci", [], background_value)
if not os.path.exists(sulci_file):
raise IOError(sulci_file + " not found")
return sulci, n_sulci, sulci_file
def realign_boundaries_to_fundus_lines(
surf_file, init_label_file, fundus_lines_file, thickness_file,
out_label_file=None):
"""
Fix label boundaries to fundus lines.
Parameters
----------
surf_file : file containing the surface geometry in vtk format
init_label_file : file containing scalars that represent the
initial guess at labels
fundus_lines_file : file containing scalars representing fundus lines.
thickness_file: file containing cortical thickness scalar data
(for masking out the medial wall only)
out_label_file : if specified, the realigned labels will be writen to
this file
Returns
-------
numpy array representing the realigned label for each surface vertex.
"""
import numpy as np
from mindboggle.guts.segment import extract_borders
import mindboggle.guts.graph as go
from mindboggle.mio.vtks import read_vtk, read_scalars, write_vtk
from mindboggle.guts.mesh import find_neighbors
import propagate_fundus_lines
## read files
faces, _, indices, points, num_points, _, _, _ = read_vtk(
surf_file, return_first=True, return_array=True)
indices = range(num_points)
init_labels, _ = read_scalars(init_label_file,
return_first=True, return_array=True)
fundus_lines, _ = read_scalars(fundus_lines_file,
return_first=True, return_array=True)
thickness, _ = read_scalars(thickness_file,
return_first=True, return_array=True)
# remove labels from vertices with zero thickness (get around
# DKT40 annotations having the label '3' for all the Corpus
# Callosum vertices).
cc_inds = [x for x in indices if thickness[x] < 0.001]
init_labels[cc_inds] = 0
## setup seeds from initial label boundaries
neighbor_lists = find_neighbors(faces, num_points)
# extract all vertices that are on a boundary between labels
boundary_indices, label_pairs, _ = extract_borders(
indices, init_labels, neighbor_lists,
return_label_pairs=True)
# split boundary vertices into segments with common boundary pairs.
boundary_segments = {}
for boundary_index, label_pair in zip(boundary_indices, label_pairs):
key = ((label_pair[0], label_pair[1]) if label_pair[0] < label_pair[1]
else (label_pair[1], label_pair[0]))
if key not in boundary_segments:
boundary_segments[key] = []
boundary_segments[key].append(boundary_index)
boundary_matrix, boundary_matrix_keys = _build_boundary_matrix(
boundary_segments, num_points)
# build the affinity matrix
affinity_matrix = go.weight_graph(
np.array(points), indices, np.array(faces), sigma=10, add_to_graph=False)
## propagate boundaries to fundus line vertices
learned_matrix = _propagate_labels(
affinity_matrix, boundary_matrix, boundary_indices, 100, 1)
# assign labels to fundus line vertices based on highest probability
new_boundaries = -1 * np.ones(init_labels.shape)
fundus_line_indices = [i for i, x in enumerate(fundus_lines) if x > 0.5]
# tile the surface into connected components delimited by fundus lines
closed_fundus_lines, _, _ = propagate_fundus_lines.propagate_fundus_lines(
points, faces, fundus_line_indices, thickness)
closed_fundus_line_indices = np.where(closed_fundus_lines > 0)[0]
# split surface into connected components
connected_component_faces = _remove_boundary_faces(
points, faces, closed_fundus_line_indices)
# label components based on most probable label assignment
new_labels = _label_components(
connected_component_faces, num_points, boundary_indices, learned_matrix,
boundary_matrix_keys)
# propagate new labels to fill holes
label_matrix, label_map = _build_label_matrix(new_labels)
new_learned_matrix = _propagate_labels(
affinity_matrix, label_matrix,
[i for i in range(num_points) if new_labels[i] >= 0], 100, 1)
# assign most probable labels
for idx in [i for i in range(num_points) if new_labels[i] == -1]:
max_idx = np.argmax(new_learned_matrix[idx])
new_labels[idx] = label_map[max_idx]
# save
if out_label_file is not None:
write_vtk(out_label_file, points, faces=faces,
scalars=[int(x) for x in new_labels], scalar_type='int')
return new_labels
def spectrum_from_file(vtk_file,
spectrum_size=10,
exclude_labels=[-1],
normalization=None,
area_file='',
verbose=False):
"""
Compute Laplace-Beltrami spectrum of a 3D shape in a VTK file.
Parameters
----------
vtk_file : string
the input vtk file
spectrum_size : integer
number of eigenvalues to be computed (the length of the spectrum)
exclude_labels : list of integers
labels to be excluded
normalization : string
the method used to normalize eigenvalues ('area' or None)
if "area", use area of the 2D structure as in Reuter et al. 2006
area_file : string
name of VTK file with surface area scalar values
verbose : bool
print statements?
Returns
-------
spectrum : list of floats
first spectrum_size of Laplace-Beltrami spectrum
Examples
--------
>>> # Spectrum for entire left hemisphere of Twins-2-1:
>>> import numpy as np
>>> from mindboggle.shapes.laplace_beltrami import spectrum_from_file
>>> from mindboggle.shapes.laplace_beltrami import spectrum_per_label
>>> from mindboggle.mio.fetch_data import prep_tests
>>> urls, fetch_data = prep_tests()
>>> vtk_file = fetch_data(urls['left_freesurfer_labels'], '', '.vtk')
>>> spectrum = spectrum_from_file(vtk_file, spectrum_size=6)
>>> print(np.array_str(np.array(spectrum[1::]),
... precision=5, suppress_small=True))
[ 0.00013 0.00027 0.00032 0.00047 0.00058]
"""
from mindboggle.mio.vtks import read_vtk, read_scalars
from mindboggle.shapes.laplace_beltrami import spectrum_of_largest
points, indices, lines, faces, scalars, scalar_names, npoints, \
input_vtk = read_vtk(vtk_file)
# Area file:
if area_file:
areas, u1 = read_scalars(area_file)
else:
areas = None
spectrum = spectrum_of_largest(points, faces, spectrum_size,
exclude_labels, normalization, areas,
verbose)
return spectrum
def spectrum_per_label(vtk_file,
spectrum_size=10,
exclude_labels=[-1],
normalization='area',
area_file='',
largest_segment=True):
"""
Compute Laplace-Beltrami spectrum per labeled region in a file.
Parameters
----------
vtk_file : string
name of VTK surface mesh file containing index scalars (labels)
spectrum_size : integer
number of eigenvalues to be computed (the length of the spectrum)
exclude_labels : list of integers
labels to be excluded
normalization : string
the method used to normalize eigenvalues ('area' or None)
if "area", use area of the 2D structure as in Reuter et al. 2006
area_file : string (optional)
name of VTK file with surface area scalar values
largest_segment : Boolean
compute spectrum only for largest segment with a given label?
Returns
-------
spectrum_lists : list of lists
first eigenvalues for each label's Laplace-Beltrami spectrum
label_list : list of integers
list of unique labels for which spectra are obtained
Examples
--------
>>> # Uncomment "if label==22:" below to run example:
>>> # Spectrum for Twins-2-1 left postcentral (22) pial surface:
>>> import os
>>> from mindboggle.shapes.laplace_beltrami import spectrum_per_label
>>> path = os.environ['MINDBOGGLE_DATA']
>>> vtk_file = os.path.join(path, 'arno', 'labels', 'lh.labels.DKT31.manual.vtk')
>>> area_file = os.path.join(path, 'arno', 'shapes', 'lh.pial.area.vtk')
>>> spectrum_size = 6
>>> exclude_labels = [0] #[-1]
>>> largest_segment = True
>>> spectrum_per_label(vtk_file, spectrum_size, exclude_labels, None,
>>> area_file, largest_segment)
([[6.3469513010430304e-18,
0.0005178862383467463,
0.0017434911095630772,
0.003667561767487686,
0.005429017880363784,
0.006309346984678924]],
[22])
"""
from mindboggle.mio.vtks import read_vtk, read_scalars
from mindboggle.guts.mesh import remove_faces, reindex_faces_points
from mindboggle.shapes.laplace_beltrami import fem_laplacian,\
spectrum_of_largest
# Read VTK surface mesh file:
faces, u1, u2, points, u4, labels, u5, u6 = read_vtk(vtk_file)
# Area file:
if area_file:
areas, u1 = read_scalars(area_file)
else:
areas = None
# Loop through labeled regions:
ulabels = []
[
ulabels.append(int(x)) for x in labels if x not in ulabels
if x not in exclude_labels
]
label_list = []
spectrum_lists = []
for label in ulabels:
#if label == 22:
# print("DEBUG: COMPUTE FOR ONLY ONE LABEL")
# Determine the indices per label:
Ilabel = [i for i, x in enumerate(labels) if x == label]
print('{0} vertices for label {1}'.format(len(Ilabel), label))
# Remove background faces:
pick_faces = remove_faces(faces, Ilabel)
pick_faces, pick_points, o1 = reindex_faces_points(pick_faces, points)
# Compute Laplace-Beltrami spectrum for the label:
if largest_segment:
exclude_labels_inner = [-1]
spectrum = spectrum_of_largest(pick_points, pick_faces,
spectrum_size, exclude_labels_inner,
normalization, areas)
else:
spectrum = fem_laplacian(pick_points, pick_faces, spectrum_size,
normalization)
# Append to a list of lists of spectra:
spectrum_lists.append(spectrum)
label_list.append(label)
return spectrum_lists, label_list
def spectrum_per_label(vtk_file, spectrum_size=10, exclude_labels=[-1],
normalization='area', area_file='',
largest_segment=True, verbose=False):
"""
Compute Laplace-Beltrami spectrum per labeled region in a file.
Parameters
----------
vtk_file : string
name of VTK surface mesh file containing index scalars (labels)
spectrum_size : integer
number of eigenvalues to be computed (the length of the spectrum)
exclude_labels : list of integers
labels to be excluded
normalization : string
the method used to normalize eigenvalues ('area' or None)
if "area", use area of the 2D structure as in Reuter et al. 2006
area_file : string (optional)
name of VTK file with surface area scalar values
largest_segment : bool
compute spectrum only for largest segment with a given label?
verbose : bool
print statements?
Returns
-------
spectrum_lists : list of lists
first eigenvalues for each label's Laplace-Beltrami spectrum
label_list : list of integers
list of unique labels for which spectra are obtained
Examples
--------
>>> # Uncomment "if label==22:" below to run example:
>>> # Spectrum for Twins-2-1 left postcentral (22) pial surface:
>>> import numpy as np
>>> from mindboggle.shapes.laplace_beltrami import spectrum_per_label
>>> from mindboggle.mio.fetch_data import prep_tests
>>> urls, fetch_data = prep_tests()
>>> vtk_file = fetch_data(urls['left_freesurfer_labels'], '', '.vtk')
>>> area_file = fetch_data(urls['left_area'], '', '.vtk')
>>> spectrum_size = 6
>>> exclude_labels = [0] #[-1]
>>> largest_segment = True
>>> verbose = False
>>> spectrum_lists, label_list = spectrum_per_label(vtk_file,
... spectrum_size, exclude_labels, None, area_file, largest_segment,
... verbose)
>>> print(np.array_str(np.array(spectrum_lists[0][1::]),
... precision=5, suppress_small=True))
[ 0.00054 0.00244 0.00291 0.00456 0.00575]
>>> label_list[0:10]
[1029, 1005, 1011, 1021, 1008, 1025, 999, 1013, 1007, 1022]
"""
from mindboggle.mio.vtks import read_vtk, read_scalars
from mindboggle.guts.mesh import keep_faces, reindex_faces_points
from mindboggle.shapes.laplace_beltrami import fem_laplacian,\
spectrum_of_largest
# Read VTK surface mesh file:
points, indices, lines, faces, labels, scalar_names, npoints, \
input_vtk = read_vtk(vtk_file)
# Area file:
if area_file:
areas, u1 = read_scalars(area_file)
else:
areas = None
# Loop through labeled regions:
ulabels = []
[ulabels.append(int(x)) for x in labels if x not in ulabels
if x not in exclude_labels]
label_list = []
spectrum_lists = []
for label in ulabels:
#if label == 22:
# print("DEBUG: COMPUTE FOR ONLY ONE LABEL")
# Determine the indices per label:
Ilabel = [i for i,x in enumerate(labels) if x == label]
if verbose:
print('{0} vertices for label {1}'.format(len(Ilabel), label))
# Remove background faces:
pick_faces = keep_faces(faces, Ilabel)
pick_faces, pick_points, o1 = reindex_faces_points(pick_faces, points)
# Compute Laplace-Beltrami spectrum for the label:
if largest_segment:
exclude_labels_inner = [-1]
spectrum = spectrum_of_largest(pick_points, pick_faces,
spectrum_size,
exclude_labels_inner,
normalization, areas, verbose)
else:
spectrum = fem_laplacian(pick_points, pick_faces, spectrum_size,
normalization, verbose)
# Append to a list of lists of spectra:
spectrum_lists.append(spectrum)
label_list.append(label)
return spectrum_lists, label_list
def compute_likelihood(trained_file, depth_file, curvature_file, folds,
save_file=False):
"""
Compute likelihoods based on input values, folds, and estimated parameters.
Compute likelihood values for a given VTK surface mesh file, after training
on distributions of depth and curvature values from multiple files.
Parameters
----------
trained_file : pickle compressed file
contains the following dictionaries containing lists of floats
(estimates of depth or curvature means, sigmas, and weights
trained on fold vertices either on or off sulcus label borders)
depth_border, curv_border, depth_nonborder, curv_nonborder
depth_file : string
VTK surface mesh file with depth values in [0,1] for all vertices
curvature_file : string
VTK surface mesh file with curvature values in [-1,1] for all vertices
folds : list of integers
fold number for all vertices (-1 for non-fold vertices)
save_file : Boolean
save output VTK file?
Returns
-------
likelihoods : list of floats
likelihood values for all vertices (0 for non-fold vertices)
likelihoods_file : string (if save_file)
name of output VTK file with likelihood scalars
(-1 for non-fold vertices)
Examples
--------
>>> import os
>>> from mindboggle.mio.vtks import read_scalars, rewrite_scalars
>>> from mindboggle.shapes.likelihood import compute_likelihood
>>> from mindboggle.mio.plots import plot_surfaces
>>> path = os.environ['MINDBOGGLE_DATA']
>>> trained_file = os.path.join(path, 'atlases', 'depth_curv_border_nonborder_parameters.pkl')
>>> #depth_file = os.path.join(path, 'arno', 'shapes', 'travel_depth_rescaled.vtk')
>>> depth_file = os.path.join(path, 'arno', 'shapes', 'lh.pial.travel_depth.vtk')
>>> curvature_file = os.path.join(path, 'arno', 'shapes', 'lh.pial.mean_curvature.vtk')
>>> folds_file = os.path.join(path, 'arno', 'features', 'folds.vtk')
>>> folds, name = read_scalars(folds_file)
>>> save_file = True
>>> #
>>> compute_likelihood(trained_file, depth_file, curvature_file, folds, save_file)
>>> # View:
>>> plot_surfaces('likelihoods.vtk', folds_file)
"""
import os
import numpy as np
from math import pi
import cPickle as pickle
from mindboggle.mio.vtks import read_scalars, rewrite_scalars
# Initialize variables:
tiny = 0.000000001
L = np.zeros(len(folds))
probs_border = np.zeros(len(folds))
probs_nonborder = np.zeros(len(folds))
# Load estimated depth and curvature distribution parameters:
depth_border, curv_border, depth_nonborder, curv_nonborder = pickle.load(
open(trained_file, "r"))
# Load depths, curvatures:
depths, name = read_scalars(depth_file, True, True)
curvatures, name = read_scalars(curvature_file, True, True)
# Prep for below:
n = 2
twopiexp = (2*pi)**(n/2)
border_sigmas = depth_border['sigmas'] * curv_border['sigmas']
nonborder_sigmas = depth_nonborder['sigmas'] * curv_nonborder['sigmas']
norm_border = 1 / (twopiexp * border_sigmas + tiny)
norm_nonborder = 1 / (twopiexp * nonborder_sigmas + tiny)
I = [i for i,x in enumerate(folds) if x != -1]
N = depth_border['sigmas'].shape[0]
for j in range(N):
# Border:
expB = depth_border['weights'][j] * \
((depths[I]-depth_border['means'][j])**2) / \
depth_border['sigmas'][j]**2
expB += curv_border['weights'][j] * \
((curvatures[I]-curv_border['means'][j])**2) / \
curv_border['sigmas'][j]**2
expB = -expB / 2
probs_border[I] = probs_border[I] + norm_border[j] * np.exp(expB)
# Non-border:
expNB = depth_nonborder['weights'][j] * \
((depths[I]-depth_nonborder['means'][j])**2) / \
depth_nonborder['sigmas'][j]**2
expNB += curv_nonborder['weights'][j] * \
((curvatures[I]-curv_nonborder['means'][j])**2) / \
curv_nonborder['sigmas'][j]**2
expNB = -expNB / 2
probs_nonborder[I] = probs_nonborder[I] + norm_nonborder[j] * np.exp(expNB)
likelihoods = probs_border / (probs_nonborder + probs_border + tiny)
likelihoods = likelihoods.tolist()
#-------------------------------------------------------------------------
# Return likelihoods and output file name
#-------------------------------------------------------------------------
if save_file:
likelihoods_file = os.path.join(os.getcwd(), 'likelihoods.vtk')
rewrite_scalars(depth_file, likelihoods_file, likelihoods,
'likelihoods', likelihoods)
if not os.path.exists(likelihoods_file):
raise(IOError(likelihoods_file + " not found"))
else:
likelihoods_file = None
return likelihoods, likelihoods_file
def spectrum_per_label(vtk_file, spectrum_size=10, exclude_labels=[-1],
normalization='area', area_file='',
largest_segment=True):
"""
Compute Laplace-Beltrami spectrum per labeled region in a file.
Parameters
----------
vtk_file : string
name of VTK surface mesh file containing index scalars (labels)
spectrum_size : integer
number of eigenvalues to be computed (the length of the spectrum)
exclude_labels : list of integers
labels to be excluded
normalization : string
the method used to normalize eigenvalues ('area' or None)
if "area", use area of the 2D structure as in Reuter et al. 2006
area_file : string (optional)
name of VTK file with surface area scalar values
largest_segment : Boolean
compute spectrum only for largest segment with a given label?
Returns
-------
spectrum_lists : list of lists
first eigenvalues for each label's Laplace-Beltrami spectrum
label_list : list of integers
list of unique labels for which spectra are obtained
Examples
--------
>>> # Uncomment "if label==22:" below to run example:
>>> # Spectrum for Twins-2-1 left postcentral (22) pial surface:
>>> import os
>>> from mindboggle.shapes.laplace_beltrami import spectrum_per_label
>>> path = os.environ['MINDBOGGLE_DATA']
>>> vtk_file = os.path.join(path, 'arno', 'labels', 'lh.labels.DKT31.manual.vtk')
>>> area_file = os.path.join(path, 'arno', 'shapes', 'lh.pial.area.vtk')
>>> spectrum_size = 6
>>> exclude_labels = [0] #[-1]
>>> largest_segment = True
>>> spectrum_per_label(vtk_file, spectrum_size, exclude_labels, None,
>>> area_file, largest_segment)
([[6.3469513010430304e-18,
0.0005178862383467463,
0.0017434911095630772,
0.003667561767487686,
0.005429017880363784,
0.006309346984678924]],
[22])
"""
from mindboggle.mio.vtks import read_vtk, read_scalars
from mindboggle.guts.mesh import remove_faces, reindex_faces_points
from mindboggle.shapes.laplace_beltrami import fem_laplacian,\
spectrum_of_largest
# Read VTK surface mesh file:
faces, u1, u2, points, u4, labels, u5, u6 = read_vtk(vtk_file)
# Area file:
if area_file:
areas, u1 = read_scalars(area_file)
else:
areas = None
# Loop through labeled regions:
ulabels = []
[ulabels.append(int(x)) for x in labels if x not in ulabels
if x not in exclude_labels]
label_list = []
spectrum_lists = []
for label in ulabels:
#if label == 22:
# print("DEBUG: COMPUTE FOR ONLY ONE LABEL")
# Determine the indices per label:
Ilabel = [i for i,x in enumerate(labels) if x == label]
print('{0} vertices for label {1}'.format(len(Ilabel), label))
# Remove background faces:
pick_faces = remove_faces(faces, Ilabel)
pick_faces, pick_points, o1 = reindex_faces_points(pick_faces, points)
# Compute Laplace-Beltrami spectrum for the label:
if largest_segment:
exclude_labels_inner = [-1]
spectrum = spectrum_of_largest(pick_points, pick_faces,
spectrum_size,
exclude_labels_inner,
normalization, areas)
else:
spectrum = fem_laplacian(pick_points, pick_faces,
spectrum_size, normalization)
# Append to a list of lists of spectra:
spectrum_lists.append(spectrum)
label_list.append(label)
return spectrum_lists, label_list
def extract_fundi(folds, curv_file, depth_file, min_separation=10,
erode_ratio=0.1, erode_min_size=1, save_file=False):
"""
Extract fundi from folds.
A fundus is a branching curve that runs along the deepest and most
highly curved portions of a fold.
Steps ::
1. Find fundus endpoints (outer anchors) with find_outer_anchors().
2. Include inner anchor points.
3. Connect anchor points using connect_points_erosion();
inner anchors are removed if they result in endpoints.
Parameters
----------
folds : numpy array or list of integers
fold number for each vertex
curv_file : string
surface mesh file in VTK format with mean curvature values
depth_file : string
surface mesh file in VTK format with rescaled depth values
likelihoods : list of integers
fundus likelihood value for each vertex
min_separation : integer
minimum number of edges between inner/outer anchor points
erode_ratio : float
fraction of indices to test for removal at each iteration
in connect_points_erosion()
save_file : Boolean
save output VTK file?
Returns
-------
fundus_per_fold : list of integers
fundus numbers for all vertices, labeled by fold
(-1 for non-fundus vertices)
n_fundi_in_folds : integer
number of fundi
fundus_per_fold_file : string (if save_file)
output VTK file with fundus numbers (-1 for non-fundus vertices)
Examples
--------
>>> # Extract fundus from one or more folds:
>>> single_fold = True
>>> import os
>>> from mindboggle.mio.vtks import read_scalars
>>> from mindboggle.features.fundi import extract_fundi
>>> from mindboggle.mio.plots import plot_surfaces
>>> path = os.environ['MINDBOGGLE_DATA']
>>> curv_file = os.path.join(path, 'arno', 'shapes', 'lh.pial.mean_curvature.vtk')
>>> depth_file = os.path.join(path, 'arno', 'shapes', 'travel_depth_rescaled.vtk')
>>> folds_file = os.path.join(path, 'arno', 'features', 'folds.vtk')
>>> folds, name = read_scalars(folds_file, True, True)
>>> if single_fold:
>>> fold_number = 2 #11
>>> folds[folds != fold_number] = -1
>>> min_separation = 10
>>> erode_ratio = 0.10
>>> erode_min_size = 10
>>> save_file = True
>>> o1, o2, fundus_per_fold_file = extract_fundi(folds, curv_file,
... depth_file, min_separation, erode_ratio, erode_min_size, save_file)
>>> #
>>> # View:
>>> plot_surfaces(fundi_file)
"""
# Extract a skeleton to connect endpoints in a fold:
import os
import numpy as np
from time import time
from mindboggle.mio.vtks import read_scalars, read_vtk, rewrite_scalars
from mindboggle.guts.compute import median_abs_dev
from mindboggle.guts.paths import find_max_values
from mindboggle.guts.mesh import find_neighbors_from_file, find_complete_faces
from mindboggle.guts.paths import find_outer_anchors, connect_points_erosion
if isinstance(folds, list):
folds = np.array(folds)
# Load values, inner anchor threshold, and neighbors:
points, indices, lines, faces, curvs, scalar_names, npoints, \
input_vtk = read_vtk(curv_file, True, True)
depths, name = read_scalars(depth_file, True, True)
values = curvs * depths
values0 = [x for x in values if x > 0]
thr = np.median(values0) + 2 * median_abs_dev(values0)
neighbor_lists = find_neighbors_from_file(curv_file)
#-------------------------------------------------------------------------
# Loop through folds:
#-------------------------------------------------------------------------
t1 = time()
skeletons = []
unique_fold_IDs = [x for x in np.unique(folds) if x != -1]
if len(unique_fold_IDs) == 1:
print("Extract a fundus from 1 fold...")
else:
print("Extract a fundus from each of {0} folds...".
format(len(unique_fold_IDs)))
for fold_ID in unique_fold_IDs:
indices_fold = [i for i,x in enumerate(folds) if x == fold_ID]
if indices_fold:
print(' Fold {0}:'.format(int(fold_ID)))
#-----------------------------------------------------------------
# Find outer anchor points on the boundary of the surface region,
# to serve as fundus endpoints:
#-----------------------------------------------------------------
outer_anchors, tracks = find_outer_anchors(indices_fold,
neighbor_lists, values, depths, min_separation)
#-----------------------------------------------------------------
# Find inner anchor points:
#-----------------------------------------------------------------
inner_anchors = find_max_values(points, values, min_separation, thr)
#-----------------------------------------------------------------
# Connect anchor points to create skeleton:
#-----------------------------------------------------------------
B = -1 * np.ones(npoints)
B[indices_fold] = 1
skeleton = connect_points_erosion(B, neighbor_lists,
outer_anchors, inner_anchors, values,
erode_ratio, erode_min_size, save_steps=[], save_vtk='')
if skeleton:
skeletons.extend(skeleton)
#-----------------------------------------------------------------
# Remove fundus vertices if they complete triangle faces:
#-----------------------------------------------------------------
Iremove = find_complete_faces(skeletons, faces)
if Iremove:
skeletons = list(frozenset(skeletons).difference(Iremove))
indices_skel = [x for x in skeletons if folds[x] != -1]
fundus_per_fold = -1 * np.ones(npoints)
fundus_per_fold[indices_skel] = folds[indices_skel]
n_fundi_in_folds = len([x for x in np.unique(fundus_per_fold)
if x != -1])
if n_fundi_in_folds == 1:
sdum = 'fold fundus'
else:
sdum = 'fold fundi'
print(' ...Extracted {0} {1}; {2} total ({3:.2f} seconds)'.
format(n_fundi_in_folds, sdum, n_fundi_in_folds, time() - t1))
#-------------------------------------------------------------------------
# Return fundi, number of fundi, and file name:
#-------------------------------------------------------------------------
if n_fundi_in_folds > 0:
fundus_per_fold = [int(x) for x in fundus_per_fold]
if save_file:
fundus_per_fold_file = os.path.join(os.getcwd(),
'fundus_per_fold.vtk')
rewrite_scalars(curv_file, fundus_per_fold_file, fundus_per_fold,
'fundi', folds)
if not os.path.exists(fundus_per_fold_file):
raise(IOError(fundus_per_fold_file + " not found"))
else:
fundus_per_fold_file = None
return fundus_per_fold, n_fundi_in_folds, fundus_per_fold_file
def spectrum_per_label(vtk_file,
spectrum_size=10,
exclude_labels=[-1],
normalization='areaindex',
area_file='',
largest_segment=True,
verbose=False):
"""
Compute Laplace-Beltrami spectrum per labeled region in a file.
Parameters
----------
vtk_file : string
name of VTK surface mesh file containing index scalars (labels)
spectrum_size : integer
number of eigenvalues to be computed (the length of the spectrum)
exclude_labels : list of integers
labels to be excluded
normalization : string
the method used to normalize eigenvalues
if None, no normalization is used
if "area", use area of the 2D structure as in Reuter et al. 2006
if "index", divide eigenvalue by index to account for linear trend
if "areaindex", do both (default)
area_file : string (optional)
name of VTK file with surface area scalar values
largest_segment : bool
compute spectrum only for largest segment with a given label?
verbose : bool
print statements?
Returns
-------
spectrum_lists : list of lists
first eigenvalues for each label's Laplace-Beltrami spectrum
label_list : list of integers
list of unique labels for which spectra are obtained
Examples
--------
>>> # Uncomment "if label==22:" below to run example:
>>> # Spectrum for Twins-2-1 left postcentral (22) pial surface:
>>> import numpy as np
>>> from mindboggle.shapes.laplace_beltrami import spectrum_per_label
>>> from mindboggle.mio.fetch_data import prep_tests
>>> urls, fetch_data = prep_tests()
>>> vtk_file = fetch_data(urls['left_freesurfer_labels'], '', '.vtk')
>>> area_file = fetch_data(urls['left_area'], '', '.vtk')
>>> spectrum_size = 6
>>> exclude_labels = [0] #[-1]
>>> largest_segment = True
>>> verbose = False
>>> spectrum_lists, label_list = spectrum_per_label(vtk_file,
... spectrum_size, exclude_labels, None, area_file, largest_segment,
... verbose)
>>> [np.float("{0:.{1}f}".format(x, 5)) for x in spectrum_lists[0]]
[0.0, 0.00054, 0.00244, 0.00291, 0.00456, 0.00575]
>>> label_list[0:10]
[1029, 1005, 1011, 1021, 1008, 1025, 999, 1013, 1007, 1022]
"""
from mindboggle.mio.vtks import read_vtk, read_scalars
from mindboggle.guts.mesh import keep_faces, reindex_faces_points
from mindboggle.shapes.laplace_beltrami import fem_laplacian,\
spectrum_of_largest
# Read VTK surface mesh file:
points, indices, lines, faces, labels, scalar_names, npoints, \
input_vtk = read_vtk(vtk_file)
# Area file:
if area_file:
areas, u1 = read_scalars(area_file)
else:
areas = None
# Loop through labeled regions:
ulabels = []
[
ulabels.append(int(x)) for x in labels if x not in ulabels
if x not in exclude_labels
]
label_list = []
spectrum_lists = []
for label in ulabels:
#if label == 22:
# print("DEBUG: COMPUTE FOR ONLY ONE LABEL")
# Determine the indices per label:
Ilabel = [i for i, x in enumerate(labels) if x == label]
if verbose:
print('{0} vertices for label {1}'.format(len(Ilabel), label))
# Remove background faces:
pick_faces = keep_faces(faces, Ilabel)
pick_faces, pick_points, o1 = reindex_faces_points(pick_faces, points)
# Compute Laplace-Beltrami spectrum for the label:
if largest_segment:
exclude_labels_inner = [-1]
spectrum = spectrum_of_largest(pick_points, pick_faces,
spectrum_size, exclude_labels_inner,
normalization, areas, verbose)
else:
spectrum = fem_laplacian(pick_points, pick_faces, spectrum_size,
normalization, verbose)
# Append to a list of lists of spectra:
spectrum_lists.append(spectrum)
label_list.append(label)
return spectrum_lists, label_list
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.mio.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/surface_cpp_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.guts.mesh import remove_faces, reindex_faces_points
from mindboggle.guts.utilities import execute
from mindboggle.mio.plots import plot_surfaces
from mindboggle.mio.vtks 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:
#-----------------------------------------------------------------
points, indices, lines, faces, scalars, scalar_names, npoints, \
input_vtk = 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 spectrum_from_file(vtk_file,
spectrum_size=10,
exclude_labels=[-1],
normalization=None,
area_file=''):
"""
Compute Laplace-Beltrami spectrum of a 3D shape in a VTK file.
Parameters
----------
vtk_file : string
the input vtk file
spectrum_size : integer
number of eigenvalues to be computed (the length of the spectrum)
exclude_labels : list of integers
labels to be excluded
normalization : string
the method used to normalize eigenvalues ('area' or None)
if "area", use area of the 2D structure as in Reuter et al. 2006
area_file : string
name of VTK file with surface area scalar values
Returns
-------
spectrum : list of floats
first spectrum_size of Laplace-Beltrami spectrum
Examples
--------
>>> # Spectrum for entire left hemisphere of Twins-2-1:
>>> import os
>>> from mindboggle.shapes.laplace_beltrami import spectrum_from_file
>>> path = os.environ['MINDBOGGLE_DATA']
>>> vtk_file = os.path.join(path, 'arno', 'labels', 'lh.labels.DKT25.manual.vtk')
>>> spectrum_from_file(vtk_file, spectrum_size=6)
[4.829758648026223e-18,
0.00012841730024671977,
0.0002715181572272744,
0.00032051508471594173,
0.000470162807048644,
0.0005768904023010327]
>>> # Spectrum for Twins-2-1 left postcentral pial surface (22)
>>> # (after running explode_scalars() with reindex=True):
>>> import os
>>> from mindboggle.shapes.laplace_beltrami import spectrum_from_file
>>> path = os.environ['MINDBOGGLE_DATA']
>>> vtk_file = os.path.join(path, 'arno', 'labels', 'label22.vtk')
>>> spectrum_from_file(vtk_file, spectrum_size=6)
[6.3469513010430304e-18,
0.0005178862383467463,
0.0017434911095630772,
0.003667561767487686,
0.005429017880363784,
0.006309346984678924]
>>> # Loop thru all MB 101 brains
>>> from mindboggle.shapes.laplace_beltrami import spectrum_from_file
>>> for hemidir in os.listdir(header):
>>> print hemidir
>>> sulci_file = os.path.join(header, hemidir, "sulci.vtk")
>>> spectrum = spectrum_from_file(sulci_file)
"""
from mindboggle.mio.vtks import read_vtk, read_scalars
from mindboggle.shapes.laplace_beltrami import spectrum_of_largest
faces, u1, u2, points, u4, u5, u6, u7 = read_vtk(vtk_file)
# Area file:
if area_file:
areas, u1 = read_scalars(area_file)
else:
areas = None
spectrum = spectrum_of_largest(points, faces, spectrum_size,
exclude_labels, normalization, areas)
return spectrum
def extract_sulci(labels_file, folds_or_file, hemi, min_boundary=1,
sulcus_names=[], verbose=False):
"""
Identify sulci from folds in a brain surface according to a labeling
protocol that includes a list of label pairs defining each sulcus.
A fold is a group of connected, deep vertices.
Steps for each fold ::
1. Remove fold if it has fewer than two labels.
2. Remove fold if its labels do not contain a sulcus label pair.
3. Find vertices with labels that are in only one of the fold's
label boundary pairs. Assign the vertices the sulcus with the label
pair if they are connected to the label boundary for that pair.
4. If there are remaining vertices, segment into sets of vertices
connected to label boundaries, and assign a unique ID to each set.
Parameters
----------
labels_file : string
file name for surface mesh VTK containing labels for all vertices
folds_or_file : list or string
fold number for each vertex / name of VTK file containing fold scalars
hemi : string
hemisphere abbreviation in {'lh', 'rh'} for sulcus labels
min_boundary : integer
minimum number of vertices for a sulcus label boundary segment
sulcus_names : list of strings
names of sulci
verbose : bool
print statements?
Returns
-------
sulci : list of integers
sulcus numbers for all vertices (-1 for non-sulcus vertices)
n_sulci : integers
number of sulci
sulci_file : string
output VTK file with sulcus numbers (-1 for non-sulcus vertices)
Examples
--------
>>> from mindboggle.features.sulci import extract_sulci
>>> from mindboggle.mio.vtks import read_scalars
>>> from mindboggle.mio.fetch_data import prep_tests
>>> urls, fetch_data = prep_tests()
>>> # Load labels, folds, neighbor lists, and sulcus names and label pairs
>>> labels_file = fetch_data(urls['left_freesurfer_labels'])
>>> folds_file = fetch_data(urls['left_folds'])
>>> folds_or_file, name = read_scalars(folds_file)
>>> hemi = 'lh'
>>> min_boundary = 10
>>> sulcus_names = []
>>> verbose = False
>>> sulci, n_sulci, sulci_file = extract_sulci(labels_file, folds_or_file,
... hemi, min_boundary, sulcus_names, verbose)
>>> n_sulci
23
>>> lens = [len([x for x in sulci if x == y]) for y in range(n_sulci)]
>>> lens[0:10]
[0, 6573, 3366, 6689, 5358, 4049, 6379, 3551, 2632, 4225]
>>> lens[10::]
[754, 3724, 2197, 5823, 1808, 5122, 513, 2153, 1445, 418, 0, 3556, 1221]
View result (skip test):
>>> from mindboggle.mio.plots import plot_surfaces
>>> plot_surfaces('sulci.vtk') # doctest: +SKIP
"""
import os
from time import time
import numpy as np
from mindboggle.mio.vtks import read_scalars, read_vtk, rewrite_scalars
from mindboggle.guts.mesh import find_neighbors
from mindboggle.guts.segment import extract_borders, propagate, segment
from mindboggle.mio.labels import DKTprotocol
# Load fold numbers if folds_or_file is a string:
if isinstance(folds_or_file, str):
folds, name = read_scalars(folds_or_file)
elif isinstance(folds_or_file, list):
folds = folds_or_file
dkt = DKTprotocol()
if hemi == 'lh':
pair_lists = dkt.left_sulcus_label_pair_lists
elif hemi == 'rh':
pair_lists = dkt.right_sulcus_label_pair_lists
else:
raise IOError("Warning: hemisphere not properly specified ('lh' or 'rh').")
# Load points, faces, and neighbors:
points, indices, lines, faces, labels, scalar_names, npoints, \
input_vtk = read_vtk(labels_file)
neighbor_lists = find_neighbors(faces, npoints)
# Array of sulcus IDs for fold vertices, initialized as -1.
# Since we do not touch gyral vertices and vertices whose labels
# are not in the label list, or vertices having only one label,
# their sulcus IDs will remain -1:
sulci = -1 * np.ones(npoints)
#-------------------------------------------------------------------------
# Loop through folds
#-------------------------------------------------------------------------
fold_numbers = [int(x) for x in np.unique(folds) if x != -1]
n_folds = len(fold_numbers)
if verbose:
print("Extract sulci from {0} folds...".format(n_folds))
t0 = time()
for n_fold in fold_numbers:
fold = [i for i,x in enumerate(folds) if x == n_fold]
len_fold = len(fold)
# List the labels in this fold:
fold_labels = [labels[x] for x in fold]
unique_fold_labels = [int(x) for x in np.unique(fold_labels)
if x != -1]
#---------------------------------------------------------------------
# NO MATCH -- fold has fewer than two labels
#---------------------------------------------------------------------
if verbose and len(unique_fold_labels) < 2:
# Ignore: sulci already initialized with -1 values:
if not unique_fold_labels:
print(" Fold {0} ({1} vertices): "
"NO MATCH -- fold has no labels".
format(n_fold, len_fold))
else:
print(" Fold {0} ({1} vertices): "
"NO MATCH -- fold has only one label ({2})".
format(n_fold, len_fold, unique_fold_labels[0]))
# Ignore: sulci already initialized with -1 values
else:
# Find all label boundary pairs within the fold:
indices_fold_pairs, fold_pairs, unique_fold_pairs = \
extract_borders(fold, labels, neighbor_lists,
ignore_values=[], return_label_pairs=True)
# Find fold label pairs in the protocol (pairs are already sorted):
fold_pairs_in_protocol = [x for x in unique_fold_pairs
if x in dkt.unique_sulcus_label_pairs]
if verbose and unique_fold_labels:
print(" Fold {0} labels: {1} ({2} vertices)".format(n_fold,
', '.join([str(x) for x in unique_fold_labels]),
len_fold))
#-----------------------------------------------------------------
# NO MATCH -- fold has no sulcus label pair
#-----------------------------------------------------------------
if verbose and not fold_pairs_in_protocol:
print(" Fold {0}: NO MATCH -- fold has no sulcus label pair".
format(n_fold, len_fold))
#-----------------------------------------------------------------
# Possible matches
#-----------------------------------------------------------------
else:
if verbose:
print(" Fold {0} label pairs in protocol: {1}".format(n_fold,
', '.join([str(x) for x in fold_pairs_in_protocol])))
# Labels in the protocol (includes repeats across label pairs):
labels_in_pairs = [x for lst in fold_pairs_in_protocol
for x in lst]
# Labels that appear in one or more sulcus label boundary:
unique_labels = []
nonunique_labels = []
for label in np.unique(labels_in_pairs):
if len([x for x in labels_in_pairs if x == label]) == 1:
unique_labels.append(label)
else:
nonunique_labels.append(label)
#-------------------------------------------------------------
# Vertices whose labels are in only one sulcus label pair
#-------------------------------------------------------------
# Find vertices with a label that is in only one of the fold's
# label pairs (the other label in the pair can exist in other
# pairs). Assign the vertices the sulcus with the label pair
# if they are connected to the label boundary for that pair.
#-------------------------------------------------------------
if unique_labels:
for pair in fold_pairs_in_protocol:
# If one or both labels in label pair is/are unique:
unique_labels_in_pair = [x for x in pair
if x in unique_labels]
n_unique = len(unique_labels_in_pair)
if n_unique:
ID = None
for i, pair_list in enumerate(pair_lists):
if not isinstance(pair_list, list):
pair_list = [pair_list]
if pair in pair_list:
ID = i
break
if ID:
# Seeds from label boundary vertices
# (fold_pairs and pair already sorted):
indices_pair = [x for i,x
in enumerate(indices_fold_pairs)
if fold_pairs[i] == pair]
# Vertices with unique label(s) in pair:
indices_unique_labels = [fold[i]
for i,x in enumerate(fold_labels)
if x in dkt.unique_sulcus_label_pairs]
# Propagate from seeds to labels in label pair:
sulci2 = segment(indices_unique_labels,
neighbor_lists,
min_region_size=1,
seed_lists=[indices_pair],
keep_seeding=False,
spread_within_labels=True,
labels=labels)
sulci[sulci2 != -1] = ID
# Print statement:
if verbose:
if n_unique == 1:
ps1 = '1 label'
else:
ps1 = 'Both labels'
if len(sulcus_names):
ps2 = sulcus_names[ID]
else:
ps2 = ''
print(" {0} unique to one fold pair: "
"{1} {2}".
format(ps1, ps2,
unique_labels_in_pair))
#-------------------------------------------------------------
# Vertex labels shared by multiple label pairs
#-------------------------------------------------------------
# Propagate labels from label borders to vertices with labels
# that are shared by multiple label pairs in the fold.
#-------------------------------------------------------------
if len(nonunique_labels):
# For each label shared by different label pairs:
for label in nonunique_labels:
# Print statement:
if verbose:
print(" Propagate sulcus borders with label {0}".
format(int(label)))
# Construct seeds from label boundary vertices:
seeds = -1 * np.ones(len(points))
for ID, pair_list in enumerate(pair_lists):
if not isinstance(pair_list, list):
pair_list = [pair_list]
label_pairs = [x for x in pair_list if label in x]
for label_pair in label_pairs:
indices_pair = [x for i,x
in enumerate(indices_fold_pairs)
if np.sort(fold_pairs[i]).
tolist() == label_pair]
if indices_pair:
# Do not include short boundary segments:
if min_boundary > 1:
indices_pair2 = []
seeds2 = segment(indices_pair,
neighbor_lists)
useeds2 = [x for x in
np.unique(seeds2)
if x != -1]
for seed2 in useeds2:
iseed2 = [i for i,x
in enumerate(seeds2)
if x == seed2]
if len(iseed2) >= min_boundary:
indices_pair2.extend(iseed2)
elif verbose:
if len(iseed2) == 1:
print(" Remove "
"assignment "
"of ID {0} from "
"1 vertex".
format(seed2))
else:
print(" Remove "
"assignment "
"of ID {0} from "
"{1} vertices".
format(seed2,
len(iseed2)))
indices_pair = indices_pair2
# Assign sulcus IDs to seeds:
seeds[indices_pair] = ID
# Identify vertices with the label:
label_array = -1 * np.ones(len(points))
indices_label = [fold[i] for i,x
in enumerate(fold_labels)
if x == label]
if len(indices_label):
label_array[indices_label] = 1
# Propagate from seeds to vertices with label:
#indices_seeds = []
#for seed in range(int(max(seeds))+1):
# indices_seeds.append([i for i,x
# in enumerate(seeds)
# if x == seed])
#sulci2 = segment(indices_label, neighbor_lists,
# 50, indices_seeds, False, True,
# labels)
sulci2 = propagate(points, faces,
label_array, seeds, sulci,
max_iters=10000,
tol=0.001, sigma=5)
sulci[sulci2 != -1] = sulci2[sulci2 != -1]
sulcus_numbers = [int(x) for x in np.unique(sulci) if x != -1]
# if not np.isnan(x)]
n_sulci = len(sulcus_numbers)
#-------------------------------------------------------------------------
# Print statements
#-------------------------------------------------------------------------
if verbose:
print("Extracted {0} sulci from {1} folds ({2:.1f}s):".
format(n_sulci, n_folds, time()-t0))
if sulcus_names:
for sulcus_number in sulcus_numbers:
print(" {0}: {1}".format(sulcus_number,
sulcus_names[sulcus_number]))
elif sulcus_numbers:
print(" " + ", ".join([str(x) for x in sulcus_numbers]))
unresolved = [i for i in range(len(pair_lists))
if i not in sulcus_numbers]
if len(unresolved) == 1:
print("The following sulcus is unaccounted for:")
else:
print("The following {0} sulci are unaccounted for:".
format(len(unresolved)))
if sulcus_names:
for sulcus_number in unresolved:
print(" {0}: {1}".format(sulcus_number,
sulcus_names[sulcus_number]))
else:
print(" " + ", ".join([str(x) for x in unresolved]))
#-------------------------------------------------------------------------
# Return sulci, number of sulci, and file name
#-------------------------------------------------------------------------
sulci = [int(x) for x in sulci]
sulci_file = os.path.join(os.getcwd(), 'sulci.vtk')
rewrite_scalars(labels_file, sulci_file, sulci, 'sulci', sulci)
if not os.path.exists(sulci_file):
raise IOError(sulci_file + " not found")
return sulci, n_sulci, sulci_file
def extract_fundi(folds, curv_file, depth_file, min_separation=10,
erode_ratio=0.1, erode_min_size=1, save_file=False,
output_file='', background_value=-1, verbose=False):
"""
Extract fundi from folds.
A fundus is a branching curve that runs along the deepest and most highly
curved portions of a fold. This function extracts one fundus from each
fold by finding the deepest vertices inside the fold, finding endpoints
along the edge of the fold, and connecting the former to the latter with
tracks that run along deep and curved paths (through vertices with high
values of travel depth multiplied by curvature), and a final filtration
step.
The deepest vertices are those with values at least two median
absolute deviations above the median (non-zero) value, with the higher
value chosen if two of the vertices are within (a default of) 10 edges
from each other (to reduce the number of possible fundus paths as well
as computation time).
To find the endpoints, the find_outer_endpoints function propagates
multiple tracks from seed vertices at median depth in the fold through
concentric rings toward the fold’s edge, selecting maximal values within
each ring, and terminating at candidate endpoints. The final endpoints
are those candidates at the end of tracks that have a high median value,
with the higher value chosen if two candidate endpoints are within
(a default of) 10 edges from each other (otherwise, the resulting fundi
can have spurious branching at the fold’s edge).
The connect_points_erosion function connects the deepest fold vertices
to the endpoints with a skeleton of 1-vertex-thick curves by erosion.
It erodes by iteratively removing simple topological points and endpoints
in order of lowest to highest values, where a simple topological point
is a vertex that when added to or removed from an object on a surface
mesh (such as a fundus curve) does not alter the object's topology.
Steps ::
1. Find fundus endpoints (outer anchors) with find_outer_endpoints().
2. Include inner anchor points.
3. Connect anchor points using connect_points_erosion();
inner anchors are removed if they result in endpoints.
Note ::
Follow this with segment_by_region() to segment fundi by sulci.
Parameters
----------
folds : numpy array or list of integers
fold number for each vertex
curv_file : string
surface mesh file in VTK format with mean curvature values
depth_file : string
surface mesh file in VTK format with rescaled depth values
likelihoods : list of integers
fundus likelihood value for each vertex
min_separation : integer
minimum number of edges between inner/outer anchor points
erode_ratio : float
fraction of indices to test for removal at each iteration
in connect_points_erosion()
save_file : bool
save output VTK file?
output_file : string
output VTK file
background_value : integer or float
background value
verbose : bool
print statements?
Returns
-------
fundus_per_fold : list of integers
fundus numbers for all vertices, labeled by fold
(-1 for non-fundus vertices)
n_fundi_in_folds : integer
number of fundi
fundus_per_fold_file : string (if save_file)
output VTK file with fundus numbers (-1 for non-fundus vertices)
Examples
--------
>>> # Extract fundus from one or more folds:
>>> import numpy as np
>>> from mindboggle.mio.vtks import read_scalars
>>> from mindboggle.features.fundi import extract_fundi
>>> from mindboggle.mio.fetch_data import prep_tests
>>> urls, fetch_data = prep_tests()
>>> curv_file = fetch_data(urls['left_mean_curvature'], '', '.vtk')
>>> depth_file = fetch_data(urls['left_travel_depth'], '', '.vtk')
>>> folds_file = fetch_data(urls['left_folds'], '', '.vtk')
>>> folds, name = read_scalars(folds_file, True, True)
>>> # Limit number of folds to speed up the test:
>>> limit_folds = True
>>> if limit_folds:
... fold_numbers = [4] #[4, 6]
... i0 = [i for i,x in enumerate(folds) if x not in fold_numbers]
... folds[i0] = -1
>>> min_separation = 10
>>> erode_ratio = 0.10
>>> erode_min_size = 10
>>> save_file = True
>>> output_file = 'extract_fundi_fold4.vtk'
>>> background_value = -1
>>> verbose = False
>>> o1, o2, fundus_per_fold_file = extract_fundi(folds, curv_file,
... depth_file, min_separation, erode_ratio, erode_min_size,
... save_file, output_file, background_value, verbose)
>>> lens = [len([x for x in o1 if x == y])
... for y in np.unique(o1) if y != background_value]
>>> lens[0:10] # [66, 2914, 100, 363, 73, 331, 59, 30, 1, 14] # (if not limit_folds)
[73]
View result without background (skip test):
>>> from mindboggle.mio.plots import plot_surfaces # doctest: +SKIP
>>> from mindboggle.mio.vtks import rewrite_scalars # doctest: +SKIP
>>> rewrite_scalars(fundus_per_fold_file,
... 'extract_fundi_fold4_no_background.vtk', o1,
... 'fundus_per_fold', folds) # doctest: +SKIP
>>> plot_surfaces('extract_fundi_fold4_no_background.vtk') # doctest: +SKIP
"""
# Extract a skeleton to connect endpoints in a fold:
import os
import numpy as np
from time import time
from mindboggle.mio.vtks import read_scalars, read_vtk, rewrite_scalars
from mindboggle.guts.compute import median_abs_dev
from mindboggle.guts.paths import find_max_values
from mindboggle.guts.mesh import find_neighbors_from_file
from mindboggle.guts.mesh import find_complete_faces
from mindboggle.guts.paths import find_outer_endpoints
from mindboggle.guts.paths import connect_points_erosion
if isinstance(folds, list):
folds = np.array(folds)
# Load values, inner anchor threshold, and neighbors:
if os.path.isfile(curv_file):
points, indices, lines, faces, curvs, scalar_names, npoints, \
input_vtk = read_vtk(curv_file, True, True)
else:
raise IOError("{0} doesn't exist!".format(curv_file))
if os.path.isfile(curv_file):
depths, name = read_scalars(depth_file, True, True)
else:
raise IOError("{0} doesn't exist!".format(depth_file))
values = curvs * depths
values0 = [x for x in values if x > 0]
thr = np.median(values0) + 2 * median_abs_dev(values0)
neighbor_lists = find_neighbors_from_file(curv_file)
# ------------------------------------------------------------------------
# Loop through folds:
# ------------------------------------------------------------------------
t1 = time()
skeletons = []
unique_fold_IDs = [x for x in np.unique(folds) if x != background_value]
if verbose:
if len(unique_fold_IDs) == 1:
print("Extract a fundus from 1 fold...")
else:
print("Extract a fundus from each of {0} folds...".
format(len(unique_fold_IDs)))
for fold_ID in unique_fold_IDs:
indices_fold = [i for i,x in enumerate(folds) if x == fold_ID]
if indices_fold:
if verbose:
print(' Fold {0}:'.format(int(fold_ID)))
# ----------------------------------------------------------------
# Find outer anchor points on the boundary of the surface region,
# to serve as fundus endpoints:
# ----------------------------------------------------------------
outer_anchors, tracks = find_outer_endpoints(indices_fold,
neighbor_lists, values, depths, min_separation,
background_value, verbose)
# ----------------------------------------------------------------
# Find inner anchor points:
# ----------------------------------------------------------------
inner_anchors = find_max_values(points, values, min_separation,
thr)
# ----------------------------------------------------------------
# Connect anchor points to create skeleton:
# ----------------------------------------------------------------
B = background_value * np.ones(npoints)
B[indices_fold] = 1
skeleton = connect_points_erosion(B, neighbor_lists,
outer_anchors, inner_anchors, values, erode_ratio,
erode_min_size, [], '', background_value, verbose)
if skeleton:
skeletons.extend(skeleton)
## ---------------------------------------------------------------
## Remove fundus vertices if they make complete triangle faces:
## ---------------------------------------------------------------
#Iremove = find_complete_faces(skeletons, faces)
#if Iremove:
# skeletons = list(frozenset(skeletons).difference(Iremove))
indices_skel = [x for x in skeletons if folds[x] != background_value]
fundus_per_fold = background_value * np.ones(npoints)
fundus_per_fold[indices_skel] = folds[indices_skel]
n_fundi_in_folds = len([x for x in np.unique(fundus_per_fold)
if x != background_value])
if n_fundi_in_folds == 1:
sdum = 'fold fundus'
else:
sdum = 'fold fundi'
if verbose:
print(' ...Extracted {0} {1}; {2} total ({3:.2f} seconds)'.
format(n_fundi_in_folds, sdum, n_fundi_in_folds, time() - t1))
# ------------------------------------------------------------------------
# Return fundi, number of fundi, and file name:
# ------------------------------------------------------------------------
fundus_per_fold_file = None
if n_fundi_in_folds > 0:
fundus_per_fold = [int(x) for x in fundus_per_fold]
if save_file:
if output_file:
fundus_per_fold_file = output_file
else:
fundus_per_fold_file = os.path.join(os.getcwd(),
'fundus_per_fold.vtk')
rewrite_scalars(curv_file, fundus_per_fold_file, fundus_per_fold,
'fundi', [], background_value)
if not os.path.exists(fundus_per_fold_file):
raise IOError(fundus_per_fold_file + " not found")
return fundus_per_fold, n_fundi_in_folds, fundus_per_fold_file
def extract_fundi(folds,
curv_file,
depth_file,
min_separation=10,
erode_ratio=0.1,
erode_min_size=1,
save_file=False,
verbose=False):
"""
Extract fundi from folds.
A fundus is a branching curve that runs along the deepest and most
highly curved portions of a fold.
Steps ::
1. Find fundus endpoints (outer anchors) with find_outer_anchors().
2. Include inner anchor points.
3. Connect anchor points using connect_points_erosion();
inner anchors are removed if they result in endpoints.
Parameters
----------
folds : numpy array or list of integers
fold number for each vertex
curv_file : string
surface mesh file in VTK format with mean curvature values
depth_file : string
surface mesh file in VTK format with rescaled depth values
likelihoods : list of integers
fundus likelihood value for each vertex
min_separation : integer
minimum number of edges between inner/outer anchor points
erode_ratio : float
fraction of indices to test for removal at each iteration
in connect_points_erosion()
save_file : bool
save output VTK file?
verbose : bool
print statements?
Returns
-------
fundus_per_fold : list of integers
fundus numbers for all vertices, labeled by fold
(-1 for non-fundus vertices)
n_fundi_in_folds : integer
number of fundi
fundus_per_fold_file : string (if save_file)
output VTK file with fundus numbers (-1 for non-fundus vertices)
Examples
--------
>>> # Extract fundus from one or more folds:
>>> single_fold = True
>>> from mindboggle.mio.vtks import read_scalars
>>> from mindboggle.features.fundi import extract_fundi
>>> from mindboggle.mio.fetch_data import prep_tests
>>> urls, fetch_data = prep_tests()
>>> curv_file = fetch_data(urls['left_mean_curvature'])
>>> depth_file = fetch_data(urls['left_travel_depth'])
>>> folds_file = fetch_data(urls['left_folds'])
>>> folds, name = read_scalars(folds_file, True, True)
>>> if single_fold:
... fold_number = 2 #11
... folds[folds != fold_number] = -1
>>> min_separation = 10
>>> erode_ratio = 0.10
>>> erode_min_size = 10
>>> save_file = True
>>> verbose = False
>>> o1, o2, fundus_per_fold_file = extract_fundi(folds, curv_file,
... depth_file, min_separation, erode_ratio, erode_min_size,
... save_file, verbose)
>>> if single_fold:
... lens = [len([x for x in o1 if x == 2])]
... else:
... lens = [len([x for x in o1 if x == y]) for y in range(o2)]
>>> lens[0:10]
[115]
View result (skip test):
>>> from mindboggle.mio.plots import plot_surfaces
>>> plot_surfaces(fundus_per_fold_file) # doctest: +SKIP
"""
# Extract a skeleton to connect endpoints in a fold:
import os
import numpy as np
from time import time
from mindboggle.mio.vtks import read_scalars, read_vtk, rewrite_scalars
from mindboggle.guts.compute import median_abs_dev
from mindboggle.guts.paths import find_max_values
from mindboggle.guts.mesh import find_neighbors_from_file
from mindboggle.guts.mesh import find_complete_faces
from mindboggle.guts.paths import find_outer_anchors
from mindboggle.guts.paths import connect_points_erosion
if isinstance(folds, list):
folds = np.array(folds)
# Load values, inner anchor threshold, and neighbors:
points, indices, lines, faces, curvs, scalar_names, npoints, \
input_vtk = read_vtk(curv_file, True, True)
depths, name = read_scalars(depth_file, True, True)
values = curvs * depths
values0 = [x for x in values if x > 0]
thr = np.median(values0) + 2 * median_abs_dev(values0)
neighbor_lists = find_neighbors_from_file(curv_file)
#-------------------------------------------------------------------------
# Loop through folds:
#-------------------------------------------------------------------------
t1 = time()
skeletons = []
unique_fold_IDs = [x for x in np.unique(folds) if x != -1]
if verbose:
if len(unique_fold_IDs) == 1:
print("Extract a fundus from 1 fold...")
else:
print("Extract a fundus from each of {0} folds...".format(
len(unique_fold_IDs)))
for fold_ID in unique_fold_IDs:
indices_fold = [i for i, x in enumerate(folds) if x == fold_ID]
if indices_fold:
if verbose:
print(' Fold {0}:'.format(int(fold_ID)))
#-----------------------------------------------------------------
# Find outer anchor points on the boundary of the surface region,
# to serve as fundus endpoints:
#-----------------------------------------------------------------
verbose = False
outer_anchors, tracks = find_outer_anchors(indices_fold,
neighbor_lists, values,
depths, min_separation,
verbose)
#-----------------------------------------------------------------
# Find inner anchor points:
#-----------------------------------------------------------------
inner_anchors = find_max_values(points, values, min_separation,
thr)
#-----------------------------------------------------------------
# Connect anchor points to create skeleton:
#-----------------------------------------------------------------
B = -1 * np.ones(npoints)
B[indices_fold] = 1
skeleton = connect_points_erosion(B,
neighbor_lists,
outer_anchors,
inner_anchors,
values,
erode_ratio,
erode_min_size,
save_steps=[],
save_vtk='',
verbose=False)
if skeleton:
skeletons.extend(skeleton)
#-----------------------------------------------------------------
# Remove fundus vertices if they complete triangle faces:
#-----------------------------------------------------------------
Iremove = find_complete_faces(skeletons, faces)
if Iremove:
skeletons = list(frozenset(skeletons).difference(Iremove))
indices_skel = [x for x in skeletons if folds[x] != -1]
fundus_per_fold = -1 * np.ones(npoints)
fundus_per_fold[indices_skel] = folds[indices_skel]
n_fundi_in_folds = len([x for x in np.unique(fundus_per_fold) if x != -1])
if n_fundi_in_folds == 1:
sdum = 'fold fundus'
else:
sdum = 'fold fundi'
if verbose:
print(' ...Extracted {0} {1}; {2} total ({3:.2f} seconds)'.format(
n_fundi_in_folds, sdum, n_fundi_in_folds,
time() - t1))
#-------------------------------------------------------------------------
# Return fundi, number of fundi, and file name:
#-------------------------------------------------------------------------
if n_fundi_in_folds > 0:
fundus_per_fold = [int(x) for x in fundus_per_fold]
if save_file:
fundus_per_fold_file = os.path.join(os.getcwd(),
'fundus_per_fold.vtk')
rewrite_scalars(curv_file, fundus_per_fold_file, fundus_per_fold,
'fundi', folds)
if not os.path.exists(fundus_per_fold_file):
raise IOError(fundus_per_fold_file + " not found")
else:
fundus_per_fold_file = None
return fundus_per_fold, n_fundi_in_folds, fundus_per_fold_file
def write_vertex_measures(output_table,
labels_or_file,
sulci=[],
fundi=[],
affine_transform_files=[],
inverse_booleans=[],
transform_format='itk',
area_file='',
mean_curvature_file='',
travel_depth_file='',
geodesic_depth_file='',
freesurfer_thickness_file='',
freesurfer_curvature_file='',
freesurfer_sulc_file=''):
"""
Make a table of shape values per vertex.
Note ::
This function is tailored for Mindboggle outputs.
Parameters
----------
output_table : string
output file (full path)
labels_or_file : list or string
label number for each vertex or name of VTK file with index scalars
sulci : list of integers
indices to sulci, one per vertex, with -1 indicating no sulcus
fundi : list of integers
indices to fundi, one per vertex, with -1 indicating no fundus
affine_transform_files : list of strings
affine transform files to standard space
inverse_booleans : list of of zeros and ones
for each transform, 1 to take the inverse, else 0
transform_format : string
format for transform file
Ex: 'txt' for text, 'itk' for ITK, and 'mat' for Matlab format
area_file : string
name of VTK file with surface area scalar values
mean_curvature_file : string
name of VTK file with mean curvature scalar values
travel_depth_file : string
name of VTK file with travel depth scalar values
geodesic_depth_file : string
name of VTK file with geodesic depth scalar values
freesurfer_thickness_file : string
name of VTK file with FreeSurfer thickness scalar values
freesurfer_curvature_file : string
name of VTK file with FreeSurfer curvature (curv) scalar values
freesurfer_sulc_file : string
name of VTK file with FreeSurfer convexity (sulc) scalar values
Returns
-------
output_table : table file name for vertex shape values
Examples
--------
>>> import os
>>> from mindboggle.mio.vtks import read_scalars
>>> from mindboggle.mio.tables import write_vertex_measures
>>> #
>>> output_table = ''#vertex_shapes.csv'
>>> path = os.environ['MINDBOGGLE_DATA']
>>> labels_or_file = os.path.join(path, 'arno', 'labels', 'lh.labels.DKT25.manual.vtk')
>>> sulci_file = os.path.join(path, 'arno', 'features', 'sulci.vtk')
>>> fundi_file = os.path.join(path, 'arno', 'features', 'fundi.vtk')
>>> sulci, name = read_scalars(sulci_file)
>>> fundi, name = read_scalars(fundi_file)
>>> affine_transform_files = [os.path.join(path, 'arno', 'mri',
>>> 't1weighted_brain.MNI152Affine.txt')]
>>> inverse_booleans = [1]
>>> transform_format = 'itk'
>>> swap_xy = True
>>> area_file = os.path.join(path, 'arno', 'shapes', 'lh.pial.area.vtk')
>>> mean_curvature_file = os.path.join(path, 'arno', 'shapes', 'lh.pial.mean_curvature.vtk')
>>> travel_depth_file = os.path.join(path, 'arno', 'shapes', 'lh.pial.travel_depth.vtk')
>>> geodesic_depth_file = os.path.join(path, 'arno', 'shapes', 'lh.pial.geodesic_depth.vtk')
>>> freesurfer_thickness_file = ''
>>> freesurfer_curvature_file = ''
>>> freesurfer_sulc_file = ''
>>> #
>>> write_vertex_measures(output_table, labels_or_file, sulci, fundi,
>>> affine_transform_files, inverse_booleans, transform_format, area_file,
>>> mean_curvature_file, travel_depth_file, geodesic_depth_file,
>>> freesurfer_thickness_file, freesurfer_curvature_file, freesurfer_sulc_file)
"""
import os
import numpy as np
import pandas as pd
from mindboggle.mio.vtks import read_scalars, read_vtk, \
apply_affine_transforms
# Make sure inputs are lists:
if isinstance(labels_or_file, np.ndarray):
labels = [int(x) for x in labels_or_file]
elif isinstance(labels_or_file, list):
labels = labels_or_file
elif isinstance(labels_or_file, str):
labels, name = read_scalars(labels_or_file)
if isinstance(sulci, np.ndarray):
sulci = [int(x) for x in sulci]
if isinstance(fundi, np.ndarray):
fundi = [int(x) for x in fundi]
if not labels and not sulci and not fundi:
import sys
sys.exit('No feature data to tabulate in write_vertex_measures().')
# Feature names and corresponding feature lists:
feature_names = ['label ID', 'sulcus ID', 'fundus ID']
feature_lists = [labels, sulci, fundi]
# Shape names corresponding to shape files below:
shape_names = [
'area', 'travel depth', 'geodesic depth', 'mean curvature',
'freesurfer curvature', 'freesurfer thickness',
'freesurfer convexity (sulc)'
]
# Load shape files as a list of numpy arrays of per-vertex shape values:
shape_files = [
area_file, travel_depth_file, geodesic_depth_file, mean_curvature_file,
freesurfer_curvature_file, freesurfer_thickness_file,
freesurfer_sulc_file
]
# Append columns of per-vertex scalar values:
columns = []
column_names = []
for ifeature, values in enumerate(feature_lists):
if values:
columns.append(values)
column_names.append(feature_names[ifeature])
first_pass = True
for ishape, shape_file in enumerate(shape_files):
if os.path.exists(shape_file):
if first_pass:
# Append x,y,z position per vertex to columns:
u1, u2, u3, points, u4, scalars, u5, u6 = read_vtk(shape_file)
xyz_positions = np.asarray(points)
for ixyz, xyz in enumerate(['x', 'y', 'z']):
column_names.append('position: {0}'.format(xyz))
columns.append(xyz_positions[:, ixyz].tolist())
first_pass = False
# Append standard space x,y,z position to columns:
if affine_transform_files and transform_format:
affine_points, \
foo1 = apply_affine_transforms(affine_transform_files,
inverse_booleans, transform_format,
points, vtk_file_stem='')
xyz_std_positions = affine_points
for ixyz, xyz in enumerate(['x', 'y', 'z']):
column_names.append('position in standard space:'
' {0}'.format(xyz))
columns.append(xyz_std_positions[:, ixyz].tolist())
else:
scalars, name = read_scalars(shape_file)
if len(scalars):
columns.append(scalars)
column_names.append(shape_names[ishape])
# Prepend with column of indices and write table
if not output_table:
output_table = os.path.join(os.getcwd(), 'vertices.csv')
df = pd.DataFrame(np.transpose(columns), columns=column_names)
df.to_csv(output_table, index=False)
if not os.path.exists(output_table):
raise (IOError(output_table + " not found"))
return output_table
def concatenate_sulcus_scalars(scalar_files,
fold_files,
label_files,
background_value=-1):
"""
Prepare data for estimating scalar distributions along and outside fundi.
Extract (e.g., depth, curvature) scalar values in folds, along sulcus
label boundaries as well as outside the sulcus label boundaries.
Concatenate these scalar values across multiple files.
Parameters
----------
scalar_files : list of strings
names of surface mesh VTK files with scalar values to concatenate
fold_files : list of strings (corr. to each list in scalar_files)
VTK files with fold numbers as scalars (-1 for non-fold vertices)
label_files : list of strings (corr. to fold_files)
VTK files with label numbers (-1 for unlabeled vertices)
background_value : integer or float
background value
Returns
-------
border_scalars : list of floats
concatenated scalar values within folds along sulcus label boundaries
nonborder_scalars : list of floats
concatenated scalar values within folds outside sulcus label boundaries
Examples
--------
>>> # Concatenate (duplicate) depth scalars:
>>> import numpy as np
>>> from mindboggle.shapes.likelihood import concatenate_sulcus_scalars
>>> from mindboggle.mio.fetch_data import prep_tests
>>> urls, fetch_data = prep_tests()
>>> depth_file = fetch_data(urls['left_travel_depth'], '', '.vtk')
>>> labels_file = fetch_data(urls['left_freesurfer_labels'], '', '.vtk')
>>> folds_file = fetch_data(urls['left_folds'], '', '.vtk')
>>> scalar_files = [depth_file, depth_file]
>>> fold_files = [folds_file, folds_file]
>>> label_files = [labels_file, labels_file]
>>> background_value = -1
>>> border, nonborder = concatenate_sulcus_scalars(scalar_files,
... fold_files, label_files, background_value)
>>> print(np.array_str(np.array(border[0:5]),
... precision=5, suppress_small=True))
[ 3.48284 2.57157 4.27596 4.56549 3.84881]
>>> print(np.array_str(np.array(nonborder[0:5]),
... precision=5, suppress_small=True))
[ 2.87204 2.89388 3.55364 2.81681 3.70736]
"""
import numpy as np
from mindboggle.mio.vtks import read_scalars
from mindboggle.guts.mesh import find_neighbors_from_file
from mindboggle.guts.segment import extract_borders
from mindboggle.mio.labels import DKTprotocol
dkt = DKTprotocol()
# Prepare (non-unique) list of sulcus label pairs:
protocol_label_pairs = [
x for lst in dkt.sulcus_label_pair_lists for x in lst
]
border_scalars = []
nonborder_scalars = []
# Loop through files with the scalar values:
for ifile, scalar_file in enumerate(scalar_files):
#print(scalar_file)
# Load scalars, folds, and labels:
folds_file = fold_files[ifile]
labels_file = label_files[ifile]
scalars, name = read_scalars(scalar_file, True, True)
if scalars.shape:
folds, name = read_scalars(folds_file)
labels, name = read_scalars(labels_file)
indices_folds = [
i for i, x in enumerate(folds) if x != background_value
]
neighbor_lists = find_neighbors_from_file(labels_file)
# Find all label border pairs within the folds:
indices_label_pairs, label_pairs, unique_pairs = extract_borders(
indices_folds,
labels,
neighbor_lists,
ignore_values=[-1],
return_label_pairs=True)
indices_label_pairs = np.array(indices_label_pairs)
# Find vertices with label pairs in the sulcus labeling protocol:
Ipairs_in_protocol = [
i for i, x in enumerate(label_pairs)
if x in protocol_label_pairs
]
indices_label_pairs = indices_label_pairs[Ipairs_in_protocol]
indices_outside_pairs = list(
frozenset(indices_folds).difference(indices_label_pairs))
# Store scalar values in folds along label border pairs:
border_scalars.extend(scalars[indices_label_pairs].tolist())
# Store scalar values in folds outside label border pairs:
nonborder_scalars.extend(scalars[indices_outside_pairs].tolist())
return border_scalars, nonborder_scalars
def write_average_face_values_per_label(input_indices_vtk,
input_values_vtk='',
area_file='',
output_stem='',
exclude_values=[-1],
background_value=-1):
"""
Write out a separate VTK file for each integer
in (the first) scalar list of an input VTK file.
Optionally write the values drawn from a second VTK file.
Parameters
----------
input_indices_vtk : string
path of the input VTK file that contains indices as scalars
input_values_vtk : string
path of the input VTK file that contains values as scalars
output_stem : string
path and stem of the output VTK file
exclude_values : list or array
values to exclude
background_value : integer or float
background value in output VTK files
scalar_name : string
name of a lookup table of scalars values
Examples
--------
>>> import os
>>> from mindboggle.mio.tables import write_average_face_values_per_label
>>> path = '/homedir/mindboggled'
>>> input_indices_vtk = os.path.join(path, 'Twins-2-1', 'labels', 'left_cortical_surface', 'freesurfer_cortex_labels.vtk')
>>> input_values_vtk = os.path.join(path, 'Twins-2-1', 'shapes', 'left_cortical_surface', 'freesurfer_thickness.vtk')
>>> area_file = os.path.join(path, 'Twins-2-1', 'shapes', 'left_cortical_surface', 'area.vtk')
>>> output_stem = 'labels_thickness'
>>> exclude_values = [-1]
>>> background_value = -1
>>> #
>>> write_average_face_values_per_label(input_indices_vtk,
>>> input_values_vtk, area_file, output_stem, exclude_values, background_value)
>>> #
>>> # View:
>>> #example_vtk = os.path.join(os.getcwd(), output_stem + '0.vtk')
>>> #from mindboggle.mio.plots import plot_surfaces
>>> #plot_surfaces(example_vtk)
"""
import os
import numpy as np
import pandas as pd
from mindboggle.mio.vtks import read_scalars, read_vtk, write_vtk
from mindboggle.guts.mesh import remove_faces
# Load VTK file:
faces, lines, indices, points, npoints, scalars, scalar_names, \
foo1 = read_vtk(input_indices_vtk, True, True)
if area_file:
area_scalars, name = read_scalars(area_file, True, True)
print("Explode the scalar list in {0}".format(
os.path.basename(input_indices_vtk)))
if input_values_vtk != input_indices_vtk:
values, name = read_scalars(input_values_vtk, True, True)
print("Explode the scalar list of values in {0} "
"with the scalar list of indices in {1}".format(
os.path.basename(input_values_vtk),
os.path.basename(input_indices_vtk)))
else:
values = np.copy(scalars)
# Loop through unique (non-excluded) scalar values:
unique_scalars = [
int(x) for x in np.unique(scalars) if x not in exclude_values
]
for scalar in unique_scalars:
keep_indices = [x for sublst in faces for x in sublst]
new_faces = remove_faces(faces, keep_indices)
# Create array and indices for scalar value:
select_scalars = np.copy(scalars)
select_scalars[scalars != scalar] = background_value
scalar_indices = [
i for i, x in enumerate(select_scalars) if x == scalar
]
print(" Scalar {0}: {1} vertices".format(scalar, len(scalar_indices)))
#---------------------------------------------------------------------
# For each face, average vertex values:
#---------------------------------------------------------------------
output_table = os.path.join(os.getcwd(),
output_stem + str(scalar) + '.csv')
columns = []
for face in new_faces:
values = []
for index in face:
if area_file:
values.append(scalars[index] / area_scalars[index])
else:
values.append(scalars[index])
columns.append(np.mean(values))
#-----------------------------------------------------------------
# Write to table:
#-----------------------------------------------------------------
df = pd.DataFrame({'': columns})
df.to_csv(output_table, index=False)
# Write VTK file with scalar value:
#output_vtk = os.path.join(os.getcwd(), output_stem + str(scalar) + '.vtk')
#write_vtk(output_vtk, points, indices, lines, new_faces,
# [select_values.tolist()], [output_scalar_name])
if not os.path.exists(output_table):
raise (IOError(output_table + " not found"))
def compute_likelihood(trained_file,
depth_file,
curvature_file,
folds,
save_file=False,
background_value=-1):
"""
Compute likelihoods based on input values, folds, estimated parameters.
Compute likelihood values for a given VTK surface mesh file, after
training on distributions of depth and curvature values from multiple
files.
Parameters
----------
trained_file : pickle compressed file
depth_border, curv_border, depth_nonborder, and curv_nonborder are
dictionaries containing lists of floats
(estimates of depth or curvature means, sigmas, and weights
trained on fold vertices either on or off sulcus label borders)
depth_file : string
VTK surface mesh file with depth values in [0,1] for all vertices
curvature_file : string
VTK surface mesh file with curvature values in [-1,1] for all vertices
folds : list of integers
fold number for all vertices (-1 for non-fold vertices)
save_file : bool
save output VTK file?
background_value : integer or float
background value
Returns
-------
likelihoods : list of floats
likelihood values for all vertices (0 for non-fold vertices)
likelihoods_file : string (if save_file)
name of output VTK file with likelihood scalars
(-1 for non-fold vertices)
Notes
-----
The depth_curv_border_nonborder_parameters.pkl file needs to be updated.
Examples
--------
>>> from mindboggle.mio.vtks import read_scalars
>>> from mindboggle.shapes.likelihood import compute_likelihood
>>> from mindboggle.mio.fetch_data import prep_tests
>>> urls, fetch_data = prep_tests()
>>> depth_file = fetch_data(urls['left_travel_depth'], '', '.vtk')
>>> curvature_file = fetch_data(urls['left_mean_curvature'], '', '.vtk')
>>> folds_file = fetch_data(urls['left_folds'], '', '.vtk')
>>> trained_file = fetch_data(urls[
... 'depth_curv_border_nonborder_parameters'], '', '.pkl') # doctest: +SKIP
>>> folds, name = read_scalars(folds_file)
>>> save_file = True
>>> background_value = -1
>>> likelihoods, likelihoods_file = compute_likelihood(trained_file,
... depth_file, curvature_file, folds, save_file, background_value) # doctest: +SKIP
View result (skip test):
>>> from mindboggle.mio.plots import plot_surfaces # doctest: +SKIP
>>> plot_surfaces('likelihoods.vtk', folds_file) # doctest: +SKIP
"""
import os
import numpy as np
from math import pi
import pickle
from io import open
from mindboggle.mio.vtks import read_scalars, rewrite_scalars
# Initialize variables:
tiny = 0.000000001
L = np.zeros(len(folds))
probs_border = np.zeros(len(folds))
probs_nonborder = np.zeros(len(folds))
# Load estimated depth and curvature distribution parameters:
depth_border, curv_border, depth_nonborder, \
curv_nonborder = pickle.load(open(trained_file, "rb"))
# Load depths, curvatures:
depths, name = read_scalars(depth_file, True, True)
curvatures, name = read_scalars(curvature_file, True, True)
# Prep for below:
n = 2
twopiexp = (2 * pi)**(n / 2)
border_sigmas = depth_border['sigmas'] * curv_border['sigmas']
nonborder_sigmas = depth_nonborder['sigmas'] * curv_nonborder['sigmas']
norm_border = 1 / (twopiexp * border_sigmas + tiny)
norm_nonborder = 1 / (twopiexp * nonborder_sigmas + tiny)
I = [i for i, x in enumerate(folds) if x != background_value]
N = depth_border['sigmas'].shape[0]
for j in range(N):
# Border:
expB = depth_border['weights'][j] * \
((depths[I]-depth_border['means'][j])**2) / \
depth_border['sigmas'][j]**2
expB += curv_border['weights'][j] * \
((curvatures[I]-curv_border['means'][j])**2) / \
curv_border['sigmas'][j]**2
expB = -expB / 2
probs_border[I] = probs_border[I] + norm_border[j] * np.exp(expB)
# Non-border:
expNB = depth_nonborder['weights'][j] * \
((depths[I]-depth_nonborder['means'][j])**2) / \
depth_nonborder['sigmas'][j]**2
expNB += curv_nonborder['weights'][j] * \
((curvatures[I]-curv_nonborder['means'][j])**2) / \
curv_nonborder['sigmas'][j]**2
expNB = -expNB / 2
probs_nonborder[I] = probs_nonborder[I] + \
norm_nonborder[j] * np.exp(expNB)
likelihoods = probs_border / (probs_nonborder + probs_border + tiny)
likelihoods = likelihoods.tolist()
# ------------------------------------------------------------------------
# Return likelihoods and output file name
# ------------------------------------------------------------------------
if save_file:
likelihoods_file = os.path.join(os.getcwd(), 'likelihoods.vtk')
rewrite_scalars(depth_file, likelihoods_file, likelihoods,
'likelihoods', likelihoods, background_value)
if not os.path.exists(likelihoods_file):
raise IOError(likelihoods_file + " not found")
else:
likelihoods_file = None
return likelihoods, likelihoods_file
def rescale_by_label(input_vtk,
labels_or_file,
save_file=False,
output_filestring='rescaled_scalars'):
"""
Rescale scalars for each label (such as depth values within each fold).
Default is to normalize the scalar values of a VTK file by
a percentile value in each vertex's surface mesh for each label.
Parameters
----------
input_vtk : string
name of VTK file with a scalar value for each vertex
labels_or_file : list or string
label number for each vertex or name of VTK file with index scalars
save_file : Boolean
save output VTK file?
output_filestring : string (if save_file)
name of output file
Returns
-------
rescaled_scalars : list of floats
scalar values rescaled for each label, for label numbers not equal to -1
rescaled_scalars_file : string (if save_file)
name of output VTK file with rescaled scalar values for each label
Examples
--------
>>> # Rescale depths by neighborhood within each label:
>>> import os
>>> from mindboggle.guts.mesh import rescale_by_label
>>> from mindboggle.mio.vtks import read_scalars, rewrite_scalars
>>> from mindboggle.mio.plots import plot_surfaces
>>> path = os.environ['MINDBOGGLE_DATA']
>>> input_vtk = os.path.join(path, 'arno', 'shapes', 'lh.pial.travel_depth.vtk')
>>> labels_or_file = os.path.join(path, 'arno', 'features', 'subfolds.vtk')
>>> save_file = True
>>> output_filestring = 'rescaled_scalars'
>>> #
>>> rescaled_scalars, rescaled_scalars_file = rescale_by_label(input_vtk,
>>> labels_or_file, save_file, output_filestring)
>>> #
>>> # View rescaled scalar values per fold:
>>> folds_file = os.path.join(path, 'arno', 'features', 'folds.vtk')
>>> folds, name = read_scalars(folds_file)
>>> #
>>> rewrite_scalars(rescaled_scalars_file, rescaled_scalars_file,
>>> rescaled_scalars, 'rescaled_depths', folds)
>>> plot_surfaces(rescaled_scalars_file)
"""
import os
import numpy as np
from mindboggle.mio.vtks import read_scalars, rewrite_scalars
# Load scalars and vertex neighbor lists:
scalars, name = read_scalars(input_vtk, True, True)
print(" Rescaling scalar values within each label...")
# Load label numbers:
if isinstance(labels_or_file, str):
labels, name = read_scalars(labels_or_file, True, True)
elif isinstance(labels_or_file, list):
labels = labels_or_file
unique_labels = np.unique(labels)
unique_labels = [x for x in unique_labels if x >= 0]
# Loop through labels:
for label in unique_labels:
#print(" Rescaling scalar values within label {0} of {1} labels...".format(
# int(label), len(unique_labels)))
indices = [i for i, x in enumerate(labels) if x == label]
if indices:
# Rescale by the maximum label scalar value:
scalars[indices] = scalars[indices] / np.max(scalars[indices])
rescaled_scalars = scalars.tolist()
#-------------------------------------------------------------------------
# Return rescaled scalars and file name
#-------------------------------------------------------------------------
if save_file:
rescaled_scalars_file = os.path.join(os.getcwd(),
output_filestring + '.vtk')
rewrite_scalars(input_vtk, rescaled_scalars_file, rescaled_scalars,
'rescaled_scalars', labels)
if not os.path.exists(rescaled_scalars_file):
raise (IOError(rescaled_scalars_file + " not found"))
else:
rescaled_scalars_file = None
return rescaled_scalars, rescaled_scalars_file
def realign_boundaries_to_fundus_lines(surf_file,
init_label_file,
fundus_lines_file,
thickness_file,
out_label_file=None):
"""
Fix label boundaries to fundus lines.
Parameters
----------
surf_file : file containing the surface geometry in vtk format
init_label_file : file containing scalars that represent the
initial guess at labels
fundus_lines_file : file containing scalars representing fundus lines.
thickness_file: file containing cortical thickness scalar data
(for masking out the medial wall only)
out_label_file : if specified, the realigned labels will be writen to
this file
Returns
-------
numpy array representing the realigned label for each surface vertex.
"""
import numpy as np
from mindboggle.guts.segment import extract_borders
import mindboggle.guts.graph as go
from mindboggle.mio.vtks import read_vtk, read_scalars, write_vtk
from mindboggle.guts.mesh import find_neighbors
import propagate_fundus_lines
## read files
points, indices, lines, faces, scalars, scalar_names, num_points, \
input_vtk = read_vtk(surf_file, return_first=True, return_array=True)
indices = range(num_points)
init_labels, _ = read_scalars(init_label_file,
return_first=True,
return_array=True)
fundus_lines, _ = read_scalars(fundus_lines_file,
return_first=True,
return_array=True)
thickness, _ = read_scalars(thickness_file,
return_first=True,
return_array=True)
# remove labels from vertices with zero thickness (get around
# DKT40 annotations having the label '3' for all the Corpus
# Callosum vertices).
cc_inds = [x for x in indices if thickness[x] < 0.001]
init_labels[cc_inds] = 0
## setup seeds from initial label boundaries
neighbor_lists = find_neighbors(faces, num_points)
# extract all vertices that are on a boundary between labels
boundary_indices, label_pairs, _ = extract_borders(indices,
init_labels,
neighbor_lists,
return_label_pairs=True)
# split boundary vertices into segments with common boundary pairs.
boundary_segments = {}
for boundary_index, label_pair in zip(boundary_indices, label_pairs):
key = ((label_pair[0],
label_pair[1]) if label_pair[0] < label_pair[1] else
(label_pair[1], label_pair[0]))
if key not in boundary_segments:
boundary_segments[key] = []
boundary_segments[key].append(boundary_index)
boundary_matrix, boundary_matrix_keys = _build_boundary_matrix(
boundary_segments, num_points)
# build the affinity matrix
affinity_matrix = go.weight_graph(np.array(points),
indices,
np.array(faces),
sigma=10,
add_to_graph=False)
## propagate boundaries to fundus line vertices
learned_matrix = _propagate_labels(affinity_matrix, boundary_matrix,
boundary_indices, 100, 1)
# assign labels to fundus line vertices based on highest probability
new_boundaries = -1 * np.ones(init_labels.shape)
fundus_line_indices = [i for i, x in enumerate(fundus_lines) if x > 0.5]
# tile the surface into connected components delimited by fundus lines
closed_fundus_lines, _, _ = propagate_fundus_lines.propagate_fundus_lines(
points, faces, fundus_line_indices, thickness)
closed_fundus_line_indices = np.where(closed_fundus_lines > 0)[0]
# split surface into connected components
connected_component_faces = _remove_boundary_faces(
points, faces, closed_fundus_line_indices)
# label components based on most probable label assignment
new_labels = _label_components(connected_component_faces, num_points,
boundary_indices, learned_matrix,
boundary_matrix_keys)
# propagate new labels to fill holes
label_matrix, label_map = _build_label_matrix(new_labels)
new_learned_matrix = _propagate_labels(
affinity_matrix, label_matrix,
[i for i in range(num_points) if new_labels[i] >= 0], 100, 1)
# assign most probable labels
for idx in [i for i in range(num_points) if new_labels[i] == -1]:
max_idx = np.argmax(new_learned_matrix[idx])
new_labels[idx] = label_map[max_idx]
# save
if out_label_file is not None:
write_vtk(out_label_file,
points,
faces=faces,
scalars=[int(x) for x in new_labels],
scalar_type='int')
return new_labels
def rescale_by_neighborhood(input_vtk, indices=[], nedges=10, p=99,
set_max_to_1=True, save_file=False, output_filestring='rescaled_scalars',
background_value=-1):
"""
Rescale the scalar values of a VTK file by a percentile value
in each vertex's surface mesh neighborhood.
Parameters
----------
input_vtk : string
name of VTK file with a scalar value for each vertex
indices : list of integers (optional)
indices of scalars to normalize
nedges : integer
number or edges from vertex, defining the size of its neighborhood
p : float in range of [0,100]
percentile used to normalize each scalar
set_max_to_1 : Boolean
set all rescaled values greater than 1 to 1.0?
save_file : Boolean
save output VTK file?
output_filestring : string (if save_file)
name of output file
background_value : integer
background value
Returns
-------
rescaled_scalars : list of floats
rescaled scalar values
rescaled_scalars_file : string (if save_file)
name of output VTK file with rescaled scalar values
Examples
--------
>>> import os
>>> from mindboggle.guts.mesh import rescale_by_neighborhood
>>> from mindboggle.mio.vtks import read_scalars, rewrite_scalars
>>> from mindboggle.mio.plots import plot_surfaces
>>> path = os.environ['MINDBOGGLE_DATA']
>>> input_vtk = os.path.join(path, 'arno', 'shapes', 'lh.pial.travel_depth.vtk')
>>> indices = []
>>> nedges = 10
>>> p = 99
>>> set_max_to_1 = True
>>> save_file = True
>>> output_filestring = 'rescaled_scalars'
>>> background_value = -1
>>> #
>>> rescaled_scalars, rescaled_scalars_file = rescale_by_neighborhood(input_vtk,
>>> indices, nedges, p, set_max_to_1, save_file, output_filestring, background_value)
>>> #
>>> # View rescaled scalar values per fold:
>>> folds_file = os.path.join(path, 'arno', 'features', 'folds.vtk')
>>> folds, name = read_scalars(folds_file)
>>> #
>>> rewrite_scalars(rescaled_scalars_file, rescaled_scalars_file,
>>> rescaled_scalars, 'rescaled_depths', folds)
>>> plot_surfaces(rescaled_scalars_file)
"""
import os
import numpy as np
from mindboggle.mio.vtks import read_scalars, rewrite_scalars
from mindboggle.guts.mesh import find_neighbors_from_file, find_neighborhood
# Load scalars and vertex neighbor lists:
scalars, name = read_scalars(input_vtk, True, True)
if not indices:
indices = [i for i,x in enumerate(scalars) if x != background_value]
print(" Rescaling {0} scalar values by neighborhood...".format(len(indices)))
neighbor_lists = find_neighbors_from_file(input_vtk)
# Loop through vertices:
rescaled_scalars = scalars.copy()
for index in indices:
# Determine the scalars in the vertex's neighborhood:
neighborhood = find_neighborhood(neighbor_lists, [index], nedges)
# Compute a high neighborhood percentile to normalize vertex's value:
normalization_factor = np.percentile(scalars[neighborhood], p)
rescaled_scalar = scalars[index] / normalization_factor
rescaled_scalars[index] = rescaled_scalar
# Make any rescaled value greater than 1 equal to 1:
if set_max_to_1:
rescaled_scalars[[x for x in indices if rescaled_scalars[x] > 1.0]] = 1
rescaled_scalars = rescaled_scalars.tolist()
#-------------------------------------------------------------------------
# Return rescaled scalars and file name
#-------------------------------------------------------------------------
if save_file:
rescaled_scalars_file = os.path.join(os.getcwd(), output_filestring + '.vtk')
rewrite_scalars(input_vtk, rescaled_scalars_file,
rescaled_scalars, 'rescaled_scalars')
if not os.path.exists(rescaled_scalars_file):
raise(IOError(rescaled_scalars_file + " not found"))
else:
rescaled_scalars_file = None
return rescaled_scalars, rescaled_scalars_file
def extract_sulci(labels_file, folds_or_file, hemi, min_boundary=1,
sulcus_names=[]):
"""
Identify sulci from folds in a brain surface according to a labeling
protocol that includes a list of label pairs defining each sulcus.
A fold is a group of connected, deep vertices.
Steps for each fold ::
1. Remove fold if it has fewer than two labels.
2. Remove fold if its labels do not contain a sulcus label pair.
3. Find vertices with labels that are in only one of the fold's
label boundary pairs. Assign the vertices the sulcus with the label
pair if they are connected to the label boundary for that pair.
4. If there are remaining vertices, segment into sets of vertices
connected to label boundaries, and assign a unique ID to each set.
Parameters
----------
labels_file : string
file name for surface mesh VTK containing labels for all vertices
folds_or_file : list or string
fold number for each vertex / name of VTK file containing fold scalars
hemi : string
hemisphere abbreviation in {'lh', 'rh'} for sulcus labels
min_boundary : integer
minimum number of vertices for a sulcus label boundary segment
sulcus_names : list of strings
names of sulci
Returns
-------
sulci : list of integers
sulcus numbers for all vertices (-1 for non-sulcus vertices)
n_sulci : integers
number of sulci
sulci_file : string
output VTK file with sulcus numbers (-1 for non-sulcus vertices)
Examples
--------
>>> import os
>>> from mindboggle.mio.vtks import read_scalars, rewrite_scalars
>>> from mindboggle.features.sulci import extract_sulci
>>> from mindboggle.mio.plots import plot_surfaces
>>> path = os.environ['MINDBOGGLE_DATA']
>>> # Load labels, folds, neighbor lists, and sulcus names and label pairs
>>> labels_file = os.path.join(path, 'arno', 'labels', 'relabeled_lh.DKTatlas40.gcs.vtk')
>>> folds_file = os.path.join(path, 'arno', 'features', 'folds.vtk')
>>> folds_or_file, name = read_scalars(folds_file)
>>> hemi = 'lh'
>>> min_boundary = 10
>>> sulcus_names = []
>>> #
>>> sulci, n_sulci, sulci_file = extract_sulci(labels_file, folds_or_file, hemi, min_boundary, sulcus_names)
>>> # View:
>>> plot_surfaces('sulci.vtk')
"""
import os
from time import time
import numpy as np
from mindboggle.mio.vtks import read_scalars, read_vtk, rewrite_scalars
from mindboggle.guts.mesh import find_neighbors
from mindboggle.guts.segment import extract_borders, propagate, segment
from mindboggle.mio.labels import DKTprotocol
# Load fold numbers if folds_or_file is a string:
if isinstance(folds_or_file, str):
folds, name = read_scalars(folds_or_file)
elif isinstance(folds_or_file, list):
folds = folds_or_file
dkt = DKTprotocol()
if hemi == 'lh':
pair_lists = dkt.left_sulcus_label_pair_lists
elif hemi == 'rh':
pair_lists = dkt.right_sulcus_label_pair_lists
else:
print("Warning: hemisphere not properly specified ('lh' or 'rh').")
# Load points, faces, and neighbors:
faces, o1, o2, points, npoints, labels, o3, o4 = read_vtk(labels_file)
neighbor_lists = find_neighbors(faces, npoints)
# Array of sulcus IDs for fold vertices, initialized as -1.
# Since we do not touch gyral vertices and vertices whose labels
# are not in the label list, or vertices having only one label,
# their sulcus IDs will remain -1:
sulci = -1 * np.ones(npoints)
#-------------------------------------------------------------------------
# Loop through folds
#-------------------------------------------------------------------------
fold_numbers = [int(x) for x in np.unique(folds) if x != -1]
n_folds = len(fold_numbers)
print("Extract sulci from {0} folds...".format(n_folds))
t0 = time()
for n_fold in fold_numbers:
fold = [i for i,x in enumerate(folds) if x == n_fold]
len_fold = len(fold)
# List the labels in this fold:
fold_labels = [labels[x] for x in fold]
unique_fold_labels = [int(x) for x in np.unique(fold_labels)
if x != -1]
#---------------------------------------------------------------------
# NO MATCH -- fold has fewer than two labels
#---------------------------------------------------------------------
if len(unique_fold_labels) < 2:
# Ignore: sulci already initialized with -1 values:
if not unique_fold_labels:
print(" Fold {0} ({1} vertices): "
"NO MATCH -- fold has no labels".
format(n_fold, len_fold))
else:
print(" Fold {0} ({1} vertices): "
"NO MATCH -- fold has only one label ({2})".
format(n_fold, len_fold, unique_fold_labels[0]))
# Ignore: sulci already initialized with -1 values
else:
# Find all label boundary pairs within the fold:
indices_fold_pairs, fold_pairs, unique_fold_pairs = \
extract_borders(fold, labels, neighbor_lists,
ignore_values=[], return_label_pairs=True)
# Find fold label pairs in the protocol (pairs are already sorted):
fold_pairs_in_protocol = [x for x in unique_fold_pairs
if x in dkt.unique_sulcus_label_pairs]
if unique_fold_labels:
print(" Fold {0} labels: {1} ({2} vertices)".format(n_fold,
', '.join([str(x) for x in unique_fold_labels]),
len_fold))
#-----------------------------------------------------------------
# NO MATCH -- fold has no sulcus label pair
#-----------------------------------------------------------------
if not fold_pairs_in_protocol:
print(" Fold {0}: NO MATCH -- fold has no sulcus label pair".
format(n_fold, len_fold))
#-----------------------------------------------------------------
# Possible matches
#-----------------------------------------------------------------
else:
print(" Fold {0} label pairs in protocol: {1}".format(n_fold,
', '.join([str(x) for x in fold_pairs_in_protocol])))
# Labels in the protocol (includes repeats across label pairs):
labels_in_pairs = [x for lst in fold_pairs_in_protocol
for x in lst]
# Labels that appear in one or more sulcus label boundary:
unique_labels = []
nonunique_labels = []
for label in np.unique(labels_in_pairs):
if len([x for x in labels_in_pairs if x == label]) == 1:
unique_labels.append(label)
else:
nonunique_labels.append(label)
#-------------------------------------------------------------
# Vertices whose labels are in only one sulcus label pair
#-------------------------------------------------------------
# Find vertices with a label that is in only one of the fold's
# label pairs (the other label in the pair can exist in other
# pairs). Assign the vertices the sulcus with the label pair
# if they are connected to the label boundary for that pair.
#-------------------------------------------------------------
if unique_labels:
for pair in fold_pairs_in_protocol:
# If one or both labels in label pair is/are unique:
unique_labels_in_pair = [x for x in pair
if x in unique_labels]
n_unique = len(unique_labels_in_pair)
if n_unique:
ID = None
for i, pair_list in enumerate(pair_lists):
if not isinstance(pair_list, list):
pair_list = [pair_list]
if pair in pair_list:
ID = i
break
if ID:
# Seeds from label boundary vertices
# (fold_pairs and pair already sorted):
indices_pair = [x for i,x
in enumerate(indices_fold_pairs)
if fold_pairs[i] == pair]
# Vertices with unique label(s) in pair:
indices_unique_labels = [fold[i]
for i,x in enumerate(fold_labels)
if x in dkt.unique_sulcus_label_pairs]
# Propagate from seeds to labels in label pair:
sulci2 = segment(indices_unique_labels,
neighbor_lists,
min_region_size=1,
seed_lists=[indices_pair],
keep_seeding=False,
spread_within_labels=True,
labels=labels)
sulci[sulci2 != -1] = ID
# Print statement:
if n_unique == 1:
ps1 = '1 label'
else:
ps1 = 'Both labels'
if len(sulcus_names):
ps2 = sulcus_names[ID]
else:
ps2 = ''
print(" {0} unique to one fold pair: "
"{1} {2}".
format(ps1, ps2, unique_labels_in_pair))
#-------------------------------------------------------------
# Vertex labels shared by multiple label pairs
#-------------------------------------------------------------
# Propagate labels from label borders to vertices with labels
# that are shared by multiple label pairs in the fold.
#-------------------------------------------------------------
if len(nonunique_labels):
# For each label shared by different label pairs:
for label in nonunique_labels:
# Print statement:
print(" Propagate sulcus borders with label {0}".
format(int(label)))
# Construct seeds from label boundary vertices:
seeds = -1 * np.ones(len(points))
for ID, pair_list in enumerate(pair_lists):
if not isinstance(pair_list, list):
pair_list = [pair_list]
label_pairs = [x for x in pair_list if label in x]
for label_pair in label_pairs:
indices_pair = [x for i,x
in enumerate(indices_fold_pairs)
if np.sort(fold_pairs[i]).
tolist() == label_pair]
if indices_pair:
# Do not include short boundary segments:
if min_boundary > 1:
indices_pair2 = []
seeds2 = segment(indices_pair,
neighbor_lists)
useeds2 = [x for x in
np.unique(seeds2)
if x != -1]
for seed2 in useeds2:
iseed2 = [i for i,x
in enumerate(seeds2)
if x == seed2]
if len(iseed2) >= min_boundary:
indices_pair2.extend(iseed2)
else:
if len(iseed2) == 1:
print(" Remove "
"assignment "
"of ID {0} from "
"1 vertex".
format(seed2))
else:
print(" Remove "
"assignment "
"of ID {0} from "
"{1} vertices".
format(seed2,
len(iseed2)))
indices_pair = indices_pair2
# Assign sulcus IDs to seeds:
seeds[indices_pair] = ID
# Identify vertices with the label:
label_array = -1 * np.ones(len(points))
indices_label = [fold[i] for i,x
in enumerate(fold_labels)
if x == label]
if len(indices_label):
label_array[indices_label] = 1
# Propagate from seeds to vertices with label:
#indices_seeds = []
#for seed in range(int(max(seeds))+1):
# indices_seeds.append([i for i,x
# in enumerate(seeds)
# if x == seed])
#sulci2 = segment(indices_label, neighbor_lists,
# 50, indices_seeds, False, True,
# labels)
sulci2 = propagate(points, faces,
label_array, seeds, sulci,
max_iters=10000,
tol=0.001, sigma=5)
sulci[sulci2 != -1] = sulci2[sulci2 != -1]
#-------------------------------------------------------------------------
# Print out assigned sulci
#-------------------------------------------------------------------------
sulcus_numbers = [int(x) for x in np.unique(sulci) if x != -1]
# if not np.isnan(x)]
n_sulci = len(sulcus_numbers)
print("Extracted {0} sulci from {1} folds ({2:.1f}s):".
format(n_sulci, n_folds, time()-t0))
if sulcus_names:
for sulcus_number in sulcus_numbers:
print(" {0}: {1}".format(sulcus_number,
sulcus_names[sulcus_number]))
elif sulcus_numbers:
print(" " + ", ".join([str(x) for x in sulcus_numbers]))
#-------------------------------------------------------------------------
# Print out unresolved sulci
#-------------------------------------------------------------------------
unresolved = [i for i in range(len(pair_lists))
if i not in sulcus_numbers]
if len(unresolved) == 1:
print("The following sulcus is unaccounted for:")
else:
print("The following {0} sulci are unaccounted for:".
format(len(unresolved)))
if sulcus_names:
for sulcus_number in unresolved:
print(" {0}: {1}".format(sulcus_number,
sulcus_names[sulcus_number]))
else:
print(" " + ", ".join([str(x) for x in unresolved]))
#-------------------------------------------------------------------------
# Return sulci, number of sulci, and file name
#-------------------------------------------------------------------------
sulci = [int(x) for x in sulci]
sulci_file = os.path.join(os.getcwd(), 'sulci.vtk')
rewrite_scalars(labels_file, sulci_file, sulci, 'sulci', sulci)
if not os.path.exists(sulci_file):
raise(IOError(sulci_file + " not found"))
return sulci, n_sulci, sulci_file
def spectrum_from_file(vtk_file, spectrum_size=10, exclude_labels=[-1],
normalization=None, area_file=''):
"""
Compute Laplace-Beltrami spectrum of a 3D shape in a VTK file.
Parameters
----------
vtk_file : string
the input vtk file
spectrum_size : integer
number of eigenvalues to be computed (the length of the spectrum)
exclude_labels : list of integers
labels to be excluded
normalization : string
the method used to normalize eigenvalues ('area' or None)
if "area", use area of the 2D structure as in Reuter et al. 2006
area_file : string
name of VTK file with surface area scalar values
Returns
-------
spectrum : list of floats
first spectrum_size of Laplace-Beltrami spectrum
Examples
--------
>>> # Spectrum for entire left hemisphere of Twins-2-1:
>>> import os
>>> from mindboggle.shapes.laplace_beltrami import spectrum_from_file
>>> path = os.environ['MINDBOGGLE_DATA']
>>> vtk_file = os.path.join(path, 'arno', 'labels', 'lh.labels.DKT25.manual.vtk')
>>> spectrum_from_file(vtk_file, spectrum_size=6)
[4.829758648026223e-18,
0.00012841730024671977,
0.0002715181572272744,
0.00032051508471594173,
0.000470162807048644,
0.0005768904023010327]
>>> # Spectrum for Twins-2-1 left postcentral pial surface (22)
>>> # (after running explode_scalars() with reindex=True):
>>> import os
>>> from mindboggle.shapes.laplace_beltrami import spectrum_from_file
>>> path = os.environ['MINDBOGGLE_DATA']
>>> vtk_file = os.path.join(path, 'arno', 'labels', 'label22.vtk')
>>> spectrum_from_file(vtk_file, spectrum_size=6)
[6.3469513010430304e-18,
0.0005178862383467463,
0.0017434911095630772,
0.003667561767487686,
0.005429017880363784,
0.006309346984678924]
>>> # Loop thru all MB 101 brains
>>> from mindboggle.shapes.laplace_beltrami import spectrum_from_file
>>> for hemidir in os.listdir(header):
>>> print hemidir
>>> sulci_file = os.path.join(header, hemidir, "sulci.vtk")
>>> spectrum = spectrum_from_file(sulci_file)
"""
from mindboggle.mio.vtks import read_vtk, read_scalars
from mindboggle.shapes.laplace_beltrami import spectrum_of_largest
faces, u1, u2, points, u4, u5, u6, u7 = read_vtk(vtk_file)
# Area file:
if area_file:
areas, u1 = read_scalars(area_file)
else:
areas = None
spectrum = spectrum_of_largest(points, faces, spectrum_size,
exclude_labels, normalization, areas)
return spectrum
def write_face_vertex_averages(input_file, output_table='', area_file=''):
"""
Make table of average vertex values per face
(divided by face area if area_file provided).
Parameters
----------
input_file : string
name of VTK file with scalars to average
area_file : string
name of VTK file with surface area scalar values
output_table : string
output table filename
Returns
-------
output_table : string
output table filename
Examples
--------
>>> from mindboggle.mio.tables import write_face_vertex_averages
>>> from mindboggle.mio.fetch_data import prep_tests
>>> urls, fetch_data = prep_tests()
>>> input_file = fetch_data(urls['left_travel_depth'], '', '.vtk')
>>> area_file = fetch_data(urls['left_area'], '', '.vtk')
>>> output_table = ''
>>> output_table = write_face_vertex_averages(input_file, output_table,
... area_file)
"""
import os
import numpy as np
import pandas as pd
from mindboggle.mio.vtks import read_vtk, read_scalars
points, indices, lines, faces, scalars, scalar_names, \
npoints, input_vtk = read_vtk(input_file, True, True)
if area_file:
area_scalars, name = read_scalars(area_file, True, True)
# --------------------------------------------------------------------
# For each face, average vertex values:
# --------------------------------------------------------------------
columns = []
for face in faces:
values = []
for index in face:
if area_file:
values.append(scalars[index] / area_scalars[index])
else:
values.append(scalars[index])
columns.append(np.mean(values))
# ----------------------------------------------------------------
# Write to table:
# ----------------------------------------------------------------
if not output_table:
output_table = os.path.join(os.getcwd(), 'average_face_values.csv')
df = pd.DataFrame({'': columns})
df.to_csv(output_table, index=False, encoding='utf-8')
if not os.path.exists(output_table):
raise IOError(output_table + " not found")
return output_table
def concatenate_sulcus_scalars(scalar_files, fold_files, label_files,
background_value=-1):
"""
Prepare data for estimating scalar distributions along and outside fundi.
Extract (e.g., depth, curvature) scalar values in folds, along sulcus
label boundaries as well as outside the sulcus label boundaries.
Concatenate these scalar values across multiple files.
Parameters
----------
scalar_files : list of strings
names of surface mesh VTK files with scalar values to concatenate
fold_files : list of strings (corr. to each list in scalar_files)
VTK files with fold numbers as scalars (-1 for non-fold vertices)
label_files : list of strings (corr. to fold_files)
VTK files with label numbers (-1 for unlabeled vertices)
background_value : integer or float
background value
Returns
-------
border_scalars : list of floats
concatenated scalar values within folds along sulcus label boundaries
nonborder_scalars : list of floats
concatenated scalar values within folds outside sulcus label boundaries
Examples
--------
>>> # Concatenate (duplicate) depth scalars:
>>> import numpy as np
>>> from mindboggle.shapes.likelihood import concatenate_sulcus_scalars
>>> from mindboggle.mio.fetch_data import prep_tests
>>> urls, fetch_data = prep_tests()
>>> depth_file = fetch_data(urls['left_travel_depth'], '', '.vtk')
>>> labels_file = fetch_data(urls['left_freesurfer_labels'], '', '.vtk')
>>> folds_file = fetch_data(urls['left_folds'], '', '.vtk')
>>> scalar_files = [depth_file, depth_file]
>>> fold_files = [folds_file, folds_file]
>>> label_files = [labels_file, labels_file]
>>> background_value = -1
>>> border, nonborder = concatenate_sulcus_scalars(scalar_files,
... fold_files, label_files, background_value)
>>> print(np.array_str(np.array(border[0:5]),
... precision=5, suppress_small=True))
[ 3.48284 2.57157 4.27596 4.56549 3.84881]
>>> print(np.array_str(np.array(nonborder[0:5]),
... precision=5, suppress_small=True))
[ 2.87204 2.89388 3.55364 2.81681 3.70736]
"""
import numpy as np
from mindboggle.mio.vtks import read_scalars
from mindboggle.guts.mesh import find_neighbors_from_file
from mindboggle.guts.segment import extract_borders
from mindboggle.mio.labels import DKTprotocol
dkt = DKTprotocol()
# Prepare (non-unique) list of sulcus label pairs:
protocol_label_pairs = [x for lst in dkt.sulcus_label_pair_lists
for x in lst]
border_scalars = []
nonborder_scalars = []
# Loop through files with the scalar values:
for ifile, scalar_file in enumerate(scalar_files):
#print(scalar_file)
# Load scalars, folds, and labels:
folds_file = fold_files[ifile]
labels_file = label_files[ifile]
scalars, name = read_scalars(scalar_file, True, True)
if scalars.shape:
folds, name = read_scalars(folds_file)
labels, name = read_scalars(labels_file)
indices_folds = [i for i,x in enumerate(folds)
if x != background_value]
neighbor_lists = find_neighbors_from_file(labels_file)
# Find all label border pairs within the folds:
indices_label_pairs, label_pairs, unique_pairs = extract_borders(
indices_folds, labels, neighbor_lists, ignore_values=[-1],
return_label_pairs=True)
indices_label_pairs = np.array(indices_label_pairs)
# Find vertices with label pairs in the sulcus labeling protocol:
Ipairs_in_protocol = [i for i,x in enumerate(label_pairs)
if x in protocol_label_pairs]
indices_label_pairs = indices_label_pairs[Ipairs_in_protocol]
indices_outside_pairs = list(frozenset(indices_folds).difference(
indices_label_pairs))
# Store scalar values in folds along label border pairs:
border_scalars.extend(scalars[indices_label_pairs].tolist())
# Store scalar values in folds outside label border pairs:
nonborder_scalars.extend(scalars[indices_outside_pairs].tolist())
return border_scalars, nonborder_scalars
def evaluate_deep_features(features_file, labels_file, sulci_file='', hemi='',
excludeIDs=[-1], output_vtk_name='', verbose=True):
"""
Evaluate deep surface features by computing the minimum distance from each
label border vertex to all of the feature vertices in the same sulcus,
and from each feature vertex to all of the label border vertices in the
same sulcus. The label borders run along the deepest parts of sulci
and correspond to fundi in the DKT cortical labeling protocol.
Parameters
----------
features_file : string
VTK surface file with feature numbers for vertex scalars
labels_file : string
VTK surface file with label numbers for vertex scalars
sulci_file : string
VTK surface file with sulcus numbers for vertex scalars
excludeIDs : list of integers
feature/sulcus/label IDs to exclude (background set to -1)
output_vtk_name : Boolean
if not empty, output a VTK file beginning with output_vtk_name that
contains a surface with mean distances as scalars
verbose : Boolean
print mean distances to standard output?
Returns
-------
feature_to_border_mean_distances : numpy array [number of features x 1]
mean distance from each feature to sulcus label border
feature_to_border_sd_distances : numpy array [number of features x 1]
standard deviations of feature-to-border distances
feature_to_border_distances_vtk : string
VTK surface file containing feature-to-border distances
border_to_feature_mean_distances : numpy array [number of features x 1]
mean distances from each sulcus label border to feature
border_to_feature_sd_distances : numpy array [number of features x 1]
standard deviations of border-to-feature distances
border_to_feature_distances_vtk : string
VTK surface file containing border-to-feature distances
"""
import os
import sys
import numpy as np
from mindboggle.mio.vtks import read_vtk, read_scalars, write_vtk
from mindboggle.guts.mesh import find_neighbors, remove_faces
from mindboggle.guts.segment import extract_borders
from mindboggle.guts.compute import source_to_target_distances
from mindboggle.mio.labels import DKTprotocol
dkt = DKTprotocol()
#-------------------------------------------------------------------------
# Load labels, features, and sulci:
#-------------------------------------------------------------------------
faces, lines, indices, points, npoints, labels, scalar_names, \
input_vtk = read_vtk(labels_file, True, True)
features, name = read_scalars(features_file, True, True)
if sulci_file:
sulci, name = read_scalars(sulci_file, True, True)
# List of indices to sulcus vertices:
sulcus_indices = [i for i,x in enumerate(sulci) if x != -1]
segmentIDs = sulci
sulcus_faces = remove_faces(faces, sulcus_indices)
else:
sulcus_indices = range(len(labels))
segmentIDs = []
sulcus_faces = faces
#-------------------------------------------------------------------------
# Prepare neighbors, label pairs, border IDs, and outputs:
#-------------------------------------------------------------------------
# Calculate neighbor lists for all points:
print('Find neighbors for all vertices...')
neighbor_lists = find_neighbors(faces, npoints)
# Find label border points in any of the sulci:
print('Find label border points in any of the sulci...')
border_indices, border_label_tuples, unique_border_label_tuples = \
extract_borders(sulcus_indices, labels, neighbor_lists,
ignore_values=[], return_label_pairs=True)
if not len(border_indices):
sys.exit('There are no label border points!')
# Initialize an array of label border IDs
# (label border vertices that define sulci in the labeling protocol):
print('Build an array of label border IDs...')
label_borders = -1 * np.ones(npoints)
if hemi == 'lh':
nsulcus_lists = len(dkt.left_sulcus_label_pair_lists)
else:
nsulcus_lists = len(dkt.right_sulcus_label_pair_lists)
feature_to_border_mean_distances = -1 * np.ones(nsulcus_lists)
feature_to_border_sd_distances = -1 * np.ones(nsulcus_lists)
border_to_feature_mean_distances = -1 * np.ones(nsulcus_lists)
border_to_feature_sd_distances = -1 * np.ones(nsulcus_lists)
feature_to_border_distances_vtk = ''
border_to_feature_distances_vtk = ''
#-------------------------------------------------------------------------
# Loop through sulci:
#-------------------------------------------------------------------------
# For each list of sorted label pairs (corresponding to a sulcus):
for isulcus, label_pairs in enumerate(dkt.sulcus_label_pair_lists):
# Keep the border points with label pair labels:
label_pair_border_indices = [x for i,x in enumerate(border_indices)
if np.unique(border_label_tuples[i]).tolist()
in label_pairs]
# Store the points as sulcus IDs in the border IDs array:
if label_pair_border_indices:
label_borders[label_pair_border_indices] = isulcus
if len(np.unique(label_borders)) > 1:
#---------------------------------------------------------------------
# Construct a feature-to-border distance matrix and VTK file:
#---------------------------------------------------------------------
# Construct a distance matrix:
print('Construct a feature-to-border distance matrix...')
sourceIDs = features
targetIDs = label_borders
distances, distance_matrix = source_to_target_distances(
sourceIDs, targetIDs, points, segmentIDs, excludeIDs)
# Compute mean distances for each feature:
nfeatures = min(np.shape(distance_matrix)[1], nsulcus_lists)
for ifeature in range(nfeatures):
feature_distances = [x for x in distance_matrix[:, ifeature]
if x != -1]
feature_to_border_mean_distances[ifeature] = \
np.mean(feature_distances)
feature_to_border_sd_distances[ifeature] = \
np.std(feature_distances)
if verbose:
print('Feature-to-border mean distances:')
print(feature_to_border_mean_distances)
print('Feature-to-border standard deviations of distances:')
print(feature_to_border_sd_distances)
# Write resulting feature-label border distances to VTK file:
if output_vtk_name:
feature_to_border_distances_vtk = os.path.join(os.getcwd(),
output_vtk_name + '_feature_to_border_mean_distances.vtk')
print('Write feature-to-border distances to {0}...'.
format(feature_to_border_distances_vtk))
write_vtk(feature_to_border_distances_vtk, points,
[], [], sulcus_faces, [distances],
['feature-to-border_distances'], 'float')
#---------------------------------------------------------------------
# Construct a border-to-feature distance matrix and VTK file:
#---------------------------------------------------------------------
# Construct a distance matrix:
print('Construct a border-to-feature distance matrix...')
sourceIDs = label_borders
targetIDs = features
distances, distance_matrix = source_to_target_distances(
sourceIDs, targetIDs, points, segmentIDs, excludeIDs)
# Compute mean distances for each feature:
nfeatures = min(np.shape(distance_matrix)[1], nsulcus_lists)
for ifeature in range(nfeatures):
border_distances = [x for x in distance_matrix[:, ifeature]
if x != -1]
border_to_feature_mean_distances[ifeature] = \
np.mean(border_distances)
border_to_feature_sd_distances[ifeature] = \
np.std(border_distances)
if verbose:
print('border-to-feature mean distances:')
print(border_to_feature_mean_distances)
print('border-to-feature standard deviations of distances:')
print(border_to_feature_sd_distances)
# Write resulting feature-label border distances to VTK file:
if output_vtk_name:
border_to_feature_distances_vtk = os.path.join(os.getcwd(),
output_vtk_name + '_border_to_feature_mean_distances.vtk')
print('Write border-to-feature distances to {0}...'.
format(border_to_feature_distances_vtk))
write_vtk(border_to_feature_distances_vtk, points,
[], [], sulcus_faces, [distances],
['border-to-feature_distances'], 'float')
#-------------------------------------------------------------------------
# Return outputs:
#-------------------------------------------------------------------------
return feature_to_border_mean_distances, feature_to_border_sd_distances,\
feature_to_border_distances_vtk,\
border_to_feature_mean_distances, border_to_feature_sd_distances,\
border_to_feature_distances_vtk
def spectrum_from_file(vtk_file,
spectrum_size=10,
exclude_labels=[-1],
normalization="areaindex",
area_file='',
verbose=False):
"""
Compute Laplace-Beltrami spectrum of a 3D shape in a VTK file.
Parameters
----------
vtk_file : string
the input vtk file
spectrum_size : integer
number of eigenvalues to be computed (the length of the spectrum)
exclude_labels : list of integers
labels to be excluded
normalization : string
the method used to normalize eigenvalues
if None, no normalization is used
if "area", use area of the 2D structure as in Reuter et al. 2006
if "index", divide eigenvalue by index to account for linear trend
if "areaindex", do both (default)
area_file : string
name of VTK file with surface area scalar values
verbose : bool
print statements?
Returns
-------
spectrum : list of floats
first spectrum_size of Laplace-Beltrami spectrum
Examples
--------
>>> # Spectrum for entire left hemisphere of Twins-2-1:
>>> import numpy as np
>>> from mindboggle.shapes.laplace_beltrami import spectrum_from_file
>>> from mindboggle.shapes.laplace_beltrami import spectrum_per_label
>>> from mindboggle.mio.fetch_data import prep_tests
>>> urls, fetch_data = prep_tests()
>>> vtk_file = fetch_data(urls['left_freesurfer_labels'], '', '.vtk')
>>> spectrum = spectrum_from_file(vtk_file, spectrum_size=6,
... exclude_labels=[-1], normalization=None, area_file="", verbose=False)
>>> [np.float("{0:.{1}f}".format(x, 5)) for x in spectrum[1::]]
[0.00013, 0.00027, 0.00032, 0.00047, 0.00058]
>>> spectrum = spectrum_from_file(vtk_file, spectrum_size=6,
... exclude_labels=[-1], normalization="areaindex", area_file="",
... verbose=False)
>>> [np.float("{0:.{1}f}".format(x, 5)) for x in spectrum[1::]]
[14.12801, 14.93573, 11.75397, 12.93141, 12.69348]
"""
from mindboggle.mio.vtks import read_vtk, read_scalars
from mindboggle.shapes.laplace_beltrami import spectrum_of_largest
points, indices, lines, faces, scalars, scalar_names, npoints, \
input_vtk = read_vtk(vtk_file)
# Area file:
if area_file:
areas, u1 = read_scalars(area_file)
else:
areas = None
spectrum = spectrum_of_largest(points, faces, spectrum_size,
exclude_labels, normalization, areas,
verbose)
return spectrum
def write_shape_stats(labels_or_file=[], sulci=[], fundi=[],
affine_transform_files=[], inverse_booleans=[], transform_format='itk',
area_file='', normalize_by_area=False, mean_curvature_file='',
travel_depth_file='', geodesic_depth_file='',
freesurfer_thickness_file='', freesurfer_curvature_file='',
freesurfer_sulc_file='',
labels_spectra=[], labels_spectra_IDs=[],
sulci_spectra=[], sulci_spectra_IDs=[],
labels_zernike=[], labels_zernike_IDs=[],
sulci_zernike=[], sulci_zernike_IDs=[],
exclude_labels=[-1]):
"""
Make tables of shape statistics per label, sulcus, and/or fundus.
Note ::
This function is tailored for Mindboggle outputs.
Parameters
----------
labels_or_file : list or string
label number for each vertex or name of VTK file with index scalars
sulci : list of integers
indices to sulci, one per vertex, with -1 indicating no sulcus
fundi : list of integers
indices to fundi, one per vertex, with -1 indicating no fundus
affine_transform_files : list of strings
affine transform files to standard space
inverse_booleans : list of of zeros and ones
for each transform, 1 to take the inverse, else 0
transform_format : string
format for transform file
Ex: 'txt' for text, 'itk' for ITK, and 'mat' for Matlab format
area_file : string
name of VTK file with surface area scalar values
normalize_by_area : Boolean
normalize all shape measures by area of label/feature? (UNTESTED)
mean_curvature_file : string
name of VTK file with mean curvature scalar values
travel_depth_file : string
name of VTK file with travel depth scalar values
geodesic_depth_file : string
name of VTK file with geodesic depth scalar values
freesurfer_thickness_file : string
name of VTK file with FreeSurfer thickness scalar values
freesurfer_curvature_file : string
name of VTK file with FreeSurfer curvature (curv) scalar values
freesurfer_sulc_file : string
name of VTK file with FreeSurfer convexity (sulc) scalar values
labels_spectra : list of lists of floats
Laplace-Beltrami spectra for each labeled region
labels_spectra_IDs : list of integers
unique labels for labels_spectra
sulci_spectra : list of lists of floats
Laplace-Beltrami spectra for each sulcus
sulci_spectra_IDs : list of integers
unique sulcus IDs for sulci_spectra
labels_zernike : list of lists of floats
Zernike moments for each labeled region
labels_zernike_IDs : list of integers
unique labels for labels_zernike
sulci_zernike : list of lists of floats
Zernike moments for each sulcus
sulci_zernike_IDs : list of integers
unique sulcus IDs for sulci_zernike
exclude_labels : list of lists of integers
indices to be excluded (in addition to -1)
Returns
-------
label_table : string
output table filename for label shapes
sulcus_table : string
output table filename for sulcus shapes
fundus_table : string
output table filename for fundus shapes
Examples
--------
>>> import os
>>> from mindboggle.mio.vtks import read_scalars
>>> from mindboggle.mio.tables import write_shape_stats
>>> path = '/homedir/mindboggled/Twins-2-1'
>>> labels_or_file = os.path.join(path, 'labels', 'left_cortical_surface', 'freesurfer_cortex_labels.vtk')
>>> sulci_file = os.path.join(path, 'features', 'left_cortical_surface', 'sulci.vtk')
>>> fundi_file = os.path.join(path, 'features', 'left_cortical_surface', 'fundus_per_sulcus.vtk')
>>> sulci, name = read_scalars(sulci_file)
>>> fundi, name = read_scalars(fundi_file)
>>> affine_transform_files = [] #os.path.join(path, 'arno', 'mri', 't1weighted_brain.MNI152Affine.txt')
>>> inverse_booleans = []
>>> #transform_format = 'mat'
>>> transform_format = 'itk'
>>> area_file = os.path.join(path, 'shapes', 'left_cortical_surface', 'area.vtk')
>>> normalize_by_area = False
>>> mean_curvature_file = os.path.join(path, 'shapes', 'left_cortical_surface', 'mean_curvature.vtk')
>>> travel_depth_file = os.path.join(path, 'shapes', 'left_cortical_surface', 'travel_depth.vtk')
>>> geodesic_depth_file = os.path.join(path, 'shapes', 'left_cortical_surface', 'geodesic_depth.vtk')
>>> freesurfer_thickness_file = ''
>>> freesurfer_curvature_file = ''
>>> freesurfer_sulc_file = ''
>>> #
>>> labels, name = read_scalars(labels_or_file)
>>> labels_spectra = []
>>> labels_spectra_IDs = []
>>> sulci_spectra = []
>>> sulci_spectra_IDs = []
>>> labels_zernike = []
>>> labels_zernike_IDs = []
>>> sulci_zernike = []
>>> sulci_zernike_IDs = []
>>> exclude_labels = [-1]
>>> #
>>> write_shape_stats(labels_or_file, sulci, fundi,
>>> affine_transform_files, inverse_booleans, transform_format,
>>> area_file, normalize_by_area,
>>> mean_curvature_file, travel_depth_file, geodesic_depth_file,
>>> freesurfer_thickness_file, freesurfer_curvature_file,
>>> freesurfer_sulc_file,
>>> labels_spectra, labels_spectra_IDs,
>>> sulci_spectra, sulci_spectra_IDs,
>>> labels_zernike, labels_zernike_IDs,
>>> sulci_zernike, sulci_zernike_IDs,
>>> exclude_labels)
"""
import os
import numpy as np
import pandas as pd
from mindboggle.guts.compute import means_per_label, stats_per_label, \
sum_per_label
from mindboggle.mio.vtks import read_scalars, read_vtk, \
apply_affine_transforms
from mindboggle.mio.labels import DKTprotocol
dkt = DKTprotocol()
# Make sure inputs are lists:
if isinstance(labels_or_file, np.ndarray):
labels = [int(x) for x in labels_or_file]
elif isinstance(labels_or_file, list):
labels = labels_or_file
elif isinstance(labels_or_file, str):
labels, name = read_scalars(labels_or_file)
if isinstance(sulci, np.ndarray):
sulci = [int(x) for x in sulci]
if isinstance(fundi, np.ndarray):
fundi = [int(x) for x in fundi]
if not labels and not sulci and not fundi:
import sys
sys.exit('No feature data to tabulate in write_shape_stats().')
spectrum_start = 1 # Store all columns of spectral components (0),
# or start from higher frequency components (>=1)
#-------------------------------------------------------------------------
# Feature lists, shape names, and shape files:
#-------------------------------------------------------------------------
# Feature lists:
feature_lists = [labels, sulci, fundi]
feature_names = ['label', 'sulcus', 'fundus']
spectra_lists = [labels_spectra, sulci_spectra]
spectra_ID_lists = [labels_spectra_IDs, sulci_spectra_IDs]
zernike_lists = [labels_zernike, sulci_zernike]
zernike_ID_lists = [labels_zernike_IDs, sulci_zernike_IDs]
table_names = ['label_shapes.csv', 'sulcus_shapes.csv',
'fundus_shapes.csv']
# Shape names corresponding to shape files below:
shape_names = ['area', 'travel depth', 'geodesic depth',
'mean curvature', 'freesurfer curvature',
'freesurfer thickness', 'freesurfer convexity (sulc)']
# Load shape files as a list of numpy arrays of per-vertex shape values:
shape_files = [area_file, travel_depth_file, geodesic_depth_file,
mean_curvature_file, freesurfer_curvature_file,
freesurfer_thickness_file, freesurfer_sulc_file]
shape_arrays = []
first_pass = True
area_array = []
for ishape, shape_file in enumerate(shape_files):
if os.path.exists(shape_file):
if first_pass:
points, indices, lines, faces, scalars_array, scalar_names, \
npoints, input_vtk = read_vtk(shape_file, True, True)
points = np.array(points)
first_pass = False
if affine_transform_files and transform_format:
affine_points, \
foo1 = apply_affine_transforms(affine_transform_files,
inverse_booleans, transform_format,
points, vtk_file_stem='')
else:
scalars_array, name = read_scalars(shape_file, True, True)
if scalars_array.size:
shape_arrays.append(scalars_array)
# Store area array:
if ishape == 0:
area_array = scalars_array.copy()
if normalize_by_area:
use_area = area_array
else:
use_area = []
# Initialize table file names:
label_table = ''
sulcus_table = ''
fundus_table = ''
# Loop through features / tables:
for itable, feature_list in enumerate(feature_lists):
column_names = []
#-----------------------------------------------------------------
# Label names:
#-----------------------------------------------------------------
label_title = 'name'
if itable == 0:
label_numbers = dkt.cerebrum_cortex_DKT31_numbers
label_names = dkt.cerebrum_cortex_DKT31_names
elif itable in [1, 2]:
label_numbers = dkt.sulcus_numbers
label_names = dkt.sulcus_names
else:
label_numbers = []
label_names = []
include_labels = label_numbers
nlabels = len(label_numbers)
#---------------------------------------------------------------------
# For each feature, construct a table of average shape values:
#---------------------------------------------------------------------
if feature_list:
feature_name = feature_names[itable]
columns = []
#-----------------------------------------------------------------
# Loop through shape measures:
#-----------------------------------------------------------------
column_names.extend(column_names[:])
for ishape, shape_array in enumerate(shape_arrays):
shape = shape_names[ishape]
print(' Compute statistics on {0} {1}...'.
format(feature_name, shape))
#-------------------------------------------------------------
# Append feature areas to columns:
#-------------------------------------------------------------
if ishape == 0 and np.size(area_array):
sums, label_list = sum_per_label(shape_array,
feature_list, include_labels, exclude_labels)
column_names.append(shape)
columns.append(sums)
#-------------------------------------------------------------
# Append feature shape statistics to columns:
#-------------------------------------------------------------
else:
medians, mads, means, sdevs, skews, kurts, \
lower_quarts, upper_quarts, \
label_list = stats_per_label(shape_array, feature_list,
include_labels, exclude_labels,
area_array, precision=1)
column_names.append(shape + ': median')
column_names.append(shape + ': MAD')
column_names.append(shape + ': mean')
column_names.append(shape + ': SD')
column_names.append(shape + ': skew')
column_names.append(shape + ': kurtosis')
column_names.append(shape + ': 25%')
column_names.append(shape + ': 75%')
columns.append(medians)
columns.append(mads)
columns.append(means)
columns.append(sdevs)
columns.append(skews)
columns.append(kurts)
columns.append(lower_quarts)
columns.append(upper_quarts)
#-----------------------------------------------------------------
# Mean positions in the original space:
#-----------------------------------------------------------------
# Compute mean position per feature:
positions, sdevs, label_list, foo = means_per_label(points,
feature_list, include_labels, exclude_labels, use_area)
# Append mean x,y,z position per feature to columns:
xyz_positions = np.asarray(positions)
for ixyz, xyz in enumerate(['x','y','z']):
column_names.append('mean position: {0}'.format(xyz))
columns.append(xyz_positions[:, ixyz].tolist())
#-----------------------------------------------------------------
# Mean positions in standard space:
#-----------------------------------------------------------------
if affine_transform_files and transform_format:
# Compute standard space mean position per feature:
standard_positions, sdevs, label_list, \
foo = means_per_label(affine_points,
feature_list, include_labels, exclude_labels, use_area)
# Append standard space x,y,z position per feature to columns:
xyz_std_positions = np.asarray(standard_positions)
for ixyz, xyz in enumerate(['x','y','z']):
column_names.append('mean position in standard space:'
' {0}'.format(xyz))
columns.append(xyz_std_positions[:, ixyz].tolist())
#-----------------------------------------------------------------
# Laplace-Beltrami spectra:
#-----------------------------------------------------------------
if itable in [0, 1]:
spectra = spectra_lists[itable]
if spectra:
spectra_IDs = spectra_ID_lists[itable]
# Construct a matrix of spectra:
len_spectrum = len(spectra[0])
spectrum_matrix = np.zeros((nlabels, len_spectrum))
for ilabel, label in enumerate(include_labels):
if label in spectra_IDs:
spectrum = spectra[spectra_IDs.index(label)]
spectrum_matrix[ilabel, 0:len_spectrum] = spectrum
# Append spectral shape name and values to columns:
for ispec in range(spectrum_start, len_spectrum):
columns.append(spectrum_matrix[:, ispec].tolist())
column_names.append('Laplace-Beltrami spectrum:'
' component {0}'.format(ispec+1))
#-----------------------------------------------------------------
# Zernike moments:
#-----------------------------------------------------------------
if itable in [0, 1]:
zernike = zernike_lists[itable]
if zernike:
zernike_IDs = zernike_ID_lists[itable]
# Construct a matrix of Zernike moments:
len_moments = len(zernike[0])
moments_matrix = np.zeros((nlabels, len_moments))
for ilabel, label in enumerate(include_labels):
if label in zernike_IDs:
moments = zernike[zernike_IDs.index(label)]
moments_matrix[ilabel, 0:len_moments] = moments
# Append Zernike shape name and values to columns:
for imoment in range(0, len_moments):
columns.append(moments_matrix[:, imoment].tolist())
column_names.append('Zernike moments: component {0}'.
format(imoment+1))
#-----------------------------------------------------------------
# Write labels/IDs and values to table:
#-----------------------------------------------------------------
# Write labels/IDs to table:
output_table = os.path.join(os.getcwd(), table_names[itable])
if columns:
df1 = pd.DataFrame({'ID': label_numbers})
df2 = pd.DataFrame(np.transpose(columns),
columns = column_names)
df = pd.concat([df1, df2], axis=1)
if label_names:
df0 = pd.DataFrame({'name': label_names})
df = pd.concat([df0, df], axis=1)
df.to_csv(output_table, index=False)
if not os.path.exists(output_table):
raise(IOError(output_table + " not found"))
#-----------------------------------------------------------------
# Return correct table file name:
#-----------------------------------------------------------------
if itable == 0:
label_table = output_table
elif itable == 1:
sulcus_table = output_table
elif itable == 2:
fundus_table = output_table
return label_table, sulcus_table, fundus_table
def rescale_by_label(input_vtk, labels_or_file, save_file=False,
output_filestring='rescaled_scalars'):
"""
Rescale scalars for each label (such as depth values within each fold).
Default is to normalize the scalar values of a VTK file by
a percentile value in each vertex's surface mesh for each label.
Parameters
----------
input_vtk : string
name of VTK file with a scalar value for each vertex
labels_or_file : list or string
label number for each vertex or name of VTK file with index scalars
save_file : Boolean
save output VTK file?
output_filestring : string (if save_file)
name of output file
Returns
-------
rescaled_scalars : list of floats
scalar values rescaled for each label, for label numbers not equal to -1
rescaled_scalars_file : string (if save_file)
name of output VTK file with rescaled scalar values for each label
Examples
--------
>>> # Rescale depths by neighborhood within each label:
>>> import os
>>> from mindboggle.guts.mesh import rescale_by_label
>>> from mindboggle.mio.vtks import read_scalars, rewrite_scalars
>>> from mindboggle.mio.plots import plot_surfaces
>>> path = os.environ['MINDBOGGLE_DATA']
>>> input_vtk = os.path.join(path, 'arno', 'shapes', 'lh.pial.travel_depth.vtk')
>>> labels_or_file = os.path.join(path, 'arno', 'features', 'subfolds.vtk')
>>> save_file = True
>>> output_filestring = 'rescaled_scalars'
>>> #
>>> rescaled_scalars, rescaled_scalars_file = rescale_by_label(input_vtk,
>>> labels_or_file, save_file, output_filestring)
>>> #
>>> # View rescaled scalar values per fold:
>>> folds_file = os.path.join(path, 'arno', 'features', 'folds.vtk')
>>> folds, name = read_scalars(folds_file)
>>> #
>>> rewrite_scalars(rescaled_scalars_file, rescaled_scalars_file,
>>> rescaled_scalars, 'rescaled_depths', folds)
>>> plot_surfaces(rescaled_scalars_file)
"""
import os
import numpy as np
from mindboggle.mio.vtks import read_scalars, rewrite_scalars
# Load scalars and vertex neighbor lists:
scalars, name = read_scalars(input_vtk, True, True)
print(" Rescaling scalar values within each label...")
# Load label numbers:
if isinstance(labels_or_file, str):
labels, name = read_scalars(labels_or_file, True, True)
elif isinstance(labels_or_file, list):
labels = labels_or_file
unique_labels = np.unique(labels)
unique_labels = [x for x in unique_labels if x >= 0]
# Loop through labels:
for label in unique_labels:
#print(" Rescaling scalar values within label {0} of {1} labels...".format(
# int(label), len(unique_labels)))
indices = [i for i,x in enumerate(labels) if x == label]
if indices:
# Rescale by the maximum label scalar value:
scalars[indices] = scalars[indices] / np.max(scalars[indices])
rescaled_scalars = scalars.tolist()
#-------------------------------------------------------------------------
# Return rescaled scalars and file name
#-------------------------------------------------------------------------
if save_file:
rescaled_scalars_file = os.path.join(os.getcwd(), output_filestring + '.vtk')
rewrite_scalars(input_vtk, rescaled_scalars_file,
rescaled_scalars, 'rescaled_scalars', labels)
if not os.path.exists(rescaled_scalars_file):
raise(IOError(rescaled_scalars_file + " not found"))
else:
rescaled_scalars_file = None
return rescaled_scalars, rescaled_scalars_file
def write_face_vertex_averages(input_file, output_table='', area_file=''):
"""
Make table of average vertex values per face
(divided by face area if area_file provided).
Parameters
----------
input_file : string
name of VTK file with scalars to average
area_file : string
name of VTK file with surface area scalar values
output_table : string
output table filename
Returns
-------
output_table : string
output table filename
Examples
--------
>>> import os
>>> from mindboggle.mio.tables import write_face_vertex_averages
>>> path = '/homedir/mindboggled'
>>> input_file = os.path.join(path, 'Twins-2-1', 'shapes', 'left_cortical_surface', 'freesurfer_thickness.vtk')
>>> area_file = os.path.join(path, 'Twins-2-1', 'shapes', 'left_cortical_surface', 'area.vtk')
>>> output_table = ''
>>> #
>>> write_face_vertex_averages(input_file, output_table, area_file)
"""
import os
import numpy as np
import pandas as pd
from mindboggle.mio.vtks import read_vtk, read_scalars
points, indices, lines, faces, scalars, scalar_names, \
npoints, input_vtk = read_vtk(input_file, True, True)
if area_file:
area_scalars, name = read_scalars(area_file, True, True)
#---------------------------------------------------------------------
# For each face, average vertex values:
#---------------------------------------------------------------------
columns = []
for face in faces:
values = []
for index in face:
if area_file:
values.append(scalars[index] / area_scalars[index])
else:
values.append(scalars[index])
columns.append(np.mean(values))
#-----------------------------------------------------------------
# Write to table:
#-----------------------------------------------------------------
if not output_table:
output_table = os.path.join(os.getcwd(), 'average_face_values.csv')
df = pd.DataFrame({'': columns})
df.to_csv(output_table, index=False)
if not os.path.exists(output_table):
raise(IOError(output_table + " not found"))
return output_table
def write_vertex_measures(output_table,
labels_or_file,
sulci=[],
fundi=[],
affine_transform_files=[],
inverse_booleans=[],
transform_format='itk',
area_file='',
mean_curvature_file='',
travel_depth_file='',
geodesic_depth_file='',
freesurfer_thickness_file='',
freesurfer_curvature_file='',
freesurfer_sulc_file=''):
"""
Make a table of shape values per vertex.
Note ::
This function is tailored for Mindboggle outputs.
Parameters
----------
output_table : string
output file (full path)
labels_or_file : list or string
label number for each vertex or name of VTK file with index scalars
sulci : list of integers
indices to sulci, one per vertex, with -1 indicating no sulcus
fundi : list of integers
indices to fundi, one per vertex, with -1 indicating no fundus
affine_transform_files : list of strings
affine transform files to standard space
inverse_booleans : list of of zeros and ones
for each transform, 1 to take the inverse, else 0
transform_format : string
format for transform file
Ex: 'txt' for text, 'itk' for ITK, and 'mat' for Matlab format
area_file : string
name of VTK file with surface area scalar values
mean_curvature_file : string
name of VTK file with mean curvature scalar values
travel_depth_file : string
name of VTK file with travel depth scalar values
geodesic_depth_file : string
name of VTK file with geodesic depth scalar values
freesurfer_thickness_file : string
name of VTK file with FreeSurfer thickness scalar values
freesurfer_curvature_file : string
name of VTK file with FreeSurfer curvature (curv) scalar values
freesurfer_sulc_file : string
name of VTK file with FreeSurfer convexity (sulc) scalar values
Returns
-------
output_table : table file name for vertex shape values
Examples
--------
>>> import os
>>> from mindboggle.mio.vtks import read_scalars
>>> from mindboggle.mio.tables import write_vertex_measures
>>> output_table = '' #vertex_shapes.csv'
>>> from mindboggle.mio.fetch_data import prep_tests
>>> urls, fetch_data = prep_tests()
>>> labels_or_file = fetch_data(urls['left_freesurfer_labels'], '', '.vtk')
>>> sulci_file = fetch_data(urls['left_sulci'], '', '.vtk')
>>> fundi_file = fetch_data(urls['left_fundus_per_sulcus'], '', '.vtk')
>>> mean_curvature_file = fetch_data(urls['left_mean_curvature'], '', '.vtk')
>>> travel_depth_file = fetch_data(urls['left_travel_depth'], '', '.vtk')
>>> geodesic_depth_file = fetch_data(urls['left_geodesic_depth'], '', '.vtk')
>>> area_file = fetch_data(urls['left_area'], '', '.vtk')
>>> freesurfer_thickness_file = fetch_data(urls['left_freesurfer_thickness'], '', '.vtk')
>>> freesurfer_curvature_file = fetch_data(urls['left_freesurfer_curvature'], '', '.vtk')
>>> freesurfer_sulc_file = fetch_data(urls['left_freesurfer_sulc'], '', '.vtk')
>>> sulci, name = read_scalars(sulci_file)
>>> if fundi_file:
... fundi, name = read_scalars(fundi_file)
... else:
... fundi = []
>>> affine_transform_file = fetch_data(urls['affine_mni_transform'], '', '.txt')
>>> inverse_booleans = [1]
>>> transform_format = 'itk'
>>> swap_xy = True
>>> affine_rename = affine_transform_file + '.txt'
>>> os.rename(affine_transform_file, affine_rename)
>>> os.rename(labels_or_file, labels_or_file + '.vtk')
>>> os.rename(area_file, area_file + '.vtk')
>>> os.rename(mean_curvature_file, mean_curvature_file + '.vtk')
>>> os.rename(travel_depth_file, travel_depth_file + '.vtk')
>>> os.rename(geodesic_depth_file, geodesic_depth_file + '.vtk')
>>> os.rename(freesurfer_thickness_file, freesurfer_thickness_file + '.vtk')
>>> os.rename(freesurfer_curvature_file, freesurfer_curvature_file + '.vtk')
>>> os.rename(freesurfer_sulc_file, freesurfer_sulc_file + '.vtk')
>>> labels_or_file = labels_or_file + '.vtk'
>>> area_file = area_file + '.vtk'
>>> mean_curvature_file = mean_curvature_file + '.vtk'
>>> travel_depth_file = travel_depth_file + '.vtk'
>>> geodesic_depth_file = geodesic_depth_file + '.vtk'
>>> freesurfer_thickness_file = freesurfer_thickness_file + '.vtk'
>>> freesurfer_curvature_file = freesurfer_curvature_file + '.vtk'
>>> freesurfer_sulc_file = freesurfer_sulc_file + '.vtk'
>>> affine_transform_files = [] # [affine_rename] # requires ANTs to test
>>> output_table = write_vertex_measures(output_table, labels_or_file,
... sulci, fundi, affine_transform_files, inverse_booleans,
... transform_format, area_file, mean_curvature_file,
... travel_depth_file, geodesic_depth_file, freesurfer_thickness_file,
... freesurfer_curvature_file, freesurfer_sulc_file)
"""
import os
import numpy as np
import pandas as pd
from mindboggle.mio.vtks import read_scalars, read_vtk, \
apply_affine_transforms
# Make sure inputs are lists:
if isinstance(labels_or_file, np.ndarray):
labels = [int(x) for x in labels_or_file]
elif isinstance(labels_or_file, list):
labels = labels_or_file
elif isinstance(labels_or_file, str):
labels, name = read_scalars(labels_or_file)
if isinstance(sulci, np.ndarray):
sulci = [int(x) for x in sulci]
if isinstance(fundi, np.ndarray):
fundi = [int(x) for x in fundi]
if not labels and not sulci and not fundi:
raise IOError(
'No feature data to tabulate in write_vertex_measures().')
# Feature names and corresponding feature lists:
feature_names = ['label ID', 'sulcus ID', 'fundus ID']
feature_lists = [labels, sulci, fundi]
# Shape names corresponding to shape files below:
shape_names = [
'area', 'travel depth', 'geodesic depth', 'mean curvature',
'freesurfer curvature', 'freesurfer thickness',
'freesurfer convexity (sulc)'
]
# Load shape files as a list of numpy arrays of per-vertex shape values:
shape_files = [
area_file, travel_depth_file, geodesic_depth_file, mean_curvature_file,
freesurfer_curvature_file, freesurfer_thickness_file,
freesurfer_sulc_file
]
# Append columns of per-vertex scalar values:
columns = []
column_names = []
for ifeature, values in enumerate(feature_lists):
if values:
columns.append(values)
column_names.append(feature_names[ifeature])
first_pass = True
for ishape, shape_file in enumerate(shape_files):
if os.path.exists(shape_file):
if first_pass:
# Append x,y,z position per vertex to columns:
points, indices, lines, faces, scalars, scalar_names, \
npoints, input_vtk = read_vtk(shape_file)
xyz_positions = np.asarray(points)
for ixyz, xyz in enumerate(['x', 'y', 'z']):
column_names.append('position: {0}'.format(xyz))
columns.append(xyz_positions[:, ixyz].tolist())
first_pass = False
# Append standard space x,y,z position to columns:
if affine_transform_files and transform_format:
affine_points, \
foo1 = apply_affine_transforms(affine_transform_files,
inverse_booleans, transform_format,
points, vtk_file_stem='')
xyz_std_positions = affine_points
for ixyz, xyz in enumerate(['x', 'y', 'z']):
column_names.append('position in standard space:'
' {0}'.format(xyz))
columns.append(xyz_std_positions[:, ixyz].tolist())
else:
scalars, name = read_scalars(shape_file)
if len(scalars):
columns.append(scalars)
column_names.append(shape_names[ishape])
# Prepend with column of indices and write table
if not output_table:
output_table = os.path.join(os.getcwd(), 'vertices.csv')
df = pd.DataFrame(np.transpose(columns), columns=column_names)
df.to_csv(output_table, index=False, encoding='utf-8')
if not os.path.exists(output_table):
raise IOError(output_table + " not found")
return output_table
def extract_fundi(folds,
curv_file,
depth_file,
min_separation=10,
erode_ratio=0.1,
erode_min_size=1,
save_file=False,
output_file='',
background_value=-1,
verbose=False):
"""
Extract fundi from folds.
A fundus is a branching curve that runs along the deepest and most highly
curved portions of a fold. This function extracts one fundus from each
fold by finding the deepest vertices inside the fold, finding endpoints
along the edge of the fold, and connecting the former to the latter with
tracks that run along deep and curved paths (through vertices with high
values of travel depth multiplied by curvature), and a final filtration
step.
The deepest vertices are those with values at least two median
absolute deviations above the median (non-zero) value, with the higher
value chosen if two of the vertices are within (a default of) 10 edges
from each other (to reduce the number of possible fundus paths as well
as computation time).
To find the endpoints, the find_outer_endpoints function propagates
multiple tracks from seed vertices at median depth in the fold through
concentric rings toward the fold’s edge, selecting maximal values within
each ring, and terminating at candidate endpoints. The final endpoints
are those candidates at the end of tracks that have a high median value,
with the higher value chosen if two candidate endpoints are within
(a default of) 10 edges from each other (otherwise, the resulting fundi
can have spurious branching at the fold’s edge).
The connect_points_erosion function connects the deepest fold vertices
to the endpoints with a skeleton of 1-vertex-thick curves by erosion.
It erodes by iteratively removing simple topological points and endpoints
in order of lowest to highest values, where a simple topological point
is a vertex that when added to or removed from an object on a surface
mesh (such as a fundus curve) does not alter the object's topology.
Steps ::
1. Find fundus endpoints (outer anchors) with find_outer_endpoints().
2. Include inner anchor points.
3. Connect anchor points using connect_points_erosion();
inner anchors are removed if they result in endpoints.
Note ::
Follow this with segment_by_region() to segment fundi by sulci.
Parameters
----------
folds : numpy array or list of integers
fold number for each vertex
curv_file : string
surface mesh file in VTK format with mean curvature values
depth_file : string
surface mesh file in VTK format with rescaled depth values
likelihoods : list of integers
fundus likelihood value for each vertex
min_separation : integer
minimum number of edges between inner/outer anchor points
erode_ratio : float
fraction of indices to test for removal at each iteration
in connect_points_erosion()
save_file : bool
save output VTK file?
output_file : string
output VTK file
background_value : integer or float
background value
verbose : bool
print statements?
Returns
-------
fundus_per_fold : list of integers
fundus numbers for all vertices, labeled by fold
(-1 for non-fundus vertices)
n_fundi_in_folds : integer
number of fundi
fundus_per_fold_file : string (if save_file)
output VTK file with fundus numbers (-1 for non-fundus vertices)
Examples
--------
>>> # Extract fundus from one or more folds:
>>> import numpy as np
>>> from mindboggle.mio.vtks import read_scalars
>>> from mindboggle.features.fundi import extract_fundi
>>> from mindboggle.mio.fetch_data import prep_tests
>>> urls, fetch_data = prep_tests()
>>> curv_file = fetch_data(urls['left_mean_curvature'], '', '.vtk')
>>> depth_file = fetch_data(urls['left_travel_depth'], '', '.vtk')
>>> folds_file = fetch_data(urls['left_folds'], '', '.vtk')
>>> folds, name = read_scalars(folds_file, True, True)
>>> # Limit number of folds to speed up the test:
>>> limit_folds = True
>>> if limit_folds:
... fold_numbers = [4] #[4, 6]
... i0 = [i for i,x in enumerate(folds) if x not in fold_numbers]
... folds[i0] = -1
>>> min_separation = 10
>>> erode_ratio = 0.10
>>> erode_min_size = 10
>>> save_file = True
>>> output_file = 'extract_fundi_fold4.vtk'
>>> background_value = -1
>>> verbose = False
>>> o1, o2, fundus_per_fold_file = extract_fundi(folds, curv_file,
... depth_file, min_separation, erode_ratio, erode_min_size,
... save_file, output_file, background_value, verbose)
>>> lens = [len([x for x in o1 if x == y])
... for y in np.unique(o1) if y != background_value]
>>> lens[0:10] # [66, 2914, 100, 363, 73, 331, 59, 30, 1, 14] # (if not limit_folds)
[73]
View result without background (skip test):
>>> from mindboggle.mio.plots import plot_surfaces # doctest: +SKIP
>>> from mindboggle.mio.vtks import rewrite_scalars # doctest: +SKIP
>>> rewrite_scalars(fundus_per_fold_file,
... 'extract_fundi_fold4_no_background.vtk', o1,
... 'fundus_per_fold', folds) # doctest: +SKIP
>>> plot_surfaces('extract_fundi_fold4_no_background.vtk') # doctest: +SKIP
"""
# Extract a skeleton to connect endpoints in a fold:
import os
import numpy as np
from time import time
from mindboggle.mio.vtks import read_scalars, read_vtk, rewrite_scalars
from mindboggle.guts.compute import median_abs_dev
from mindboggle.guts.paths import find_max_values
from mindboggle.guts.mesh import find_neighbors_from_file
#from mindboggle.guts.mesh import find_complete_faces
from mindboggle.guts.paths import find_outer_endpoints
from mindboggle.guts.paths import connect_points_erosion
if isinstance(folds, list):
folds = np.array(folds)
# Load values, inner anchor threshold, and neighbors:
if os.path.isfile(curv_file):
points, indices, lines, faces, curvs, scalar_names, npoints, \
input_vtk = read_vtk(curv_file, True, True)
else:
raise IOError("{0} doesn't exist!".format(curv_file))
if os.path.isfile(curv_file):
depths, name = read_scalars(depth_file, True, True)
else:
raise IOError("{0} doesn't exist!".format(depth_file))
values = curvs * depths
values0 = [x for x in values if x > 0]
thr = np.median(values0) + 2 * median_abs_dev(values0)
neighbor_lists = find_neighbors_from_file(curv_file)
# ------------------------------------------------------------------------
# Loop through folds:
# ------------------------------------------------------------------------
t1 = time()
skeletons = []
unique_fold_IDs = [x for x in np.unique(folds) if x != background_value]
if verbose:
if len(unique_fold_IDs) == 1:
print("Extract a fundus from 1 fold...")
else:
print("Extract a fundus from each of {0} folds...".format(
len(unique_fold_IDs)))
for fold_ID in unique_fold_IDs:
indices_fold = [i for i, x in enumerate(folds) if x == fold_ID]
if indices_fold:
if verbose:
print(' Fold {0}:'.format(int(fold_ID)))
# ----------------------------------------------------------------
# Find outer anchor points on the boundary of the surface region,
# to serve as fundus endpoints:
# ----------------------------------------------------------------
outer_anchors, tracks = find_outer_endpoints(
indices_fold, neighbor_lists, values, depths, min_separation,
background_value, verbose)
# ----------------------------------------------------------------
# Find inner anchor points:
# ----------------------------------------------------------------
inner_anchors = find_max_values(points, values, min_separation,
thr)
# ----------------------------------------------------------------
# Connect anchor points to create skeleton:
# ----------------------------------------------------------------
B = background_value * np.ones(npoints)
B[indices_fold] = 1
skeleton = connect_points_erosion(B, neighbor_lists, outer_anchors,
inner_anchors, values,
erode_ratio, erode_min_size, [],
'', background_value, verbose)
if skeleton:
skeletons.extend(skeleton)
## ---------------------------------------------------------------
## Remove fundus vertices if they make complete triangle faces:
## ---------------------------------------------------------------
#Iremove = find_complete_faces(skeletons, faces)
#if Iremove:
# skeletons = list(frozenset(skeletons).difference(Iremove))
indices_skel = [x for x in skeletons if folds[x] != background_value]
fundus_per_fold = background_value * np.ones(npoints)
fundus_per_fold[indices_skel] = folds[indices_skel]
n_fundi_in_folds = len(
[x for x in np.unique(fundus_per_fold) if x != background_value])
if n_fundi_in_folds == 1:
sdum = 'fold fundus'
else:
sdum = 'fold fundi'
if verbose:
print(' ...Extracted {0} {1}; {2} total ({3:.2f} seconds)'.format(
n_fundi_in_folds, sdum, n_fundi_in_folds,
time() - t1))
# ------------------------------------------------------------------------
# Return fundi, number of fundi, and file name:
# ------------------------------------------------------------------------
fundus_per_fold_file = None
if n_fundi_in_folds > 0:
fundus_per_fold = [int(x) for x in fundus_per_fold]
if save_file:
if output_file:
fundus_per_fold_file = output_file
else:
fundus_per_fold_file = os.path.join(os.getcwd(),
'fundus_per_fold.vtk')
rewrite_scalars(curv_file, fundus_per_fold_file, fundus_per_fold,
'fundi', [], background_value)
if not os.path.exists(fundus_per_fold_file):
raise IOError(fundus_per_fold_file + " not found")
return fundus_per_fold, n_fundi_in_folds, fundus_per_fold_file
def write_average_face_values_per_label(input_indices_vtk,
input_values_vtk='',
area_file='',
output_stem='',
exclude_values=[-1],
background_value=-1,
verbose=False):
"""
Write out a separate csv table file for each integer
in (the first) scalar list of an input VTK file.
Optionally write the values drawn from a second VTK file.
Parameters
----------
input_indices_vtk : string
path of the input VTK file that contains indices as scalars
input_values_vtk : string
path of the input VTK file that contains values as scalars
output_stem : string
path and stem of the output VTK file
exclude_values : list or array
values to exclude
background_value : integer or float
background value in output VTK files
scalar_name : string
name of a lookup table of scalars values
verbose : bool
print statements?
Examples
--------
>>> import os
>>> from mindboggle.mio.tables import write_average_face_values_per_label
>>> from mindboggle.mio.fetch_data import prep_tests
>>> urls, fetch_data = prep_tests()
>>> input_indices_vtk = fetch_data(urls['left_freesurfer_labels'], '', '.vtk')
>>> input_values_vtk = fetch_data(urls['left_mean_curvature'], '', '.vtk')
>>> area_file = fetch_data(urls['left_area'], '', '.vtk')
>>> output_stem = 'labels_thickness'
>>> exclude_values = [-1]
>>> background_value = -1
>>> verbose = False
>>> write_average_face_values_per_label(input_indices_vtk,
... input_values_vtk, area_file, output_stem, exclude_values,
... background_value, verbose)
View vtk file (skip test):
>>> from mindboggle.mio.plots import plot_surfaces
>>> example_vtk = os.path.join(os.getcwd(), output_stem + '0.vtk')
>>> plot_surfaces(example_vtk) # doctest: +SKIP
"""
import os
import numpy as np
import pandas as pd
from mindboggle.mio.vtks import read_scalars, read_vtk, write_vtk
from mindboggle.guts.mesh import keep_faces
# Load VTK file:
points, indices, lines, faces, scalars, scalar_names, npoints, \
input_vtk = read_vtk(input_indices_vtk, True, True)
if area_file:
area_scalars, name = read_scalars(area_file, True, True)
if verbose:
print("Explode the scalar list in {0}".format(
os.path.basename(input_indices_vtk)))
if input_values_vtk != input_indices_vtk:
if verbose:
print("Explode the scalar list of values in {0} "
"with the scalar list of indices in {1}".format(
os.path.basename(input_values_vtk),
os.path.basename(input_indices_vtk)))
# Loop through unique (non-excluded) scalar values:
unique_scalars = [
int(x) for x in np.unique(scalars) if x not in exclude_values
]
for scalar in unique_scalars:
keep_indices = [x for sublst in faces for x in sublst]
new_faces = keep_faces(faces, keep_indices)
# Create array and indices for scalar value:
select_scalars = np.copy(scalars)
select_scalars[scalars != scalar] = background_value
scalar_indices = [
i for i, x in enumerate(select_scalars) if x == scalar
]
if verbose:
print(" Scalar {0}: {1} vertices".format(scalar,
len(scalar_indices)))
# --------------------------------------------------------------------
# For each face, average vertex values:
# --------------------------------------------------------------------
output_table = os.path.join(os.getcwd(),
output_stem + str(scalar) + '.csv')
columns = []
for face in new_faces:
values = []
for index in face:
if area_file:
values.append(scalars[index] / area_scalars[index])
else:
values.append(scalars[index])
columns.append(np.mean(values))
# ----------------------------------------------------------------
# Write to table:
# ----------------------------------------------------------------
df = pd.DataFrame({'': columns})
df.to_csv(output_table, index=False, encoding='utf-8')
if not os.path.exists(output_table):
raise IOError(output_table + " not found")
def plot_mask_surface(vtk_file,
mask_file='',
nonmask_value=-1,
masked_output='',
remove_nonmask=False,
program='vtkviewer',
use_colormap=False,
colormap_file='',
background_value=-1):
"""
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, 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 : bool
remove vertices that are not in mask? (otherwise assign nonmask_value)
program : string {'vtkviewer', 'mayavi2'}
program to visualize VTK file
use_colormap : bool
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
background_value : integer or float
background value
Examples
--------
>>> import os
>>> from mindboggle.mio.plots import plot_mask_surface
>>> from mindboggle.mio.fetch_data import prep_tests
>>> urls, fetch_data = prep_tests()
>>> vtk_file = fetch_data(urls['freesurfer_labels'], '', '.vtk')
>>> os.rename(vtk_file, vtk_file + '.nii.gz')
>>> vtk_file = vtk_file + '.nii.gz'
>>> mask_file = ''
>>> nonmask_value = 0 #-1
>>> masked_output = ''
>>> remove_nonmask = True
>>> program = 'vtkviewer'
>>> use_colormap = True
>>> colormap_file = ''
>>> background_value = -1
>>> plot_mask_surface(vtk_file, mask_file, nonmask_value, masked_output,
... remove_nonmask, program, use_colormap, colormap_file,
... background_value) # doctest: +SKIP
"""
import os
import numpy as np
from mindboggle.guts.mesh import keep_faces, reindex_faces_points
from mindboggle.guts.utilities import execute
from mindboggle.mio.plots import plot_surfaces
from mindboggle.mio.vtks 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:
# ----------------------------------------------------------------
points, indices, lines, faces, scalars, scalar_names, npoints, \
input_vtk = 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 = keep_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, ['scalars'], [],
background_value)
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 run_mindboggle_curvature(surface_file, neighborhood, method=0):
# Process the arguments and make outputs names
ccode_path = os.environ['vtk_cpp_tools']
command = os.path.join(ccode_path, 'curvature', 'CurvatureMain')
basename = os.path.splitext(os.path.basename(surface_file))[0]
extended_basename = '{}.mindboggle_m{}_n{}'.format(basename, method,
neighborhood)
stem = os.path.join(os.getcwd(), extended_basename)
mean_curv_file = '{}_mean_curv.vtk'.format(stem)
arguments = '-n {}'.format(neighborhood)
if method == 0 or method == 1:
gauss_curv_file = '{}_gauss_curv.vtk'.format(stem)
arguments = '{} -g {}'.format(arguments, gauss_curv_file)
if method == 0:
max_curv_file = '{}_kappa1.vtk'.format(stem)
min_curv_file = '{}_kappa2.vtk'.format(stem)
min_dir_file = '{}_T2.txt'.format(stem)
arguments = '{} -x {} -i {} -d {}'.format(arguments, max_curv_file,
min_curv_file, min_dir_file)
verbose = False
curvatures_file = '{}_curvatures.csv'.format(stem)
# Run the method
default_mean_curv_file, _, _, _, _ = curvature(command, method, arguments,
surface_file, verbose)
# rename mean curvature output file
os.rename(default_mean_curv_file, mean_curv_file)
# remove unneeded output file
os.remove(os.path.join(os.getcwd(), 'output.nipype'))
# Get the curvatures from VTK files
# [[x1, y1, z1], [x2, y2, z2], ...]
points = np.array(read_points(mean_curv_file))
xyz = points.T # transposed: [[x1, x2, ...], [y1, y2, ...], [z1, z2, ...]]
mean_curv, _ = read_scalars(mean_curv_file,
return_first=True,
return_array=True)
assert (xyz.shape[1] == mean_curv.size)
print('number of points: {}'.format(mean_curv.size))
if method == 0 or method == 1:
gauss_curv, _ = read_scalars(gauss_curv_file,
return_first=True,
return_array=True)
if method == 0:
max_curv, _ = read_scalars(max_curv_file,
return_first=True,
return_array=True)
min_curv, _ = read_scalars(min_curv_file,
return_first=True,
return_array=True)
# Write the curvatures to a CSV file
df = pd.DataFrame()
df['x'] = xyz[0]
df['y'] = xyz[1]
df['z'] = xyz[2]
df['mean_curvature'] = mean_curv
if method == 0 or method == 1:
df['gauss_curvature'] = gauss_curv
if method == 0:
df['kappa1'] = max_curv
df['kappa2'] = min_curv
df.to_csv(curvatures_file, sep=';')
def extract_sulci(labels_file,
folds_or_file,
hemi,
min_boundary=1,
sulcus_names=[],
save_file=False,
output_file='',
background_value=-1,
verbose=False):
"""
Identify sulci from folds in a brain surface according to a labeling
protocol that includes a list of label pairs defining each sulcus.
Since folds are defined as deep, connected areas of a surface, and since
folds may be connected to each other in ways that differ across brains,
there usually does not exist a one-to-one mapping between folds of one
brain and those of another. To address the correspondence problem then,
we need to find just those portions of the folds that correspond across
brains. To accomplish this, Mindboggle segments folds into sulci, which
do have a one-to-one correspondence across non-pathological brains.
Mindboggle defines a sulcus as a folded portion of cortex whose opposing
banks are labeled with one or more sulcus label pairs in the DKT labeling
protocol, where each label pair is unique to one sulcus and represents
a boundary between two adjacent gyri, and each vertex has one gyrus label.
This function assigns vertices in a fold to a sulcus in one of two cases.
In the first case, vertices whose labels are in only one label pair in
the fold are assigned to the label pair’s sulcus if they are connected
through similarly labeled vertices to the boundary between the two labels.
In the second case, the segment_regions function propagates labels from
label borders to vertices whose labels are in multiple label pairs in the
fold.
Steps for each fold ::
1. Remove fold if it has fewer than two labels.
2. Remove fold if its labels do not contain a sulcus label pair.
3. Find vertices with labels that are in only one of the fold's
label boundary pairs. Assign the vertices the sulcus with the label
pair if they are connected to the label boundary for that pair.
4. If there are remaining vertices, segment into sets of vertices
connected to label boundaries, and assign a unique ID to each set.
Parameters
----------
labels_file : string
file name for surface mesh VTK containing labels for all vertices
folds_or_file : numpy array, list or string
fold number for each vertex / name of VTK file containing fold scalars
hemi : string
hemisphere abbreviation in {'lh', 'rh'} for sulcus labels
min_boundary : integer
minimum number of vertices for a sulcus label boundary segment
sulcus_names : list of strings
names of sulci
save_file : bool
save output VTK file?
output_file : string
name of output file in VTK format
background_value : integer or float
background value
verbose : bool
print statements?
Returns
-------
sulci : list of integers
sulcus numbers for all vertices (-1 for non-sulcus vertices)
n_sulci : integers
number of sulci
sulci_file : string
output VTK file with sulcus numbers (-1 for non-sulcus vertices)
Examples
--------
>>> # Example 1: Extract sulcus from a fold with one sulcus label pair:
>>> import numpy as np
>>> from mindboggle.features.sulci import extract_sulci
>>> from mindboggle.mio.vtks import read_scalars
>>> from mindboggle.mio.fetch_data import prep_tests
>>> urls, fetch_data = prep_tests()
>>> # Load labels, folds, neighbor lists, and sulcus names and label pairs
>>> labels_file = fetch_data(urls['left_freesurfer_labels'], '', '.vtk')
>>> folds_file = fetch_data(urls['left_folds'], '', '.vtk')
>>> folds_or_file, name = read_scalars(folds_file, True, True)
>>> save_file = True
>>> output_file = 'extract_sulci_fold4_1sulcus.vtk'
>>> background_value = -1
>>> # Limit number of folds to speed up the test:
>>> limit_folds = True
>>> if limit_folds:
... fold_numbers = [4] #[4, 6]
... i0 = [i for i,x in enumerate(folds_or_file) if x not in fold_numbers]
... folds_or_file[i0] = background_value
>>> hemi = 'lh'
>>> min_boundary = 10
>>> sulcus_names = []
>>> verbose = False
>>> sulci, n_sulci, sulci_file = extract_sulci(labels_file, folds_or_file,
... hemi, min_boundary, sulcus_names, save_file, output_file,
... background_value, verbose)
>>> n_sulci # 23 # (if not limit_folds)
1
>>> lens = [len([x for x in sulci if x==y])
... for y in np.unique(sulci) if y != -1]
>>> lens[0:10] # [6358, 3288, 7612, 5205, 4414, 6251, 3493, 2566, 4436, 739] # (if not limit_folds)
[1151]
View result without background (skip test):
>>> from mindboggle.mio.plots import plot_surfaces # doctest: +SKIP
>>> from mindboggle.mio.vtks import rewrite_scalars # doctest: +SKIP
>>> output = 'extract_sulci_fold4_1sulcus_no_background.vtk'
>>> rewrite_scalars(sulci_file, output, sulci,
... 'sulci', sulci) # doctest: +SKIP
>>> plot_surfaces(output) # doctest: +SKIP
Example 2: Extract sulcus from a fold with multiple sulcus label pairs:
>>> folds_or_file, name = read_scalars(folds_file, True, True)
>>> output_file = 'extract_sulci_fold7_2sulci.vtk'
>>> # Limit number of folds to speed up the test:
>>> limit_folds = True
>>> if limit_folds:
... fold_numbers = [7] #[4, 6]
... i0 = [i for i,x in enumerate(folds_or_file) if x not in fold_numbers]
... folds_or_file[i0] = background_value
>>> sulci, n_sulci, sulci_file = extract_sulci(labels_file, folds_or_file,
... hemi, min_boundary, sulcus_names, save_file, output_file,
... background_value, verbose)
>>> n_sulci # 23 # (if not limit_folds)
2
>>> lens = [len([x for x in sulci if x==y])
... for y in np.unique(sulci) if y != -1]
>>> lens[0:10] # [6358, 3288, 7612, 5205, 4414, 6251, 3493, 2566, 4436, 739] # (if not limit_folds)
[369, 93]
View result without background (skip test):
>>> from mindboggle.mio.plots import plot_surfaces # doctest: +SKIP
>>> from mindboggle.mio.vtks import rewrite_scalars # doctest: +SKIP
>>> output = 'extract_sulci_fold7_2sulci_no_background.vtk'
>>> rewrite_scalars(sulci_file, output, sulci,
... 'sulci', sulci) # doctest: +SKIP
>>> plot_surfaces(output) # doctest: +SKIP
"""
import os
from time import time
import numpy as np
from mindboggle.mio.vtks import read_scalars, read_vtk, rewrite_scalars
from mindboggle.guts.mesh import find_neighbors
from mindboggle.guts.segment import extract_borders, propagate, segment_regions
from mindboggle.mio.labels import DKTprotocol
# Load fold numbers if folds_or_file is a string:
if isinstance(folds_or_file, str):
folds, name = read_scalars(folds_or_file)
elif isinstance(folds_or_file, list):
folds = folds_or_file
elif isinstance(folds_or_file, np.ndarray):
folds = folds_or_file.tolist()
dkt = DKTprotocol()
if hemi == 'lh':
pair_lists = dkt.left_sulcus_label_pair_lists
elif hemi == 'rh':
pair_lists = dkt.right_sulcus_label_pair_lists
else:
raise IOError(
"Warning: hemisphere not properly specified ('lh' or 'rh').")
# Load points, faces, and neighbors:
points, indices, lines, faces, labels, scalar_names, npoints, \
input_vtk = read_vtk(labels_file)
neighbor_lists = find_neighbors(faces, npoints)
# Array of sulcus IDs for fold vertices, initialized as -1.
# Since we do not touch gyral vertices and vertices whose labels
# are not in the label list, or vertices having only one label,
# their sulcus IDs will remain -1:
sulci = background_value * np.ones(npoints)
# ------------------------------------------------------------------------
# Loop through folds
# ------------------------------------------------------------------------
fold_numbers = [int(x) for x in np.unique(folds) if x != background_value]
n_folds = len(fold_numbers)
if verbose:
print("Extract sulci from {0} folds...".format(n_folds))
t0 = time()
for n_fold in fold_numbers:
fold_indices = [i for i, x in enumerate(folds) if x == n_fold]
len_fold = len(fold_indices)
# List the labels in this fold:
fold_labels = [labels[x] for x in fold_indices]
unique_fold_labels = [
int(x) for x in np.unique(fold_labels) if x != background_value
]
# --------------------------------------------------------------------
# NO MATCH -- fold has fewer than two labels
# --------------------------------------------------------------------
if verbose and len(unique_fold_labels) < 2:
# Ignore: sulci already initialized with -1 values:
if not unique_fold_labels:
print(" Fold {0} ({1} vertices): "
"NO MATCH -- fold has no labels".format(
n_fold, len_fold))
else:
print(" Fold {0} ({1} vertices): "
"NO MATCH -- fold has only one label ({2})".format(
n_fold, len_fold, unique_fold_labels[0]))
# Ignore: sulci already initialized with -1 values
else:
# Find all label boundary pairs within the fold:
indices_fold_pairs, fold_pairs, unique_fold_pairs = \
extract_borders(fold_indices, labels, neighbor_lists,
ignore_values=[], return_label_pairs=True)
# Find fold label pairs in the protocol (pairs are already sorted):
fold_pairs_in_protocol = [
x for x in unique_fold_pairs
if x in dkt.unique_sulcus_label_pairs
]
if verbose and unique_fold_labels:
print(" Fold {0} labels: {1} ({2} vertices)".format(
n_fold, ', '.join([str(x) for x in unique_fold_labels]),
len_fold))
# ----------------------------------------------------------------
# NO MATCH -- fold has no sulcus label pair
# ----------------------------------------------------------------
if verbose and not fold_pairs_in_protocol:
print(" Fold {0}: NO MATCH -- fold has no sulcus label pair".
format(n_fold, len_fold))
# ----------------------------------------------------------------
# Possible matches
# ----------------------------------------------------------------
else:
if verbose:
print(" Fold {0} label pairs in protocol: {1}".format(
n_fold,
', '.join([str(x) for x in fold_pairs_in_protocol])))
# Labels in the protocol (includes repeats across label pairs):
labels_in_pairs = [
x for lst in fold_pairs_in_protocol for x in lst
]
# Labels that appear in one or more sulcus label boundary:
unique_labels = []
nonunique_labels = []
for label in np.unique(labels_in_pairs):
if len([x for x in labels_in_pairs if x == label]) == 1:
unique_labels.append(label)
else:
nonunique_labels.append(label)
# ------------------------------------------------------------
# Vertices whose labels are in only one sulcus label pair
# ------------------------------------------------------------
# Find vertices with a label that is in only one of the fold's
# label pairs (the other label in the pair can exist in other
# pairs). Assign the vertices the sulcus with the label pair
# if they are connected to the label boundary for that pair.
# ------------------------------------------------------------
if unique_labels:
for pair in fold_pairs_in_protocol:
# If one or both labels in label pair is/are unique:
unique_labels_in_pair = [
x for x in pair if x in unique_labels
]
n_unique = len(unique_labels_in_pair)
if n_unique:
ID = None
for i, pair_list in enumerate(pair_lists):
if not isinstance(pair_list, list):
pair_list = [pair_list]
if pair in pair_list:
ID = i
break
if ID:
# Seeds from label boundary vertices
# (fold_pairs and pair already sorted):
indices_pair = [
x for i, x in enumerate(indices_fold_pairs)
if fold_pairs[i] == pair
]
# Vertices with unique label(s) in pair:
indices_unique_labels = [
fold_indices[i]
for i, x in enumerate(fold_labels)
if x in unique_labels_in_pair
]
#dkt.unique_sulcus_label_pairs]
# Propagate sulcus ID from seeds to vertices
# with "unique" labels (only exist in one
# label pair in a fold); propagation ensures
# that sulci consist of contiguous vertices
# for each label boundary:
sulci2 = segment_regions(
indices_unique_labels,
neighbor_lists,
min_region_size=1,
seed_lists=[indices_pair],
keep_seeding=False,
spread_within_labels=True,
labels=labels,
label_lists=[],
values=[],
max_steps='',
background_value=background_value,
verbose=False)
sulci[sulci2 != background_value] = ID
# Print statement:
if verbose:
if n_unique == 1:
ps1 = 'One label'
else:
ps1 = 'Both labels'
if len(sulcus_names):
ps2 = sulcus_names[ID]
else:
ps2 = ''
print(" {0} unique to one fold pair: "
"{1} {2}".format(
ps1, ps2, unique_labels_in_pair))
# ------------------------------------------------------------
# Vertex labels shared by multiple label pairs
# ------------------------------------------------------------
# Propagate labels from label borders to vertices with labels
# that are shared by multiple label pairs in the fold.
# ------------------------------------------------------------
if len(nonunique_labels):
# For each label shared by different label pairs:
for label in nonunique_labels:
# Print statement:
if verbose:
print(
" Propagate sulcus borders with label {0}".
format(int(label)))
# Construct seeds from label boundary vertices:
seeds = background_value * np.ones(npoints)
for ID, pair_list in enumerate(pair_lists):
if not isinstance(pair_list, list):
pair_list = [pair_list]
label_pairs = [x for x in pair_list if label in x]
for label_pair in label_pairs:
indices_pair = [
x for i, x in enumerate(indices_fold_pairs)
if np.sort(fold_pairs[i]).tolist() ==
label_pair
]
if indices_pair:
# Do not include short boundary segments:
if min_boundary > 1:
indices_pair2 = []
seeds2 = segment_regions(
indices_pair, neighbor_lists, 1,
[], False, False, [], [], [], '',
background_value, verbose)
useeds2 = [
x for x in np.unique(seeds2)
if x != background_value
]
for seed2 in useeds2:
iseed2 = [
i for i, x in enumerate(seeds2)
if x == seed2
]
if len(iseed2) >= min_boundary:
indices_pair2.extend(iseed2)
elif verbose:
if len(iseed2) == 1:
print(" Remove "
"assignment "
"of ID {0} from "
"1 vertex".format(
seed2))
else:
print(
" Remove "
"assignment "
"of ID {0} from "
"{1} vertices".format(
seed2,
len(iseed2)))
indices_pair = indices_pair2
# Assign sulcus IDs to seeds:
seeds[indices_pair] = ID
# Identify vertices with the label:
indices_label = [
fold_indices[i] for i, x in enumerate(fold_labels)
if x == label
]
if len(indices_label):
# Propagate sulcus ID from seeds to vertices
# with a given shared label:
seg_vs_prop = False
if seg_vs_prop:
indices_seeds = []
for seed in [
x for x in np.unique(seeds)
if x != background_value
]:
indices_seeds.append([
i for i, x in enumerate(seeds)
if x == seed
])
sulci2 = segment_regions(
indices_label, neighbor_lists, 50,
indices_seeds, False, True, labels, [], [],
'', background_value, verbose)
else:
label_array = background_value * \
np.ones(npoints)
label_array[indices_label] = 1
sulci2 = propagate(
points,
faces,
label_array,
seeds,
sulci,
max_iters=10000,
tol=0.001,
sigma=5,
background_value=background_value,
verbose=verbose)
sulci[sulci2 != background_value] = \
sulci2[sulci2 != background_value]
sulcus_numbers = [
int(x) for x in np.unique(sulci) if x != background_value
]
n_sulci = len(sulcus_numbers)
# ------------------------------------------------------------------------
# Print statements
# ------------------------------------------------------------------------
if verbose:
if n_sulci == 1:
sulcus_str = 'sulcus'
else:
sulcus_str = 'sulci'
if n_folds == 1:
folds_str = 'fold'
else:
folds_str = 'folds'
print("Extracted {0} {1} from {2} {3} ({4:.1f}s):".format(
n_sulci, sulcus_str, n_folds, folds_str,
time() - t0))
if sulcus_names:
for sulcus_number in sulcus_numbers:
print(" {0}: {1}".format(sulcus_number,
sulcus_names[sulcus_number]))
elif sulcus_numbers:
print(" " + ", ".join([str(x) for x in sulcus_numbers]))
unresolved = [
i for i in range(len(pair_lists)) if i not in sulcus_numbers
]
if len(unresolved) == 1:
print("The following sulcus is unaccounted for:")
else:
print("The following {0} sulci are unaccounted for:".format(
len(unresolved)))
if sulcus_names:
for sulcus_number in unresolved:
print(" {0}: {1}".format(sulcus_number,
sulcus_names[sulcus_number]))
else:
print(" " + ", ".join([str(x) for x in unresolved]))
# ------------------------------------------------------------------------
# Return sulci, number of sulci, and file name
# ------------------------------------------------------------------------
sulci = [int(x) for x in sulci]
sulci_file = os.path.join(os.getcwd(), 'sulci.vtk')
rewrite_scalars(labels_file, sulci_file, sulci, 'sulci', [],
background_value)
if not os.path.exists(sulci_file):
raise IOError(sulci_file + " not found")
return sulci, n_sulci, sulci_file
def write_vertex_measures(output_table, labels_or_file, sulci=[], fundi=[],
affine_transform_files=[], inverse_booleans=[],
transform_format='itk',
area_file='', mean_curvature_file='', travel_depth_file='',
geodesic_depth_file='', freesurfer_thickness_file='',
freesurfer_curvature_file='', freesurfer_sulc_file=''):
"""
Make a table of shape values per vertex.
Note ::
This function is tailored for Mindboggle outputs.
Parameters
----------
output_table : string
output file (full path)
labels_or_file : list or string
label number for each vertex or name of VTK file with index scalars
sulci : list of integers
indices to sulci, one per vertex, with -1 indicating no sulcus
fundi : list of integers
indices to fundi, one per vertex, with -1 indicating no fundus
affine_transform_files : list of strings
affine transform files to standard space
inverse_booleans : list of of zeros and ones
for each transform, 1 to take the inverse, else 0
transform_format : string
format for transform file
Ex: 'txt' for text, 'itk' for ITK, and 'mat' for Matlab format
area_file : string
name of VTK file with surface area scalar values
mean_curvature_file : string
name of VTK file with mean curvature scalar values
travel_depth_file : string
name of VTK file with travel depth scalar values
geodesic_depth_file : string
name of VTK file with geodesic depth scalar values
freesurfer_thickness_file : string
name of VTK file with FreeSurfer thickness scalar values
freesurfer_curvature_file : string
name of VTK file with FreeSurfer curvature (curv) scalar values
freesurfer_sulc_file : string
name of VTK file with FreeSurfer convexity (sulc) scalar values
Returns
-------
output_table : table file name for vertex shape values
Examples
--------
>>> import os
>>> from mindboggle.mio.vtks import read_scalars
>>> from mindboggle.mio.tables import write_vertex_measures
>>> #
>>> output_table = ''#vertex_shapes.csv'
>>> path = os.environ['MINDBOGGLE_DATA']
>>> labels_or_file = os.path.join(path, 'arno', 'labels', 'lh.labels.DKT25.manual.vtk')
>>> sulci_file = os.path.join(path, 'arno', 'features', 'sulci.vtk')
>>> fundi_file = os.path.join(path, 'arno', 'features', 'fundi.vtk')
>>> sulci, name = read_scalars(sulci_file)
>>> fundi, name = read_scalars(fundi_file)
>>> affine_transform_files = [os.path.join(path, 'arno', 'mri',
>>> 't1weighted_brain.MNI152Affine.txt')]
>>> inverse_booleans = [1]
>>> transform_format = 'itk'
>>> swap_xy = True
>>> area_file = os.path.join(path, 'arno', 'shapes', 'lh.pial.area.vtk')
>>> mean_curvature_file = os.path.join(path, 'arno', 'shapes', 'lh.pial.mean_curvature.vtk')
>>> travel_depth_file = os.path.join(path, 'arno', 'shapes', 'lh.pial.travel_depth.vtk')
>>> geodesic_depth_file = os.path.join(path, 'arno', 'shapes', 'lh.pial.geodesic_depth.vtk')
>>> freesurfer_thickness_file = ''
>>> freesurfer_curvature_file = ''
>>> freesurfer_sulc_file = ''
>>> #
>>> write_vertex_measures(output_table, labels_or_file, sulci, fundi,
>>> affine_transform_files, inverse_booleans, transform_format, area_file,
>>> mean_curvature_file, travel_depth_file, geodesic_depth_file,
>>> freesurfer_thickness_file, freesurfer_curvature_file, freesurfer_sulc_file)
"""
import os
import numpy as np
import pandas as pd
from mindboggle.mio.vtks import read_scalars, read_vtk, \
apply_affine_transforms
# Make sure inputs are lists:
if isinstance(labels_or_file, np.ndarray):
labels = [int(x) for x in labels_or_file]
elif isinstance(labels_or_file, list):
labels = labels_or_file
elif isinstance(labels_or_file, str):
labels, name = read_scalars(labels_or_file)
if isinstance(sulci, np.ndarray):
sulci = [int(x) for x in sulci]
if isinstance(fundi, np.ndarray):
fundi = [int(x) for x in fundi]
if not labels and not sulci and not fundi:
import sys
sys.exit('No feature data to tabulate in write_vertex_measures().')
# Feature names and corresponding feature lists:
feature_names = ['label ID', 'sulcus ID', 'fundus ID']
feature_lists = [labels, sulci, fundi]
# Shape names corresponding to shape files below:
shape_names = ['area', 'travel depth', 'geodesic depth',
'mean curvature', 'freesurfer curvature',
'freesurfer thickness', 'freesurfer convexity (sulc)']
# Load shape files as a list of numpy arrays of per-vertex shape values:
shape_files = [area_file, travel_depth_file, geodesic_depth_file,
mean_curvature_file, freesurfer_curvature_file,
freesurfer_thickness_file, freesurfer_sulc_file]
# Append columns of per-vertex scalar values:
columns = []
column_names = []
for ifeature, values in enumerate(feature_lists):
if values:
columns.append(values)
column_names.append(feature_names[ifeature])
first_pass = True
for ishape, shape_file in enumerate(shape_files):
if os.path.exists(shape_file):
if first_pass:
# Append x,y,z position per vertex to columns:
points, indices, lines, faces, scalars, scalar_names, \
npoints, input_vtk = read_vtk(shape_file)
xyz_positions = np.asarray(points)
for ixyz, xyz in enumerate(['x','y','z']):
column_names.append('position: {0}'.format(xyz))
columns.append(xyz_positions[:, ixyz].tolist())
first_pass = False
# Append standard space x,y,z position to columns:
if affine_transform_files and transform_format:
affine_points, \
foo1 = apply_affine_transforms(affine_transform_files,
inverse_booleans, transform_format,
points, vtk_file_stem='')
xyz_std_positions = affine_points
for ixyz, xyz in enumerate(['x','y','z']):
column_names.append('position in standard space:'
' {0}'.format(xyz))
columns.append(xyz_std_positions[:, ixyz].tolist())
else:
scalars, name = read_scalars(shape_file)
if len(scalars):
columns.append(scalars)
column_names.append(shape_names[ishape])
# Prepend with column of indices and write table
if not output_table:
output_table = os.path.join(os.getcwd(), 'vertices.csv')
df = pd.DataFrame(np.transpose(columns), columns = column_names)
df.to_csv(output_table, index=False)
if not os.path.exists(output_table):
raise(IOError(output_table + " not found"))
return output_table
def write_face_vertex_averages(input_file, output_table='', area_file=''):
"""
Make table of average vertex values per face
(divided by face area if area_file provided).
Parameters
----------
input_file : string
name of VTK file with scalars to average
area_file : string
name of VTK file with surface area scalar values
output_table : string
output table filename
Returns
-------
output_table : string
output table filename
Examples
--------
>>> from mindboggle.mio.tables import write_face_vertex_averages
>>> from mindboggle.mio.fetch_data import prep_tests
>>> urls, fetch_data = prep_tests()
>>> input_file = fetch_data(urls['left_travel_depth'])
>>> area_file = fetch_data(urls['left_area'])
>>> output_table = ''
>>> output_table = write_face_vertex_averages(input_file, output_table,
... area_file)
"""
import os
import numpy as np
import pandas as pd
from mindboggle.mio.vtks import read_vtk, read_scalars
points, indices, lines, faces, scalars, scalar_names, \
npoints, input_vtk = read_vtk(input_file, True, True)
if area_file:
area_scalars, name = read_scalars(area_file, True, True)
#---------------------------------------------------------------------
# For each face, average vertex values:
#---------------------------------------------------------------------
columns = []
for face in faces:
values = []
for index in face:
if area_file:
values.append(scalars[index] / area_scalars[index])
else:
values.append(scalars[index])
columns.append(np.mean(values))
#-----------------------------------------------------------------
# Write to table:
#-----------------------------------------------------------------
if not output_table:
output_table = os.path.join(os.getcwd(), 'average_face_values.csv')
df = pd.DataFrame({'': columns})
df.to_csv(output_table, index=False)
if not os.path.exists(output_table):
raise IOError(output_table + " not found")
return output_table
def write_average_face_values_per_label(input_indices_vtk,
input_values_vtk='', area_file='',
output_stem='', exclude_values=[-1], background_value=-1):
"""
Write out a separate VTK file for each integer
in (the first) scalar list of an input VTK file.
Optionally write the values drawn from a second VTK file.
Parameters
----------
input_indices_vtk : string
path of the input VTK file that contains indices as scalars
input_values_vtk : string
path of the input VTK file that contains values as scalars
output_stem : string
path and stem of the output VTK file
exclude_values : list or array
values to exclude
background_value : integer or float
background value in output VTK files
scalar_name : string
name of a lookup table of scalars values
Examples
--------
>>> import os
>>> from mindboggle.mio.tables import write_average_face_values_per_label
>>> path = '/homedir/mindboggled'
>>> input_indices_vtk = os.path.join(path, 'Twins-2-1', 'labels', 'left_cortical_surface', 'freesurfer_cortex_labels.vtk')
>>> input_values_vtk = os.path.join(path, 'Twins-2-1', 'shapes', 'left_cortical_surface', 'freesurfer_thickness.vtk')
>>> area_file = os.path.join(path, 'Twins-2-1', 'shapes', 'left_cortical_surface', 'area.vtk')
>>> output_stem = 'labels_thickness'
>>> exclude_values = [-1]
>>> background_value = -1
>>> #
>>> write_average_face_values_per_label(input_indices_vtk,
>>> input_values_vtk, area_file, output_stem, exclude_values, background_value)
>>> #
>>> # View:
>>> #example_vtk = os.path.join(os.getcwd(), output_stem + '0.vtk')
>>> #from mindboggle.mio.plots import plot_surfaces
>>> #plot_surfaces(example_vtk)
"""
import os
import numpy as np
import pandas as pd
from mindboggle.mio.vtks import read_scalars, read_vtk, write_vtk
from mindboggle.guts.mesh import remove_faces
# Load VTK file:
points, indices, lines, faces, scalars, scalar_names, npoints, \
input_vtk = read_vtk(input_indices_vtk, True, True)
if area_file:
area_scalars, name = read_scalars(area_file, True, True)
print("Explode the scalar list in {0}".
format(os.path.basename(input_indices_vtk)))
if input_values_vtk != input_indices_vtk:
values, name = read_scalars(input_values_vtk, True, True)
print("Explode the scalar list of values in {0} "
"with the scalar list of indices in {1}".
format(os.path.basename(input_values_vtk),
os.path.basename(input_indices_vtk)))
else:
values = np.copy(scalars)
# Loop through unique (non-excluded) scalar values:
unique_scalars = [int(x) for x in np.unique(scalars)
if x not in exclude_values]
for scalar in unique_scalars:
keep_indices = [x for sublst in faces for x in sublst]
new_faces = remove_faces(faces, keep_indices)
# Create array and indices for scalar value:
select_scalars = np.copy(scalars)
select_scalars[scalars != scalar] = background_value
scalar_indices = [i for i,x in enumerate(select_scalars) if x==scalar]
print(" Scalar {0}: {1} vertices".format(scalar, len(scalar_indices)))
#---------------------------------------------------------------------
# For each face, average vertex values:
#---------------------------------------------------------------------
output_table = os.path.join(os.getcwd(),
output_stem+str(scalar)+'.csv')
columns = []
for face in new_faces:
values = []
for index in face:
if area_file:
values.append(scalars[index] / area_scalars[index])
else:
values.append(scalars[index])
columns.append(np.mean(values))
#-----------------------------------------------------------------
# Write to table:
#-----------------------------------------------------------------
df = pd.DataFrame({'': columns})
df.to_csv(output_table, index=False)
# Write VTK file with scalar value:
#output_vtk = os.path.join(os.getcwd(), output_stem + str(scalar) + '.vtk')
#write_vtk(output_vtk, points, indices, lines, new_faces,
# [select_values.tolist()], [output_scalar_name])
if not os.path.exists(output_table):
raise(IOError(output_table + " not found"))
def write_average_face_values_per_label(input_indices_vtk,
input_values_vtk='', area_file='', output_stem='',
exclude_values=[-1], background_value=-1, verbose=False):
"""
Write out a separate VTK file for each integer
in (the first) scalar list of an input VTK file.
Optionally write the values drawn from a second VTK file.
Parameters
----------
input_indices_vtk : string
path of the input VTK file that contains indices as scalars
input_values_vtk : string
path of the input VTK file that contains values as scalars
output_stem : string
path and stem of the output VTK file
exclude_values : list or array
values to exclude
background_value : integer or float
background value in output VTK files
scalar_name : string
name of a lookup table of scalars values
verbose : bool
print statements?
Examples
--------
>>> import os
>>> from mindboggle.mio.tables import write_average_face_values_per_label
>>> from mindboggle.mio.fetch_data import prep_tests
>>> urls, fetch_data = prep_tests()
>>> input_indices_vtk = fetch_data(urls['left_freesurfer_labels'])
>>> input_values_vtk = fetch_data(urls['left_mean_curvature'])
>>> area_file = fetch_data(urls['left_area'])
>>> output_stem = 'labels_thickness'
>>> exclude_values = [-1]
>>> background_value = -1
>>> verbose = False
>>> write_average_face_values_per_label(input_indices_vtk,
... input_values_vtk, area_file, output_stem, exclude_values,
... background_value, verbose)
View vtk file (skip test):
>>> from mindboggle.mio.plots import plot_surfaces
>>> example_vtk = os.path.join(os.getcwd(), output_stem + '0.vtk')
>>> plot_surfaces(example_vtk) # doctest: +SKIP
"""
import os
import numpy as np
import pandas as pd
from mindboggle.mio.vtks import read_scalars, read_vtk, write_vtk
from mindboggle.guts.mesh import keep_faces
# Load VTK file:
points, indices, lines, faces, scalars, scalar_names, npoints, \
input_vtk = read_vtk(input_indices_vtk, True, True)
if area_file:
area_scalars, name = read_scalars(area_file, True, True)
if verbose:
print("Explode the scalar list in {0}".
format(os.path.basename(input_indices_vtk)))
if input_values_vtk != input_indices_vtk:
if verbose:
print("Explode the scalar list of values in {0} "
"with the scalar list of indices in {1}".
format(os.path.basename(input_values_vtk),
os.path.basename(input_indices_vtk)))
# Loop through unique (non-excluded) scalar values:
unique_scalars = [int(x) for x in np.unique(scalars)
if x not in exclude_values]
for scalar in unique_scalars:
keep_indices = [x for sublst in faces for x in sublst]
new_faces = keep_faces(faces, keep_indices)
# Create array and indices for scalar value:
select_scalars = np.copy(scalars)
select_scalars[scalars != scalar] = background_value
scalar_indices = [i for i,x in enumerate(select_scalars) if x==scalar]
if verbose:
print(" Scalar {0}: {1} vertices".format(scalar,
len(scalar_indices)))
#---------------------------------------------------------------------
# For each face, average vertex values:
#---------------------------------------------------------------------
output_table = os.path.join(os.getcwd(),
output_stem+str(scalar)+'.csv')
columns = []
for face in new_faces:
values = []
for index in face:
if area_file:
values.append(scalars[index] / area_scalars[index])
else:
values.append(scalars[index])
columns.append(np.mean(values))
#-----------------------------------------------------------------
# Write to table:
#-----------------------------------------------------------------
df = pd.DataFrame({'': columns})
df.to_csv(output_table, index=False)
if not os.path.exists(output_table):
raise IOError(output_table + " not found")
