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.utils.segment import extract_borders
import mindboggle.utils.graph as go
from mindboggle.utils.io_vtk import read_vtk, read_scalars, write_vtk
from mindboggle.utils.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 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.utils.io_vtk import read_scalars, rewrite_scalars
>>> from mindboggle.features.sulci import extract_sulci
>>> from mindboggle.utils.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.utils.io_vtk import read_scalars, read_vtk, rewrite_scalars
from mindboggle.utils.mesh import find_neighbors
from mindboggle.utils.segment import extract_borders, propagate, segment
from mindboggle.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 close_surface_pair(faces, points1, points2, scalars, background_value=-1):
"""
Close a surface patch by connecting its border vertices with
corresponding vertices in a second surface file.
Assumes no lines or indices when reading VTK files in.
Note ::
Scalar values different than background define the surface patch.
The two sets of points have a 1-to-1 mapping; they are from
two surfaces whose corresponding vertices are shifted in position.
For pial vs. gray-white matter, the two surfaces are not parallel,
so connecting the vertices leads to intersecting faces.
Parameters
----------
faces : list of lists of integers
each sublist contains 3 indices of vertices that form a face
on a surface mesh
points1 : list of lists of floats
each sublist contains 3-D coordinates of a vertex on a surface mesh
points2 : list of lists of floats
points from second surface with 1-to-1 correspondence with points1
scalars : numpy array of integers
labels used to find foreground vertices
background_value : integer
scalar value for background vertices
Returns
-------
closed_faces : list of lists of integers
indices of vertices that form a face on the closed surface mesh
closed_points : list of lists of floats
3-D coordinates from points1 and points2
closed_scalars : list of integers
scalar values for points1 and points2
Examples
--------
>>> # Example 1: build a cube by closing two parallel planes:
>>> import os
>>> from mindboggle.utils.morph import close_surface_pair
>>> from mindboggle.utils.plots import plot_surfaces
>>> from mindboggle.utils.io_vtk import write_vtk
>>> # Build plane:
>>> background_value = -1
>>> n = 10 # plane edge length
>>> points1 = []
>>> for x in range(n):
>>> for y in range(n):
>>> points1.append([x,y,0])
>>> points2 = [[x[0],x[1],1] for x in points1]
>>> scalars = [background_value for x in range(len(points1))]
>>> p = n*(n-1)/2 - 1
>>> for i in [p, p+1, p+n, p+n+1]:
>>> scalars[i] = 1
>>> faces = []
>>> for x in range(n-1):
>>> for y in range(n-1):
>>> faces.append([x+y*n,x+n+y*n,x+n+1+y*n])
>>> faces.append([x+y*n,x+1+y*n,x+n+1+y*n])
>>> #write_vtk('plane.vtk', points1, [], [], faces, scalars)
>>> #plot_surfaces('plane.vtk') # doctest: +SKIP
>>> closed_faces, closed_points, closed_scalars = close_surface_pair(faces, points1, points2, scalars, background_value)
>>> # View:
>>> write_vtk('cube.vtk', closed_points, [], [], closed_faces, closed_scalars, 'int')
>>> plot_surfaces('cube.vtk') # doctest: +SKIP
>>> #
>>> # Example 2: Gray and white cortical brain surfaces:
>>> import os
>>> from mindboggle.utils.morph import close_surface_pair
>>> from mindboggle.utils.plots import plot_surfaces
>>> from mindboggle.utils.io_vtk import read_scalars, read_vtk, read_points, write_vtk
>>> path = os.environ['MINDBOGGLE_DATA']
>>> patch_surface1 = 'fold.pial.vtk'
>>> whole_surface2 = 'fold.white.vtk'
>>> # Select a single fold:
>>> folds_file = os.path.join(path, 'arno', 'features', 'folds.vtk')
>>> points1 = read_points(folds_file)
>>> scalars, name = read_scalars(folds_file, True, True)
>>> fold_number = 11
>>> scalars[scalars != fold_number] = -1
>>> white_surface = os.path.join(path, 'arno', 'freesurfer', 'lh.white.vtk')
>>> faces, u1, u2, points2, N, u3, u4, u5 = read_vtk(white_surface)
>>> background_value = -1
>>> closed_faces, closed_points, closed_scalars = close_surface_pair(faces, points1, points2, scalars, background_value)
>>> # View:
>>> write_vtk('closed.vtk', closed_points, [], [], closed_faces, closed_scalars, name, 'int')
>>> plot_surfaces('closed.vtk') # doctest: +SKIP
"""
import sys
import numpy as np
from mindboggle.utils.mesh import find_neighbors, remove_faces
from mindboggle.utils.segment import extract_borders
if isinstance(scalars, list):
scalars = np.array(scalars)
N = len(points1)
closed_points = points1 + points2
# Find all vertex neighbors and surface patch border vertices:
neighbor_lists = find_neighbors(faces, N)
I = np.where(scalars != background_value)[0]
scalars[scalars == background_value] = background_value + 1
scalars[I] = background_value + 2
scalars = scalars.tolist()
borders, u1, u2 = extract_borders(range(N), scalars, neighbor_lists)
if not len(borders):
sys.exit('There are no border vertices!')
borders = [x for x in borders if x in I]
# Reindex copy of faces and combine with original (both zero-index):
indices = range(N)
indices2 = range(N, 2 * N)
reindex = dict([(index, indices2[i]) for i, index in enumerate(indices)])
faces = remove_faces(faces, I)
faces2 = [[reindex[i] for i in face] for face in faces]
closed_faces = faces + faces2
# Connect border vertices between surface patches and add new faces:
add_faces = []
taken_already = []
for index in borders:
if index not in taken_already:
neighbors = list(set(neighbor_lists[index]).intersection(borders))
taken_already.append(index)
#taken_already.extend([index] + neighbors)
for neighbor in neighbors:
add_faces.append([index, index + N, neighbor])
add_faces.append([index + N, neighbor, neighbor + N])
closed_faces = closed_faces + add_faces
closed_scalars = scalars * 2
return closed_faces, closed_points, closed_scalars
def close_surface_pair(faces, points1, points2, scalars, background_value=-1):
"""
Close a surface patch by connecting its border vertices with
corresponding vertices in a second surface file.
Assumes no lines or indices when reading VTK files in.
Note ::
Scalar values different than background define the surface patch.
The two sets of points have a 1-to-1 mapping; they are from
two surfaces whose corresponding vertices are shifted in position.
For pial vs. gray-white matter, the two surfaces are not parallel,
so connecting the vertices leads to intersecting faces.
Parameters
----------
faces : list of lists of integers
each sublist contains 3 indices of vertices that form a face
on a surface mesh
points1 : list of lists of floats
each sublist contains 3-D coordinates of a vertex on a surface mesh
points2 : list of lists of floats
points from second surface with 1-to-1 correspondence with points1
scalars : numpy array of integers
labels used to find foreground vertices
background_value : integer
scalar value for background vertices
Returns
-------
closed_faces : list of lists of integers
indices of vertices that form a face on the closed surface mesh
closed_points : list of lists of floats
3-D coordinates from points1 and points2
closed_scalars : list of integers
scalar values for points1 and points2
Examples
--------
>>> # Example 1: build a cube by closing two parallel planes:
>>> import os
>>> from mindboggle.utils.morph import close_surface_pair
>>> from mindboggle.utils.plots import plot_surfaces
>>> from mindboggle.utils.io_vtk import write_vtk
>>> # Build plane:
>>> background_value = -1
>>> n = 10 # plane edge length
>>> points1 = []
>>> for x in range(n):
>>> for y in range(n):
>>> points1.append([x,y,0])
>>> points2 = [[x[0],x[1],1] for x in points1]
>>> scalars = [background_value for x in range(len(points1))]
>>> p = n*(n-1)/2 - 1
>>> for i in [p, p+1, p+n, p+n+1]:
>>> scalars[i] = 1
>>> faces = []
>>> for x in range(n-1):
>>> for y in range(n-1):
>>> faces.append([x+y*n,x+n+y*n,x+n+1+y*n])
>>> faces.append([x+y*n,x+1+y*n,x+n+1+y*n])
>>> #write_vtk('plane.vtk', points1, [], [], faces, scalars)
>>> #plot_surfaces('plane.vtk') # doctest: +SKIP
>>> closed_faces, closed_points, closed_scalars = close_surface_pair(faces, points1, points2, scalars, background_value)
>>> # View:
>>> write_vtk('cube.vtk', closed_points, [], [], closed_faces, closed_scalars, 'int')
>>> plot_surfaces('cube.vtk') # doctest: +SKIP
>>> #
>>> # Example 2: Gray and white cortical brain surfaces:
>>> import os
>>> from mindboggle.utils.morph import close_surface_pair
>>> from mindboggle.utils.plots import plot_surfaces
>>> from mindboggle.utils.io_vtk import read_scalars, read_vtk, read_points, write_vtk
>>> path = os.environ['MINDBOGGLE_DATA']
>>> patch_surface1 = 'fold.pial.vtk'
>>> whole_surface2 = 'fold.white.vtk'
>>> # Select a single fold:
>>> folds_file = os.path.join(path, 'arno', 'features', 'folds.vtk')
>>> points1 = read_points(folds_file)
>>> scalars, name = read_scalars(folds_file, True, True)
>>> fold_number = 11
>>> scalars[scalars != fold_number] = -1
>>> white_surface = os.path.join(path, 'arno', 'freesurfer', 'lh.white.vtk')
>>> faces, u1, u2, points2, N, u3, u4, u5 = read_vtk(white_surface)
>>> background_value = -1
>>> closed_faces, closed_points, closed_scalars = close_surface_pair(faces, points1, points2, scalars, background_value)
>>> # View:
>>> write_vtk('closed.vtk', closed_points, [], [], closed_faces, closed_scalars, name, 'int')
>>> plot_surfaces('closed.vtk') # doctest: +SKIP
"""
import sys
import numpy as np
from mindboggle.utils.mesh import find_neighbors, remove_faces
from mindboggle.utils.segment import extract_borders
if isinstance(scalars, list):
scalars = np.array(scalars)
N = len(points1)
closed_points = points1 + points2
# Find all vertex neighbors and surface patch border vertices:
neighbor_lists = find_neighbors(faces, N)
I = np.where(scalars != background_value)[0]
scalars[scalars == background_value] = background_value + 1
scalars[I] = background_value + 2
scalars = scalars.tolist()
borders, u1, u2 = extract_borders(range(N), scalars, neighbor_lists)
if not len(borders):
sys.exit('There are no border vertices!')
borders = [x for x in borders if x in I]
# Reindex copy of faces and combine with original (both zero-index):
indices = range(N)
indices2 = range(N, 2 * N)
reindex = dict([(index, indices2[i]) for i, index in enumerate(indices)])
faces = remove_faces(faces, I)
faces2 = [[reindex[i] for i in face] for face in faces]
closed_faces = faces + faces2
# Connect border vertices between surface patches and add new faces:
add_faces = []
taken_already = []
for index in borders:
if index not in taken_already:
neighbors = list(set(neighbor_lists[index]).intersection(borders))
taken_already.append(index)
#taken_already.extend([index] + neighbors)
for neighbor in neighbors:
add_faces.append([index, index + N, neighbor])
add_faces.append([index + N, neighbor, neighbor + N])
closed_faces = closed_faces + add_faces
closed_scalars = scalars * 2
return closed_faces, closed_points, closed_scalars
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.utils.io_vtk import read_scalars, rewrite_scalars
>>> from mindboggle.features.sulci import extract_sulci
>>> from mindboggle.utils.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.utils.io_vtk import read_scalars, read_vtk, rewrite_scalars
from mindboggle.utils.mesh import find_neighbors
from mindboggle.utils.segment import extract_borders, propagate, segment
from mindboggle.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 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.utils.segment import extract_borders
import mindboggle.utils.graph as go
from mindboggle.utils.io_vtk import read_vtk, read_scalars, write_vtk
from mindboggle.utils.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 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 boundary vertex to all of the feature vertices in the same sulcus,
and from each feature vertex to all of the label boundary vertices in the
same sulcus. The label boundaries 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_fundus_mean_distances : numpy array [number of features x 1]
mean distance from each feature to sulcus label boundary ("fundus")
feature_to_fundus_sd_distances : numpy array [number of features x 1]
standard deviations of feature-to-fundus distances
feature_to_fundus_mean_distances_vtk : string
VTK surface file containing feature_to_fundus_mean_distances
fundus_to_feature_mean_distances : numpy array [number of features x 1]
mean distances from each sulcus label boundary ("fundus") to feature
fundus_to_feature_sd_distances : numpy array [number of features x 1]
standard deviations of fundus-to-feature distances
fundus_to_feature_mean_distances_vtk : string
VTK surface file containing fundus_to_feature_mean_distances
"""
import os
import sys
import numpy as np
from mindboggle.utils.io_vtk import read_vtk, read_scalars, write_vtk
from mindboggle.utils.mesh import find_neighbors, remove_faces
from mindboggle.utils.segment import extract_borders
from mindboggle.utils.compute import source_to_target_distances
from mindboggle.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, fundus IDs, and outputs:
#-------------------------------------------------------------------------
# Calculate neighbor lists for all points:
print('Find neighbors to all vertices...')
neighbor_lists = find_neighbors(faces, npoints)
# Find label boundary points in any of the sulci:
print('Find label boundary 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 boundary points!')
# Initialize an array of label boundaries fundus IDs
# (label boundary vertices that define sulci in the labeling protocol):
print('Build an array of label boundary fundus IDs...')
label_boundary_fundi = -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_fundus_mean_distances = -1 * np.ones(nsulcus_lists)
feature_to_fundus_sd_distances = -1 * np.ones(nsulcus_lists)
fundus_to_feature_mean_distances = -1 * np.ones(nsulcus_lists)
fundus_to_feature_sd_distances = -1 * np.ones(nsulcus_lists)
feature_to_fundus_mean_distances_vtk = ''
fundus_to_feature_mean_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 boundary points with label pair labels:
fundus_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 fundus IDs array:
if fundus_indices:
label_boundary_fundi[fundus_indices] = isulcus
if len(np.unique(label_boundary_fundi)) > 1:
#---------------------------------------------------------------------
# Construct a feature-to-fundus distance matrix and VTK file:
#---------------------------------------------------------------------
# Construct a distance matrix:
print('Construct a feature-to-fundus distance matrix...')
sourceIDs = features
targetIDs = label_boundary_fundi
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_fundus_mean_distances[ifeature] = \
np.mean(feature_distances)
feature_to_fundus_sd_distances[ifeature] = \
np.std(feature_distances)
if verbose:
print('Feature-to-fundus mean distances:')
print(feature_to_fundus_mean_distances)
print('Feature-to-fundus standard deviations of distances:')
print(feature_to_fundus_sd_distances)
# Write resulting feature-label boundary distances to VTK file:
if output_vtk_name:
feature_to_fundus_mean_distances_vtk = os.path.join(os.getcwd(),
output_vtk_name + '_feature_to_fundus_mean_distances.vtk')
print('Write feature-to-fundus distances to {0}...'.
format(feature_to_fundus_mean_distances_vtk))
write_vtk(feature_to_fundus_mean_distances_vtk, points,
[], [], sulcus_faces, [distances],
['feature-to-fundus_distances'], 'float')
#---------------------------------------------------------------------
# Construct a fundus-to-feature distance matrix and VTK file:
#---------------------------------------------------------------------
# Construct a distance matrix:
print('Construct a fundus-to-feature distance matrix...')
sourceIDs = label_boundary_fundi
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):
fundus_distances = [x for x in distance_matrix[:, ifeature]
if x != -1]
fundus_to_feature_mean_distances[ifeature] = \
np.mean(fundus_distances)
fundus_to_feature_sd_distances[ifeature] = \
np.std(fundus_distances)
if verbose:
print('Fundus-to-feature mean distances:')
print(fundus_to_feature_mean_distances)
print('Fundus-to-feature standard deviations of distances:')
print(fundus_to_feature_sd_distances)
# Write resulting feature-label boundary distances to VTK file:
if output_vtk_name:
fundus_to_feature_mean_distances_vtk = os.path.join(os.getcwd(),
output_vtk_name + '_fundus_to_feature_mean_distances.vtk')
print('Write fundus-to-feature distances to {0}...'.
format(fundus_to_feature_mean_distances_vtk))
write_vtk(fundus_to_feature_mean_distances_vtk, points,
[], [], sulcus_faces, [distances],
['fundus-to-feature_distances'], 'float')
#-------------------------------------------------------------------------
# Return outputs:
#-------------------------------------------------------------------------
return feature_to_fundus_mean_distances, feature_to_fundus_sd_distances,\
feature_to_fundus_mean_distances_vtk,\
fundus_to_feature_mean_distances, fundus_to_feature_sd_distances,\
fundus_to_feature_mean_distances_vtk
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.utils.io_vtk import read_scalars
from mindboggle.utils.mesh import find_neighbors_from_file
from mindboggle.utils.segment import extract_borders
from mindboggle.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 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.utils.io_vtk import read_vtk, read_scalars, write_vtk
from mindboggle.utils.mesh import find_neighbors, remove_faces
from mindboggle.utils.segment import extract_borders
from mindboggle.utils.compute import source_to_target_distances
from mindboggle.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 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.utils.io_vtk import read_scalars
from mindboggle.utils.mesh import find_neighbors_from_file
from mindboggle.utils.segment import extract_borders
from mindboggle.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