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 extract_folds(depth_file, min_vertices=10000, min_fold_size=50, do_fill_holes=False, min_hole_depth=0.001, save_file=False): """ Use depth to extract folds from a triangular surface mesh. Steps :: 1. Compute histogram of depth measures. 2. Define a depth threshold and find the deepest vertices. 3. Segment deep vertices as an initial set of folds. 4. Remove small folds. 5. Find and fill holes in the folds (optional). 6. Renumber folds. Step 2 :: To extract an initial set of deep vertices from the surface mesh, we anticipate that there will be a rapidly decreasing distribution of low depth values (on the outer surface) with a long tail of higher depth values (in the folds), so we smooth the histogram's bin values, convolve to compute slopes, and find the depth value for the first bin with slope = 0. This is our threshold. Step 5 :: The folds could have holes in areas shallower than the depth threshold. Calling fill_holes() could accidentally include very shallow areas (in an annulus-shaped fold, for example), so we include the argument exclude_range to check for any values from zero to min_hole_depth; holes are not filled if they contains values within this range. Parameters ---------- depth_file : string surface mesh file in VTK format with faces and depth scalar values min_fold_size : integer minimum fold size (number of vertices) do_fill_holes : Boolean fill holes in the folds? min_hole_depth : float largest non-zero depth value that will stop a hole from being filled save_file : Boolean save output VTK file? Returns ------- folds : list of integers fold numbers for all vertices (-1 for non-fold vertices) n_folds : int number of folds depth_threshold : float threshold defining the minimum depth for vertices to be in a fold bins : list of integers histogram bins: each is the number of vertices within a range of depth values bin_edges : list of floats histogram bin edge values defining the bin ranges of depth values folds_file : string (if save_file) name of output VTK file with fold IDs (-1 for non-fold vertices) Examples -------- >>> import os >>> import numpy as np >>> import pylab >>> from scipy.ndimage.filters import gaussian_filter1d >>> from mindboggle.mio.vtks import read_scalars >>> from mindboggle.guts.mesh import find_neighbors_from_file >>> from mindboggle.mio.plots import plot_surfaces >>> from mindboggle.features.folds import extract_folds >>> path = os.environ['MINDBOGGLE_DATA'] >>> depth_file = 'travel_depth.vtk' #os.path.join(path, 'arno', 'shapes', 'lh.pial.travel_depth.vtk') >>> neighbor_lists = find_neighbors_from_file(depth_file) >>> min_vertices = 10000 >>> min_fold_size = 50 >>> do_fill_holes = False #True >>> min_hole_depth = 0.001 >>> save_file = True >>> # >>> folds, n_folds, thr, bins, bin_edges, folds_file = extract_folds(depth_file, >>> min_vertices, min_fold_size, do_fill_holes, min_hole_depth, save_file) >>> # >>> # View folds: >>> plot_surfaces('folds.vtk') >>> # Plot histogram and depth threshold: >>> depths, name = read_scalars(depth_file) >>> nbins = np.round(len(depths) / 100.0) >>> a,b,c = pylab.hist(depths, bins=nbins) >>> pylab.plot(thr*np.ones((100,1)), np.linspace(0, max(bins), 100), 'r.') >>> pylab.show() >>> # Plot smoothed histogram: >>> bins_smooth = gaussian_filter1d(bins.tolist(), 5) >>> pylab.plot(range(len(bins)), bins, '.', range(len(bins)), bins_smooth,'-') >>> pylab.show() """ import os import sys import numpy as np from time import time from scipy.ndimage.filters import gaussian_filter1d from mindboggle.mio.vtks import rewrite_scalars, read_vtk from mindboggle.guts.mesh import find_neighbors from mindboggle.guts.morph import fill_holes from mindboggle.guts.segment import segment print("Extract folds in surface mesh") t0 = time() #------------------------------------------------------------------------- # Load depth values for all vertices #------------------------------------------------------------------------- points, indices, lines, faces, depths, scalar_names, npoints, \ input_vtk = read_vtk(depth_file, return_first=True, return_array=True) #------------------------------------------------------------------------- # Find neighbors for each vertex #------------------------------------------------------------------------- neighbor_lists = find_neighbors(faces, npoints) #------------------------------------------------------------------------- # Compute histogram of depth measures #------------------------------------------------------------------------- if npoints > min_vertices: nbins = np.round(npoints / 100.0) else: sys.err(" Expecting at least {0} vertices to create depth histogram". format(min_vertices)) bins, bin_edges = np.histogram(depths, bins=nbins) #------------------------------------------------------------------------- # Anticipating that there will be a rapidly decreasing distribution # of low depth values (on the outer surface) with a long tail of higher # depth values (in the folds), smooth the bin values (Gaussian), convolve # to compute slopes, and find the depth for the first bin with slope = 0. #------------------------------------------------------------------------- bins_smooth = gaussian_filter1d(bins.tolist(), 5) window = [-1, 0, 1] bin_slopes = np.convolve(bins_smooth, window, mode='same') / (len(window) - 1) ibins0 = np.where(bin_slopes == 0)[0] if ibins0.shape: depth_threshold = bin_edges[ibins0[0]] else: depth_threshold = np.median(depths) #------------------------------------------------------------------------- # Find the deepest vertices #------------------------------------------------------------------------- indices_deep = [i for i, x in enumerate(depths) if x >= depth_threshold] if indices_deep: #--------------------------------------------------------------------- # Segment deep vertices as an initial set of folds #--------------------------------------------------------------------- print(" Segment vertices deeper than {0:.2f} as folds".format( depth_threshold)) t1 = time() folds = segment(indices_deep, neighbor_lists) # Slightly slower alternative -- fill boundaries: #regions = -1 * np.ones(len(points)) #regions[indices_deep] = 1 #folds = segment_by_filling_borders(regions, neighbor_lists) print(' ...Segmented folds ({0:.2f} seconds)'.format(time() - t1)) #--------------------------------------------------------------------- # Remove small folds #--------------------------------------------------------------------- if min_fold_size > 1: print(' Remove folds smaller than {0}'.format(min_fold_size)) unique_folds = [x for x in np.unique(folds) if x != -1] for nfold in unique_folds: indices_fold = [i for i, x in enumerate(folds) if x == nfold] if len(indices_fold) < min_fold_size: folds[indices_fold] = -1 #--------------------------------------------------------------------- # Find and fill holes in the folds # Note: Surfaces surrounded by folds can be mistaken for holes, # so exclude_range includes outer surface values close to zero. #--------------------------------------------------------------------- if do_fill_holes: print(" Find and fill holes in the folds") folds = fill_holes(folds, neighbor_lists, values=depths, exclude_range=[0, min_hole_depth]) #--------------------------------------------------------------------- # Renumber folds so they are sequential #--------------------------------------------------------------------- renumber_folds = -1 * np.ones(len(folds)) fold_numbers = [int(x) for x in np.unique(folds) if x != -1] for i_fold, n_fold in enumerate(fold_numbers): fold = [i for i, x in enumerate(folds) if x == n_fold] renumber_folds[fold] = i_fold folds = renumber_folds n_folds = i_fold + 1 # Print statement print(' ...Extracted {0} folds ({1:.2f} seconds)'.format( n_folds, time() - t0)) else: print(' No deep vertices') folds = [int(x) for x in folds] #------------------------------------------------------------------------- # Return folds, number of folds, file name #------------------------------------------------------------------------- if save_file: folds_file = os.path.join(os.getcwd(), 'folds.vtk') rewrite_scalars(depth_file, folds_file, folds, 'folds', folds) if not os.path.exists(folds_file): raise (IOError(folds_file + " not found")) else: folds_file = None return folds, n_folds, depth_threshold, bins, bin_edges, folds_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 extract_folds(depth_file, min_vertices=10000, min_fold_size=50, do_fill_holes=False, min_hole_depth=0.001, save_file=False): """ Use depth to extract folds from a triangular surface mesh. Steps :: 1. Compute histogram of depth measures. 2. Define a depth threshold and find the deepest vertices. 3. Segment deep vertices as an initial set of folds. 4. Remove small folds. 5. Find and fill holes in the folds (optional). 6. Renumber folds. Step 2 :: To extract an initial set of deep vertices from the surface mesh, we anticipate that there will be a rapidly decreasing distribution of low depth values (on the outer surface) with a long tail of higher depth values (in the folds), so we smooth the histogram's bin values, convolve to compute slopes, and find the depth value for the first bin with slope = 0. This is our threshold. Step 5 :: The folds could have holes in areas shallower than the depth threshold. Calling fill_holes() could accidentally include very shallow areas (in an annulus-shaped fold, for example), so we include the argument exclude_range to check for any values from zero to min_hole_depth; holes are not filled if they contains values within this range. Parameters ---------- depth_file : string surface mesh file in VTK format with faces and depth scalar values min_fold_size : integer minimum fold size (number of vertices) do_fill_holes : Boolean fill holes in the folds? min_hole_depth : float largest non-zero depth value that will stop a hole from being filled save_file : Boolean save output VTK file? Returns ------- folds : list of integers fold numbers for all vertices (-1 for non-fold vertices) n_folds : int number of folds depth_threshold : float threshold defining the minimum depth for vertices to be in a fold bins : list of integers histogram bins: each is the number of vertices within a range of depth values bin_edges : list of floats histogram bin edge values defining the bin ranges of depth values folds_file : string (if save_file) name of output VTK file with fold IDs (-1 for non-fold vertices) Examples -------- >>> import os >>> import numpy as np >>> import pylab >>> from scipy.ndimage.filters import gaussian_filter1d >>> from mindboggle.mio.vtks import read_scalars >>> from mindboggle.guts.mesh import find_neighbors_from_file >>> from mindboggle.mio.plots import plot_surfaces >>> from mindboggle.features.folds import extract_folds >>> path = os.environ['MINDBOGGLE_DATA'] >>> depth_file = 'travel_depth.vtk' #os.path.join(path, 'arno', 'shapes', 'lh.pial.travel_depth.vtk') >>> neighbor_lists = find_neighbors_from_file(depth_file) >>> min_vertices = 10000 >>> min_fold_size = 50 >>> do_fill_holes = False #True >>> min_hole_depth = 0.001 >>> save_file = True >>> # >>> folds, n_folds, thr, bins, bin_edges, folds_file = extract_folds(depth_file, >>> min_vertices, min_fold_size, do_fill_holes, min_hole_depth, save_file) >>> # >>> # View folds: >>> plot_surfaces('folds.vtk') >>> # Plot histogram and depth threshold: >>> depths, name = read_scalars(depth_file) >>> nbins = np.round(len(depths) / 100.0) >>> a,b,c = pylab.hist(depths, bins=nbins) >>> pylab.plot(thr*np.ones((100,1)), np.linspace(0, max(bins), 100), 'r.') >>> pylab.show() >>> # Plot smoothed histogram: >>> bins_smooth = gaussian_filter1d(bins.tolist(), 5) >>> pylab.plot(range(len(bins)), bins, '.', range(len(bins)), bins_smooth,'-') >>> pylab.show() """ import os import sys import numpy as np from time import time from scipy.ndimage.filters import gaussian_filter1d from mindboggle.mio.vtks import rewrite_scalars, read_vtk from mindboggle.guts.mesh import find_neighbors from mindboggle.guts.morph import fill_holes from mindboggle.guts.segment import segment print("Extract folds in surface mesh") t0 = time() #------------------------------------------------------------------------- # Load depth values for all vertices #------------------------------------------------------------------------- faces, lines, indices, points, npoints, depths, name, input_vtk = read_vtk(depth_file, return_first=True, return_array=True) #------------------------------------------------------------------------- # Find neighbors for each vertex #------------------------------------------------------------------------- neighbor_lists = find_neighbors(faces, npoints) #------------------------------------------------------------------------- # Compute histogram of depth measures #------------------------------------------------------------------------- if npoints > min_vertices: nbins = np.round(npoints / 100.0) else: sys.err(" Expecting at least {0} vertices to create depth histogram". format(min_vertices)) bins, bin_edges = np.histogram(depths, bins=nbins) #------------------------------------------------------------------------- # Anticipating that there will be a rapidly decreasing distribution # of low depth values (on the outer surface) with a long tail of higher # depth values (in the folds), smooth the bin values (Gaussian), convolve # to compute slopes, and find the depth for the first bin with slope = 0. #------------------------------------------------------------------------- bins_smooth = gaussian_filter1d(bins.tolist(), 5) window = [-1, 0, 1] bin_slopes = np.convolve(bins_smooth, window, mode='same') / (len(window) - 1) ibins0 = np.where(bin_slopes == 0)[0] if ibins0.shape: depth_threshold = bin_edges[ibins0[0]] else: depth_threshold = np.median(depths) #------------------------------------------------------------------------- # Find the deepest vertices #------------------------------------------------------------------------- indices_deep = [i for i,x in enumerate(depths) if x >= depth_threshold] if indices_deep: #--------------------------------------------------------------------- # Segment deep vertices as an initial set of folds #--------------------------------------------------------------------- print(" Segment vertices deeper than {0:.2f} as folds".format(depth_threshold)) t1 = time() folds = segment(indices_deep, neighbor_lists) # Slightly slower alternative -- fill boundaries: #regions = -1 * np.ones(len(points)) #regions[indices_deep] = 1 #folds = segment_by_filling_borders(regions, neighbor_lists) print(' ...Segmented folds ({0:.2f} seconds)'.format(time() - t1)) #--------------------------------------------------------------------- # Remove small folds #--------------------------------------------------------------------- if min_fold_size > 1: print(' Remove folds smaller than {0}'.format(min_fold_size)) unique_folds = [x for x in np.unique(folds) if x != -1] for nfold in unique_folds: indices_fold = [i for i,x in enumerate(folds) if x == nfold] if len(indices_fold) < min_fold_size: folds[indices_fold] = -1 #--------------------------------------------------------------------- # Find and fill holes in the folds # Note: Surfaces surrounded by folds can be mistaken for holes, # so exclude_range includes outer surface values close to zero. #--------------------------------------------------------------------- if do_fill_holes: print(" Find and fill holes in the folds") folds = fill_holes(folds, neighbor_lists, values=depths, exclude_range=[0, min_hole_depth]) #--------------------------------------------------------------------- # Renumber folds so they are sequential #--------------------------------------------------------------------- renumber_folds = -1 * np.ones(len(folds)) fold_numbers = [int(x) for x in np.unique(folds) if x != -1] for i_fold, n_fold in enumerate(fold_numbers): fold = [i for i,x in enumerate(folds) if x == n_fold] renumber_folds[fold] = i_fold folds = renumber_folds n_folds = i_fold + 1 # Print statement print(' ...Extracted {0} folds ({1:.2f} seconds)'. format(n_folds, time() - t0)) else: print(' No deep vertices') folds = [int(x) for x in folds] #------------------------------------------------------------------------- # Return folds, number of folds, file name #------------------------------------------------------------------------- if save_file: folds_file = os.path.join(os.getcwd(), 'folds.vtk') rewrite_scalars(depth_file, folds_file, folds, 'folds', folds) if not os.path.exists(folds_file): raise(IOError(folds_file + " not found")) else: folds_file = None return folds, n_folds, depth_threshold, bins, bin_edges, folds_file
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 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): """ 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 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_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=[], 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 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 segment_fundi(fundus_per_fold, sulci=[], vtk_file='', save_file=False, verbose=False): """ Segment fundi by sulcus definitions. Parameters ---------- fundus_per_fold : list of integers fundus numbers for all vertices, labeled by fold (-1 for non-fundus vertices) sulci : numpy array or list of integers sulcus number for each vertex, used to filter and label fundi vtk_file : string (if save_file) VTK file with sulcus number for each vertex save_file : bool save output VTK file? verbose : bool print statements? Returns ------- fundus_per_sulcus : list of integers fundus numbers for all vertices, labeled by sulcus (-1 for non-fundus vertices) n_fundi : integer number of fundi fundus_per_sulcus_file : string (if save_file) output VTK file with fundus numbers (-1 for non-fundus vertices) Examples -------- >>> # Extract fundus from one or more sulci: >>> import numpy as np >>> single_fold = True >>> from mindboggle.features.fundi import segment_fundi >>> from mindboggle.features.fundi import extract_fundi >>> from mindboggle.mio.vtks import read_scalars >>> 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']) >>> vtk_file = fetch_data(urls['left_sulci']) >>> sulci, name = read_scalars(vtk_file, True, True) >>> 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 >>> fundus_per_fold, o1, o2 = extract_fundi(folds, ... curv_file, depth_file, min_separation, erode_ratio, ... erode_min_size, save_file, verbose) >>> o1, o2, fundus_per_sulcus_file = segment_fundi(fundus_per_fold, ... sulci, vtk_file, save_file, verbose) >>> segment_numbers = [x for x in np.unique(o1) if x != -1] >>> lens = [] >>> if single_fold: ... for segment_number in segment_numbers: ... lens.append(len([x for x in o1 if x == segment_number])) >>> lens [14, 13, 88] View result (skip test): >>> from mindboggle.mio.plots import plot_surfaces >>> plot_surfaces(fundus_per_sulcus_file) # doctest: +SKIP """ # Extract a skeleton to connect endpoints in a fold: import os import numpy as np from mindboggle.mio.vtks import rewrite_scalars if isinstance(sulci, list): sulci = np.array(sulci) #------------------------------------------------------------------------- # Create fundi by segmenting fold fundi with overlapping sulcus labels: #------------------------------------------------------------------------- indices = [i for i,x in enumerate(fundus_per_fold) if x != -1] if indices and np.size(sulci): fundus_per_sulcus = -1 * np.ones(len(sulci)) fundus_per_sulcus[indices] = sulci[indices] n_fundi = len([x for x in np.unique(fundus_per_sulcus) if x != -1]) else: fundus_per_sulcus = [] n_fundi = 0 if n_fundi == 1: sdum = 'sulcus fundus' else: sdum = 'sulcus fundi' if verbose: print(' Segmented {0} {1}'.format(n_fundi, sdum)) #------------------------------------------------------------------------- # Return fundi, number of fundi, and file name: #------------------------------------------------------------------------- fundus_per_sulcus_file = None if n_fundi > 0: fundus_per_sulcus = [int(x) for x in fundus_per_sulcus] if save_file and os.path.exists(vtk_file): fundus_per_sulcus_file = os.path.join(os.getcwd(), 'fundus_per_sulcus.vtk') # Do not filter faces/points by scalars when saving file: rewrite_scalars(vtk_file, fundus_per_sulcus_file, fundus_per_sulcus, 'fundus_per_sulcus') if not os.path.exists(fundus_per_sulcus_file): raise IOError(fundus_per_sulcus_file + " not found") return fundus_per_sulcus, n_fundi, fundus_per_sulcus_file
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 rescale_by_label(input_vtk, labels_or_file, save_file=False, output_filestring='rescaled_scalars', verbose=False): """ 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 : bool save output VTK file? output_filestring : string (if save_file) name of output file verbose : bool print statements? 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 numpy as np >>> from mindboggle.guts.mesh import rescale_by_label >>> from mindboggle.mio.vtks import read_scalars >>> from mindboggle.mio.plots import plot_surfaces >>> from mindboggle.mio.fetch_data import prep_tests >>> urls, fetch_data = prep_tests() >>> input_vtk = fetch_data(urls['left_travel_depth']) >>> labels_or_file = fetch_data(urls['left_folds']) >>> save_file = True >>> output_filestring = 'rescaled_scalars' >>> verbose = False >>> rescaled, rescaled_label_file = rescale_by_label(input_vtk, ... labels_or_file, save_file, output_filestring, verbose) >>> scalars1, name = read_scalars(input_vtk) >>> print('{0:0.5f}, {1:0.5f}'.format(max(scalars1), max(rescaled))) 34.95560, 1.00000 >>> print('{0:0.5f}, {1:0.5f}'.format(np.mean(scalars1), np.mean(rescaled))) 7.43822, 0.28389 View rescaled scalar values on surface (skip test): >>> from mindboggle.mio.plots import plot_surfaces # doctest: +SKIP >>> plot_surfaces(rescaled_label_file) # doctest: +SKIP """ 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) if verbose: 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) # Loop through labels: for label in unique_labels: if verbose: print( " Rescaling 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]) #print(max(scalars), 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 segment_fundi(fundus_per_fold, sulci=[], vtk_file='', save_file=False): """ Segment fundi by sulcus definitions. Parameters ---------- fundus_per_fold : list of integers fundus numbers for all vertices, labeled by fold (-1 for non-fundus vertices) sulci : numpy array or list of integers sulcus number for each vertex, used to filter and label fundi vtk_file : string (if save_file) VTK file with sulcus number for each vertex save_file : Boolean save output VTK file? Returns ------- fundus_per_sulcus : list of integers fundus numbers for all vertices, labeled by sulcus (-1 for non-fundus vertices) n_fundi : integer number of fundi fundus_per_sulcus_file : string (if save_file) output VTK file with fundus numbers (-1 for non-fundus vertices) Examples -------- >>> # Extract fundus from one or more sulci: >>> single_fold = True >>> import os >>> from mindboggle.mio.vtks import read_scalars >>> from mindboggle.features.fundi import extract_fundi, segment_fundi >>> from mindboggle.mio.plots import plot_surfaces >>> path = os.environ['MINDBOGGLE_DATA'] >>> vtk_file = os.path.join(path, 'arno', 'features', 'sulci.vtk') >>> sulci, name = read_scalars(vtk_file, True, True) >>> 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 >>> fundus_per_fold, o1, o2 = extract_fundi(folds, curv_file, depth_file, min_separation, erode_ratio, erode_min_size, save_file) >>> o1, o2, fundus_per_sulcus_file = segment_fundi(fundus_per_fold, sulci, vtk_file, save_file) >>> # >>> # View: >>> plot_surfaces(fundus_per_sulcus_file) """ # Extract a skeleton to connect endpoints in a fold: import os import numpy as np from mindboggle.mio.vtks import rewrite_scalars if isinstance(sulci, list): sulci = np.array(sulci) #------------------------------------------------------------------------- # Create fundi by segmenting fold fundi with overlapping sulcus labels: #------------------------------------------------------------------------- indices = [i for i,x in enumerate(fundus_per_fold) if x != -1] if indices and np.size(sulci): fundus_per_sulcus = -1 * np.ones(len(sulci)) fundus_per_sulcus[indices] = sulci[indices] n_fundi = len([x for x in np.unique(fundus_per_sulcus) if x != -1]) else: fundus_per_sulcus = [] n_fundi = 0 if n_fundi == 1: sdum = 'sulcus fundus' else: sdum = 'sulcus fundi' print(' Segmented {0} {1}'.format(n_fundi, sdum)) #------------------------------------------------------------------------- # Return fundi, number of fundi, and file name: #------------------------------------------------------------------------- fundus_per_sulcus_file = None if n_fundi > 0: fundus_per_sulcus = [int(x) for x in fundus_per_sulcus] if save_file and os.path.exists(vtk_file): fundus_per_sulcus_file = os.path.join(os.getcwd(), 'fundus_per_sulcus.vtk') rewrite_scalars(vtk_file, fundus_per_sulcus_file, fundus_per_sulcus, 'fundus_per_sulcus', fundus_per_sulcus) if not os.path.exists(fundus_per_sulcus_file): raise(IOError(fundus_per_sulcus_file + " not found")) return fundus_per_sulcus, n_fundi, fundus_per_sulcus_file
def segment_fundi(fundus_per_fold, sulci=[], vtk_file='', save_file=False, verbose=False): """ Segment fundi by sulcus definitions. Parameters ---------- fundus_per_fold : list of integers fundus numbers for all vertices, labeled by fold (-1 for non-fundus vertices) sulci : numpy array or list of integers sulcus number for each vertex, used to filter and label fundi vtk_file : string (if save_file) VTK file with sulcus number for each vertex save_file : bool save output VTK file? verbose : bool print statements? Returns ------- fundus_per_sulcus : list of integers fundus numbers for all vertices, labeled by sulcus (-1 for non-fundus vertices) n_fundi : integer number of fundi fundus_per_sulcus_file : string (if save_file) output VTK file with fundus numbers (-1 for non-fundus vertices) Examples -------- >>> # Extract fundus from one or more sulci: >>> import numpy as np >>> single_fold = True >>> from mindboggle.features.fundi import segment_fundi >>> from mindboggle.features.fundi import extract_fundi >>> from mindboggle.mio.vtks import read_scalars >>> 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']) >>> vtk_file = fetch_data(urls['left_sulci']) >>> sulci, name = read_scalars(vtk_file, True, True) >>> 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 >>> fundus_per_fold, o1, o2 = extract_fundi(folds, ... curv_file, depth_file, min_separation, erode_ratio, ... erode_min_size, save_file, verbose) >>> o1, o2, fundus_per_sulcus_file = segment_fundi(fundus_per_fold, ... sulci, vtk_file, save_file, verbose) >>> segment_numbers = [x for x in np.unique(o1) if x != -1] >>> lens = [] >>> if single_fold: ... for segment_number in segment_numbers: ... lens.append(len([x for x in o1 if x == segment_number])) >>> lens [14, 13, 88] View result (skip test): >>> from mindboggle.mio.plots import plot_surfaces >>> plot_surfaces(fundus_per_sulcus_file) # doctest: +SKIP """ # Extract a skeleton to connect endpoints in a fold: import os import numpy as np from mindboggle.mio.vtks import rewrite_scalars if isinstance(sulci, list): sulci = np.array(sulci) #------------------------------------------------------------------------- # Create fundi by segmenting fold fundi with overlapping sulcus labels: #------------------------------------------------------------------------- indices = [i for i, x in enumerate(fundus_per_fold) if x != -1] if indices and np.size(sulci): fundus_per_sulcus = -1 * np.ones(len(sulci)) fundus_per_sulcus[indices] = sulci[indices] n_fundi = len([x for x in np.unique(fundus_per_sulcus) if x != -1]) else: fundus_per_sulcus = [] n_fundi = 0 if n_fundi == 1: sdum = 'sulcus fundus' else: sdum = 'sulcus fundi' if verbose: print(' Segmented {0} {1}'.format(n_fundi, sdum)) #------------------------------------------------------------------------- # Return fundi, number of fundi, and file name: #------------------------------------------------------------------------- fundus_per_sulcus_file = None if n_fundi > 0: fundus_per_sulcus = [int(x) for x in fundus_per_sulcus] if save_file and os.path.exists(vtk_file): fundus_per_sulcus_file = os.path.join(os.getcwd(), 'fundus_per_sulcus.vtk') # Do not filter faces/points by scalars when saving file: rewrite_scalars(vtk_file, fundus_per_sulcus_file, fundus_per_sulcus, 'fundus_per_sulcus') if not os.path.exists(fundus_per_sulcus_file): raise IOError(fundus_per_sulcus_file + " not found") return fundus_per_sulcus, n_fundi, fundus_per_sulcus_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_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 segment_fundi(fundus_per_fold, sulci=[], vtk_file='', save_file=False): """ Segment fundi by sulcus definitions. Parameters ---------- fundus_per_fold : list of integers fundus numbers for all vertices, labeled by fold (-1 for non-fundus vertices) sulci : numpy array or list of integers sulcus number for each vertex, used to filter and label fundi vtk_file : string (if save_file) VTK file with sulcus number for each vertex save_file : Boolean save output VTK file? Returns ------- fundus_per_sulcus : list of integers fundus numbers for all vertices, labeled by sulcus (-1 for non-fundus vertices) n_fundi : integer number of fundi fundus_per_sulcus_file : string (if save_file) output VTK file with fundus numbers (-1 for non-fundus vertices) Examples -------- >>> # Extract fundus from one or more sulci: >>> single_fold = True >>> import os >>> from mindboggle.mio.vtks import read_scalars >>> from mindboggle.features.fundi import extract_fundi, segment_fundi >>> from mindboggle.mio.plots import plot_surfaces >>> path = os.environ['MINDBOGGLE_DATA'] >>> vtk_file = os.path.join(path, 'arno', 'features', 'sulci.vtk') >>> sulci, name = read_scalars(vtk_file, True, True) >>> 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 >>> fundus_per_fold, o1, o2 = extract_fundi(folds, curv_file, depth_file, min_separation, erode_ratio, erode_min_size, save_file) >>> o1, o2, fundus_per_sulcus_file = segment_fundi(fundus_per_fold, sulci, vtk_file, save_file) >>> # >>> # View: >>> plot_surfaces(fundus_per_sulcus_file) """ # Extract a skeleton to connect endpoints in a fold: import os import numpy as np from mindboggle.mio.vtks import rewrite_scalars if isinstance(sulci, list): sulci = np.array(sulci) #------------------------------------------------------------------------- # Create fundi by segmenting fold fundi with overlapping sulcus labels: #------------------------------------------------------------------------- indices = [i for i, x in enumerate(fundus_per_fold) if x != -1] if indices and np.size(sulci): fundus_per_sulcus = -1 * np.ones(len(sulci)) fundus_per_sulcus[indices] = sulci[indices] n_fundi = len([x for x in np.unique(fundus_per_sulcus) if x != -1]) else: fundus_per_sulcus = [] n_fundi = 0 if n_fundi == 1: sdum = 'sulcus fundus' else: sdum = 'sulcus fundi' print(' Segmented {0} {1}'.format(n_fundi, sdum)) #------------------------------------------------------------------------- # Return fundi, number of fundi, and file name: #------------------------------------------------------------------------- fundus_per_sulcus_file = None if n_fundi > 0: fundus_per_sulcus = [int(x) for x in fundus_per_sulcus] if save_file and os.path.exists(vtk_file): fundus_per_sulcus_file = os.path.join(os.getcwd(), 'fundus_per_sulcus.vtk') rewrite_scalars(vtk_file, fundus_per_sulcus_file, fundus_per_sulcus, 'fundus_per_sulcus', fundus_per_sulcus) if not os.path.exists(fundus_per_sulcus_file): raise (IOError(fundus_per_sulcus_file + " not found")) return fundus_per_sulcus, n_fundi, fundus_per_sulcus_file
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
# [[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] 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['curvedness'] = curvedness df.to_csv(curvatures_file, sep=';')
def extract_folds(depth_file, depth_threshold=2, min_fold_size=50, save_file=False, output_file='', background_value=-1, verbose=False): """ Use depth threshold to extract folds from a triangular surface mesh. A fold is a group of connected, deep vertices. To extract folds, a depth threshold is used to segment deep vertices of the surface mesh. We have observed in the histograms of travel depth measures of cortical surfaces that there is a rapidly decreasing distribution of low depth values (corresponding to the outer surface, or gyral crowns) with a long tail of higher depth values (corresponding to the folds). The find_depth_threshold function therefore computes a histogram of travel depth measures, smooths the histogram's bin values, convolves to compute slopes, and finds the depth value for the first bin with zero slope. The extract_folds function uses this depth value, segments deep vertices, and removes extremely small folds (empirically set at 50 vertices or fewer out of a total mesh size of over 100,000 vertices). Steps :: 1. Segment deep vertices as an initial set of folds. 2. Remove small folds. 3. Renumber folds. Note :: Removed option: Find and fill holes in the folds: Folds could have holes in areas shallower than the depth threshold. Calling fill_holes() could accidentally include very shallow areas (in an annulus-shaped fold, for example). However, we could include the argument exclude_range to check for any values from zero to min_hole_depth; holes would not be filled if they were to contain values within this range. Parameters ---------- depth_file : string surface mesh file in VTK format with faces and depth scalar values depth_threshold : float threshold defining the minimum depth for vertices to be in a fold min_fold_size : integer minimum fold size (number of vertices) 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 ------- folds : list of integers fold numbers for all vertices (-1 for non-fold vertices) n_folds : int number of folds folds_file : string (if save_file) name of output VTK file with fold IDs (-1 for non-fold vertices) Examples -------- >>> from mindboggle.features.folds import extract_folds >>> from mindboggle.mio.fetch_data import prep_tests >>> urls, fetch_data = prep_tests() >>> depth_file = fetch_data(urls['left_travel_depth'], '', '.vtk') >>> depth_threshold = 2.36089 >>> min_fold_size = 50 >>> save_file = True >>> output_file = 'extract_folds.vtk' >>> background_value = -1 >>> verbose = False >>> folds, n_folds, folds_file = extract_folds(depth_file, ... depth_threshold, min_fold_size, save_file, output_file, ... background_value, verbose) >>> n_folds 33 >>> lens = [len([x for x in folds if x == y]) for y in range(n_folds)] >>> lens[0:10] [726, 67241, 2750, 5799, 1151, 6360, 1001, 505, 228, 198] View folds (skip test): >>> from mindboggle.mio.plots import plot_surfaces # doctest: +SKIP >>> plot_surfaces('extract_folds.vtk') # doctest: +SKIP View folds without background (skip test): >>> from mindboggle.mio.plots import plot_surfaces # doctest: +SKIP >>> from mindboggle.mio.vtks import rewrite_scalars # doctest: +SKIP >>> rewrite_scalars(depth_file, 'extract_folds_no_background.vtk', folds, ... 'just_folds', folds, -1) # doctest: +SKIP >>> plot_surfaces('extract_folds_no_background.vtk') # doctest: +SKIP """ import os import numpy as np from time import time from mindboggle.mio.vtks import rewrite_scalars, read_vtk from mindboggle.guts.mesh import find_neighbors from mindboggle.guts.segment import segment_regions if verbose: print("Extract folds in surface mesh") t0 = time() # ------------------------------------------------------------------------ # Load depth values for all vertices # ------------------------------------------------------------------------ points, indices, lines, faces, depths, scalar_names, npoints, \ input_vtk = read_vtk(depth_file, return_first=True, return_array=True) # ------------------------------------------------------------------------ # Find the deepest vertices # ------------------------------------------------------------------------ indices_deep = [i for i, x in enumerate(depths) if x >= depth_threshold] if indices_deep: # -------------------------------------------------------------------- # Find neighbors for each vertex # -------------------------------------------------------------------- neighbor_lists = find_neighbors(faces, npoints) # -------------------------------------------------------------------- # Segment deep vertices as an initial set of folds # -------------------------------------------------------------------- if verbose: print(" Segment vertices deeper than {0:.2f} as folds".format( depth_threshold)) t1 = time() folds = segment_regions(indices_deep, neighbor_lists, 1, [], False, False, [], [], [], '', background_value, False) if verbose: print(' ...Segmented folds ({0:.2f} seconds)'.format(time() - t1)) # -------------------------------------------------------------------- # Remove small folds # -------------------------------------------------------------------- if min_fold_size > 1: if verbose: print(' Remove folds smaller than {0}'.format(min_fold_size)) unique_folds = [ x for x in np.unique(folds) if x != background_value ] for nfold in unique_folds: indices_fold = [i for i, x in enumerate(folds) if x == nfold] if len(indices_fold) < min_fold_size: folds[indices_fold] = background_value # -------------------------------------------------------------------- # Find and fill holes in the folds # Note: Surfaces surrounded by folds can be mistaken for holes, # so exclude_range includes outer surface values close to zero. # -------------------------------------------------------------------- # folds = fill_holes(folds, neighbor_lists, values=depths, # exclude_range=[0, min_hole_depth]) # -------------------------------------------------------------------- # Renumber folds so they are sequential. # NOTE: All vertices are included (-1 for non-fold vertices). # -------------------------------------------------------------------- renumber_folds = background_value * np.ones(npoints) fold_numbers = [x for x in np.unique(folds) if x != background_value] for i_fold, n_fold in enumerate(fold_numbers): fold_indices = [i for i, x in enumerate(folds) if x == n_fold] renumber_folds[fold_indices] = i_fold folds = renumber_folds folds = [int(x) for x in folds] n_folds = i_fold + 1 # Print statement if verbose: print(' ...Extracted {0} folds ({1:.2f} seconds)'.format( n_folds, time() - t0)) else: if verbose: print(' No deep vertices') # ------------------------------------------------------------------------ # Return folds, number of folds, file name # ------------------------------------------------------------------------ if save_file: if output_file: folds_file = output_file else: folds_file = os.path.join(os.getcwd(), 'folds.vtk') rewrite_scalars(depth_file, folds_file, folds, 'folds', [], background_value) if not os.path.exists(folds_file): raise IOError(folds_file + " not found") else: folds_file = None return folds, n_folds, folds_file
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 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 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 extract_subfolds(depth_file, folds, min_size=10, depth_factor=0.25, depth_ratio=0.1, tolerance=0.01, save_file=False): """ Use depth to segment folds into subfolds in a triangular surface mesh. Note :: The function extract_sulci() performs about the same whether folds or subfolds are used as input. The latter leads to some loss of small subfolds and possibly holes for small subfolds in the middle of other subfolds. Note about the watershed() function: The watershed() function performs individual seed growing from deep seeds, repeats segmentation from the resulting seeds until each seed's segment touches a boundary. The function segment() fills in the rest. Finally segments are joined if their seeds are too close to each other. Despite these precautions, the order of seed selection in segment() could possibly influence the resulting borders between adjoining segments. [The propagate() function is slower and insensitive to depth, but is not biased by seed order.] Parameters ---------- depth_file : string surface mesh file in VTK format with faces and depth scalar values folds : list of integers fold numbers for all vertices (-1 for non-fold vertices) min_size : integer minimum number of vertices for a subfold depth_factor : float watershed() depth_factor: factor to determine whether to merge two neighboring watershed catchment basins -- they are merged if the Euclidean distance between their basin seeds is less than this fraction of the maximum Euclidean distance between points having minimum and maximum depths depth_ratio : float watershed() depth_ratio: the minimum fraction of depth for a neighboring shallower watershed catchment basin (otherwise merged with the deeper basin) tolerance : float watershed() tolerance: tolerance for detecting differences in depth between vertices save_file : Boolean save output VTK file? Returns ------- subfolds : list of integers fold numbers for all vertices (-1 for non-fold vertices) n_subfolds : int number of subfolds subfolds_file : string (if save_file) name of output VTK file with fold IDs (-1 for non-fold vertices) Examples -------- >>> import os >>> from mindboggle.mio.vtks import read_scalars, rewrite_scalars >>> from mindboggle.guts.mesh import find_neighbors_from_file >>> from mindboggle.features.folds import extract_subfolds >>> from mindboggle.mio.plots import plot_surfaces >>> path = os.environ['MINDBOGGLE_DATA'] >>> depth_file = os.path.join(path, 'arno', 'shapes', 'lh.pial.travel_depth.vtk') >>> folds_file = os.path.join(path, 'arno', 'features', 'folds.vtk') >>> folds, name = read_scalars(folds_file) >>> min_size = 10 >>> depth_factor = 0.5 >>> depth_ratio = 0.1 >>> tolerance = 0.01 >>> # >>> subfolds, n_subfolds, subfolds_file = extract_subfolds(depth_file, >>> folds, min_size, depth_factor, depth_ratio, tolerance, True) >>> # >>> # View: >>> rewrite_scalars(depth_file, 'subfolds.vtk', subfolds, 'subfolds', subfolds) >>> plot_surfaces('subfolds.vtk') """ import os import numpy as np from time import time from mindboggle.mio.vtks import rewrite_scalars, read_vtk from mindboggle.guts.mesh import find_neighbors from mindboggle.guts.segment import segment, propagate, watershed print("Segment folds into subfolds") t0 = time() #------------------------------------------------------------------------- # Load depth values for all vertices #------------------------------------------------------------------------- faces, lines, indices, points, npoints, depths, \ name, input_vtk = read_vtk(depth_file, return_first=True, return_array=True) #------------------------------------------------------------------------- # Find neighbors for each vertex #------------------------------------------------------------------------- neighbor_lists = find_neighbors(faces, npoints) #------------------------------------------------------------------------- # Segment folds into "watershed basins" #------------------------------------------------------------------------- indices_folds = [i for i,x in enumerate(folds) if x != -1] subfolds, seed_indices = watershed(depths, points, indices_folds, neighbor_lists, min_size, depth_factor=0.25, depth_ratio=0.1, tolerance=0.01, regrow=True) # Print statement n_subfolds = len([x for x in np.unique(subfolds) if x != -1]) print(' ...Extracted {0} subfolds ({1:.2f} seconds)'. format(n_subfolds, time() - t0)) #------------------------------------------------------------------------- # Return subfolds, number of subfolds, file name #------------------------------------------------------------------------- if save_file: subfolds_file = os.path.join(os.getcwd(), 'subfolds.vtk') rewrite_scalars(depth_file, subfolds_file, subfolds, 'subfolds', subfolds) if not os.path.exists(subfolds_file): raise(IOError(subfolds_file + " not found")) else: subfolds_file = None return subfolds, n_subfolds, subfolds_file
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 extract_folds(depth_file, depth_threshold=2, min_fold_size=50, save_file=False, output_file='', background_value=-1, verbose=False): """ Use depth threshold to extract folds from a triangular surface mesh. A fold is a group of connected, deep vertices. To extract folds, a depth threshold is used to segment deep vertices of the surface mesh. We have observed in the histograms of travel depth measures of cortical surfaces that there is a rapidly decreasing distribution of low depth values (corresponding to the outer surface, or gyral crowns) with a long tail of higher depth values (corresponding to the folds). The find_depth_threshold function therefore computes a histogram of travel depth measures, smooths the histogram's bin values, convolves to compute slopes, and finds the depth value for the first bin with zero slope. The extract_folds function uses this depth value, segments deep vertices, and removes extremely small folds (empirically set at 50 vertices or fewer out of a total mesh size of over 100,000 vertices). Steps :: 1. Segment deep vertices as an initial set of folds. 2. Remove small folds. 3. Renumber folds. Note :: Removed option: Find and fill holes in the folds: Folds could have holes in areas shallower than the depth threshold. Calling fill_holes() could accidentally include very shallow areas (in an annulus-shaped fold, for example). However, we could include the argument exclude_range to check for any values from zero to min_hole_depth; holes would not be filled if they were to contain values within this range. Parameters ---------- depth_file : string surface mesh file in VTK format with faces and depth scalar values depth_threshold : float threshold defining the minimum depth for vertices to be in a fold min_fold_size : integer minimum fold size (number of vertices) 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 ------- folds : list of integers fold numbers for all vertices (-1 for non-fold vertices) n_folds : int number of folds folds_file : string (if save_file) name of output VTK file with fold IDs (-1 for non-fold vertices) Examples -------- >>> from mindboggle.features.folds import extract_folds >>> from mindboggle.mio.fetch_data import prep_tests >>> urls, fetch_data = prep_tests() >>> depth_file = fetch_data(urls['left_travel_depth'], '', '.vtk') >>> depth_threshold = 2.36089 >>> min_fold_size = 50 >>> save_file = True >>> output_file = 'extract_folds.vtk' >>> background_value = -1 >>> verbose = False >>> folds, n_folds, folds_file = extract_folds(depth_file, ... depth_threshold, min_fold_size, save_file, output_file, ... background_value, verbose) >>> n_folds 33 >>> lens = [len([x for x in folds if x == y]) for y in range(n_folds)] >>> lens[0:10] [726, 67241, 2750, 5799, 1151, 6360, 1001, 505, 228, 198] View folds (skip test): >>> from mindboggle.mio.plots import plot_surfaces # doctest: +SKIP >>> plot_surfaces('extract_folds.vtk') # doctest: +SKIP View folds without background (skip test): >>> from mindboggle.mio.plots import plot_surfaces # doctest: +SKIP >>> from mindboggle.mio.vtks import rewrite_scalars # doctest: +SKIP >>> rewrite_scalars(depth_file, 'extract_folds_no_background.vtk', folds, ... 'just_folds', folds, -1) # doctest: +SKIP >>> plot_surfaces('extract_folds_no_background.vtk') # doctest: +SKIP """ import os import numpy as np from time import time from mindboggle.mio.vtks import rewrite_scalars, read_vtk from mindboggle.guts.mesh import find_neighbors from mindboggle.guts.segment import segment_regions if verbose: print("Extract folds in surface mesh") t0 = time() # ------------------------------------------------------------------------ # Load depth values for all vertices # ------------------------------------------------------------------------ points, indices, lines, faces, depths, scalar_names, npoints, \ input_vtk = read_vtk(depth_file, return_first=True, return_array=True) # ------------------------------------------------------------------------ # Find the deepest vertices # ------------------------------------------------------------------------ indices_deep = [i for i,x in enumerate(depths) if x >= depth_threshold] if indices_deep: # -------------------------------------------------------------------- # Find neighbors for each vertex # -------------------------------------------------------------------- neighbor_lists = find_neighbors(faces, npoints) # -------------------------------------------------------------------- # Segment deep vertices as an initial set of folds # -------------------------------------------------------------------- if verbose: print(" Segment vertices deeper than {0:.2f} as folds".format(depth_threshold)) t1 = time() folds = segment_regions(indices_deep, neighbor_lists, 1, [], False, False, [], [], [], '', background_value, False) if verbose: print(' ...Segmented folds ({0:.2f} seconds)'.format(time() - t1)) # -------------------------------------------------------------------- # Remove small folds # -------------------------------------------------------------------- if min_fold_size > 1: if verbose: print(' Remove folds smaller than {0}'.format(min_fold_size)) unique_folds = [x for x in np.unique(folds) if x != background_value] for nfold in unique_folds: indices_fold = [i for i,x in enumerate(folds) if x == nfold] if len(indices_fold) < min_fold_size: folds[indices_fold] = background_value # -------------------------------------------------------------------- # Find and fill holes in the folds # Note: Surfaces surrounded by folds can be mistaken for holes, # so exclude_range includes outer surface values close to zero. # -------------------------------------------------------------------- # folds = fill_holes(folds, neighbor_lists, values=depths, # exclude_range=[0, min_hole_depth]) # -------------------------------------------------------------------- # Renumber folds so they are sequential. # NOTE: All vertices are included (-1 for non-fold vertices). # -------------------------------------------------------------------- renumber_folds = background_value * np.ones(npoints) fold_numbers = [x for x in np.unique(folds) if x != background_value] for i_fold, n_fold in enumerate(fold_numbers): fold_indices = [i for i,x in enumerate(folds) if x == n_fold] renumber_folds[fold_indices] = i_fold folds = renumber_folds folds = [int(x) for x in folds] n_folds = i_fold + 1 # Print statement if verbose: print(' ...Extracted {0} folds ({1:.2f} seconds)'. format(n_folds, time() - t0)) else: if verbose: print(' No deep vertices') # ------------------------------------------------------------------------ # Return folds, number of folds, file name # ------------------------------------------------------------------------ if save_file: if output_file: folds_file = output_file else: folds_file = os.path.join(os.getcwd(), 'folds.vtk') rewrite_scalars(depth_file, folds_file, folds, 'folds', [], background_value) if not os.path.exists(folds_file): raise IOError(folds_file + " not found") else: folds_file = None return folds, n_folds, folds_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 extract_subfolds(depth_file, folds, min_size=10, depth_factor=0.25, depth_ratio=0.1, tolerance=0.01, save_file=False): """ Use depth to segment folds into subfolds in a triangular surface mesh. Note :: The function extract_sulci() performs about the same whether folds or subfolds are used as input. The latter leads to some loss of small subfolds and possibly holes for small subfolds in the middle of other subfolds. Note about the watershed() function: The watershed() function performs individual seed growing from deep seeds, repeats segmentation from the resulting seeds until each seed's segment touches a boundary. The function segment() fills in the rest. Finally segments are joined if their seeds are too close to each other. Despite these precautions, the order of seed selection in segment() could possibly influence the resulting borders between adjoining segments. [The propagate() function is slower and insensitive to depth, but is not biased by seed order.] Parameters ---------- depth_file : string surface mesh file in VTK format with faces and depth scalar values folds : list of integers fold numbers for all vertices (-1 for non-fold vertices) min_size : integer minimum number of vertices for a subfold depth_factor : float watershed() depth_factor: factor to determine whether to merge two neighboring watershed catchment basins -- they are merged if the Euclidean distance between their basin seeds is less than this fraction of the maximum Euclidean distance between points having minimum and maximum depths depth_ratio : float watershed() depth_ratio: the minimum fraction of depth for a neighboring shallower watershed catchment basin (otherwise merged with the deeper basin) tolerance : float watershed() tolerance: tolerance for detecting differences in depth between vertices save_file : Boolean save output VTK file? Returns ------- subfolds : list of integers fold numbers for all vertices (-1 for non-fold vertices) n_subfolds : int number of subfolds subfolds_file : string (if save_file) name of output VTK file with fold IDs (-1 for non-fold vertices) Examples -------- >>> import os >>> from mindboggle.mio.vtks import read_scalars, rewrite_scalars >>> from mindboggle.guts.mesh import find_neighbors_from_file >>> from mindboggle.features.folds import extract_subfolds >>> from mindboggle.mio.plots import plot_surfaces >>> path = os.environ['MINDBOGGLE_DATA'] >>> depth_file = os.path.join(path, 'arno', 'shapes', 'lh.pial.travel_depth.vtk') >>> folds_file = os.path.join(path, 'arno', 'features', 'folds.vtk') >>> folds, name = read_scalars(folds_file) >>> min_size = 10 >>> depth_factor = 0.5 >>> depth_ratio = 0.1 >>> tolerance = 0.01 >>> # >>> subfolds, n_subfolds, subfolds_file = extract_subfolds(depth_file, >>> folds, min_size, depth_factor, depth_ratio, tolerance, True) >>> # >>> # View: >>> rewrite_scalars(depth_file, 'subfolds.vtk', subfolds, 'subfolds', subfolds) >>> plot_surfaces('subfolds.vtk') """ import os import numpy as np from time import time from mindboggle.mio.vtks import rewrite_scalars, read_vtk from mindboggle.guts.mesh import find_neighbors from mindboggle.guts.segment import segment, propagate, watershed print("Segment folds into subfolds") t0 = time() #------------------------------------------------------------------------- # Load depth values for all vertices #------------------------------------------------------------------------- points, indices, lines, faces, depths, scalar_names, npoints, \ input_vtk = read_vtk(depth_file, return_first=True, return_array=True) #------------------------------------------------------------------------- # Find neighbors for each vertex #------------------------------------------------------------------------- neighbor_lists = find_neighbors(faces, npoints) #------------------------------------------------------------------------- # Segment folds into "watershed basins" #------------------------------------------------------------------------- indices_folds = [i for i, x in enumerate(folds) if x != -1] subfolds, seed_indices = watershed(depths, points, indices_folds, neighbor_lists, min_size, depth_factor=0.25, depth_ratio=0.1, tolerance=0.01, regrow=True) # Print statement n_subfolds = len([x for x in np.unique(subfolds) if x != -1]) print(' ...Extracted {0} subfolds ({1:.2f} seconds)'.format( n_subfolds, time() - t0)) #------------------------------------------------------------------------- # Return subfolds, number of subfolds, file name #------------------------------------------------------------------------- if save_file: subfolds_file = os.path.join(os.getcwd(), 'subfolds.vtk') rewrite_scalars(depth_file, subfolds_file, subfolds, 'subfolds', subfolds) if not os.path.exists(subfolds_file): raise (IOError(subfolds_file + " not found")) else: subfolds_file = None return subfolds, n_subfolds, subfolds_file
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 : bool set all rescaled values greater than 1 to 1.0? save_file : bool 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 numpy as np >>> from mindboggle.guts.mesh import rescale_by_neighborhood >>> from mindboggle.mio.vtks import read_scalars >>> from mindboggle.mio.plots import plot_surfaces >>> from mindboggle.mio.fetch_data import prep_tests >>> urls, fetch_data = prep_tests() >>> input_vtk = fetch_data(urls['left_travel_depth']) >>> indices = [] >>> nedges = 10 >>> p = 99 >>> set_max_to_1 = True >>> save_file = True >>> output_filestring = 'rescaled_scalars' >>> background_value = -1 >>> rescaled, rescaled_file = rescale_by_neighborhood(input_vtk, ... indices, nedges, p, set_max_to_1, save_file, output_filestring, ... background_value) >>> scalars1, name = read_scalars(input_vtk) >>> print('{0:0.5f}, {1:0.5f}'.format(max(scalars1), max(rescaled))) 34.95560, 1.00000 >>> print('{0:0.5f}, {1:0.5f}'.format(np.mean(scalars1), np.mean(rescaled))) 7.43822, 0.44950 View rescaled scalar values on surface (skip test): >>> from mindboggle.mio.plots import plot_surfaces # doctest: +SKIP >>> plot_surfaces(rescaled_file) # doctest: +SKIP """ 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