def evaluate_surface_overlaps(labels, index, table1, table2,
output_file='', save_output=True):
"""
Measure surface overlap per label by comparing Mindboggle vertices tables.
Parameters
----------
labels : list
cortical label indices to measure surface overlap
index : integer
index (starting from zero) to column of table containing label indices
table1 : string
table with index labels for scalar values
table2 : string
table with index labels for scalar values
output_file : string
(optional) output file name
Returns
-------
dice_overlaps : numpy array
Dice overlap values
jacc_overlaps : numpy array
Jaccard overlap values
output_file : string
(optional) output file name
save_output : Boolean
save output file?
Examples
--------
>>> import os
>>> from mindboggle.evaluate.evaluate_labels import evaluate_surface_overlaps
>>> from mindboggle.mio.labels import DKTprotocol
>>> dkt = DKTprotocol()
>>> labels = dkt.cerebrum_cortex_DKT31_numbers
>>> index = 1
>>> path = '/homedir/mindboggled'
>>> table1 = os.path.join(path, 'Twins-2-1', 'tables', 'left_cortical_surface', 'vertices.csv')
>>> table2 = os.path.join(path, 'Twins-2-1', 'tables', 'left_cortical_surface', 'vertices.csv')
>>> output_file = ''
>>> save_output = True
>>> evaluate_surface_overlaps(labels, index, table1, table2, output_file=output_file, save_output=save_output)
"""
import pandas as pd
from mindboggle.guts.compute import compute_overlaps
# Load surface label tables:
df1 = pd.read_csv(table1)
df2 = pd.read_csv(table2)
list1 = df1.iloc[:, index]
list2 = df2.iloc[:, index]
print(list1)
dice_overlaps, jacc_overlaps, output_file = compute_overlaps(labels,
list1, list2, output_file=output_file, save_output=save_output)
return dice_overlaps, jacc_overlaps, output_file
def evaluate_volume_overlaps(labels, file1, file2,
output_file='', save_output=True):
"""
Compute overlap between individual label regions
in source and target nifti (nii.gz) images.
Parameters
----------
labels : list
label indices
file1 : string
source image, consisting of index-labeled pixels/voxels
file2 : string
target image, consisting of index-labeled pixels/voxels
output_file : string
(optional) output file name
save_output : bool
save output file?
Returns
-------
dice_overlaps : numpy array
Dice overlap values
jacc_overlaps : numpy array
Jaccard overlap values
output_file : string
output text file name with overlap values
Examples
--------
>>> # Compare FreeSurfer and ants labels for the same brain:
>>> import os
>>> from mindboggle.evaluate.evaluate_labels import evaluate_volume_overlaps
>>> from mindboggle.mio.labels import DKTprotocol
>>> from mindboggle.mio.fetch_data import prep_tests
>>> urls, fetch_data = prep_tests()
>>> file1 = fetch_data(urls['freesurfer_labels'], '', '.nii.gz')
>>> file2 = fetch_data(urls['ants_labels'], '', '.nii.gz')
>>> dkt = DKTprotocol()
>>> labels = dkt.cerebrum_cortex_DKT31_numbers
>>> output_file = ''
>>> save_output = True
>>> evaluate_volume_overlaps(labels, file1, file2,
... output_file=output_file, save_output=save_output) # doctest: +SKIP
"""
import nibabel as nb
from mindboggle.guts.compute import compute_overlaps
# Load labeled image volumes:
list1 = nb.load(file1).get_data().ravel()
list2 = nb.load(file2).get_data().ravel()
dice_overlaps, jacc_overlaps, output_file = compute_overlaps(labels,
list1, list2, output_file=output_file, save_output=save_output)
return dice_overlaps, jacc_overlaps, output_file
def evaluate_volume_overlaps(labels, file1, file2,
output_file='', save_output=True):
"""
Compute overlap between individual label regions
in source and target nifti (nii.gz) images.
Parameters
----------
labels : list
label indices
file1 : string
source image, consisting of index-labeled pixels/voxels
file2 : string
target image, consisting of index-labeled pixels/voxels
output_file : string
(optional) output file name
save_output : bool
save output file?
Returns
-------
dice_overlaps : numpy array
Dice overlap values
jacc_overlaps : numpy array
Jaccard overlap values
output_file : string
output text file name with overlap values
Examples
--------
>>> # Compare FreeSurfer and ants labels for the same brain:
>>> import os
>>> from mindboggle.evaluate.evaluate_labels import evaluate_volume_overlaps
>>> from mindboggle.mio.labels import DKTprotocol
>>> from mindboggle.mio.fetch_data import prep_tests
>>> urls, fetch_data = prep_tests()
>>> file1 = fetch_data(urls['freesurfer_labels'], '', '.nii.gz')
>>> file2 = fetch_data(urls['ants_labels'], '', '.nii.gz')
>>> dkt = DKTprotocol()
>>> labels = dkt.cerebrum_cortex_DKT31_numbers
>>> output_file = ''
>>> save_output = True
>>> evaluate_volume_overlaps(labels, file1, file2,
... output_file=output_file, save_output=save_output) # doctest: +SKIP
"""
import nibabel as nb
from mindboggle.guts.compute import compute_overlaps
# Load labeled image volumes:
list1 = nb.load(file1).get_data().ravel()
list2 = nb.load(file2).get_data().ravel()
dice_overlaps, jacc_overlaps, output_file = compute_overlaps(labels,
list1, list2, output_file=output_file, save_output=save_output)
return dice_overlaps, jacc_overlaps, output_file
def evaluate_volume_overlaps(labels, file1, file2,
output_file='', save_output=True):
"""
Compute overlap between individual label regions
in source and target nifti (nii.gz) images.
Parameters
----------
labels : list
label indices
file1 : string
source image, consisting of index-labeled pixels/voxels
file2 : string
target image, consisting of index-labeled pixels/voxels
output_file : string
(optional) output file name
save_output : Boolean
save output file?
Returns
-------
dice_overlaps : numpy array
Dice overlap values
jacc_overlaps : numpy array
Jaccard overlap values
output_file : string
output text file name with overlap values
Examples
--------
>>> import os
>>> from mindboggle.evaluate.evaluate_labels import evaluate_volume_overlaps
>>> from mindboggle.mio.labels import DKTprotocol
>>> path = '/homedir/mindboggled'
>>> file1 = os.path.join(path, 'Twins-2-1', 'labels', 'freesurfer_wmparc_filled_labels.nii.gz')
>>> file2 = os.path.join(path, 'Twins-2-1', 'labels', 'freesurfer_wmparc_filled_labels.nii.gz')
>>> dkt = DKTprotocol()
>>> labels = dkt.cerebrum_cortex_DKT31_numbers
>>> output_file = ''
>>> save_output = True
>>> evaluate_volume_overlaps(labels, file1, file2, output_file=output_file, save_output=save_output)
"""
import nibabel as nb
from mindboggle.guts.compute import compute_overlaps
# Load labeled image volumes:
list1 = nb.load(file1).get_data().ravel()
list2 = nb.load(file2).get_data().ravel()
dice_overlaps, jacc_overlaps, output_file = compute_overlaps(labels,
list1, list2, output_file=output_file, save_output=save_output)
return dice_overlaps, jacc_overlaps, output_file
def evaluate_surface_overlaps(labels,
index,
table1,
table2,
output_file='',
save_output=True):
"""
Measure surface overlap per label by comparing Mindboggle vertices tables.
Parameters
----------
labels : list
cortical label indices to measure surface overlap
index : integer
index (starting from zero) to column of table containing label indices
table1 : string
table with index labels for scalar values
table2 : string
table with index labels for scalar values
output_file : string
(optional) output file name
Returns
-------
dice_overlaps : numpy array
Dice overlap values
jacc_overlaps : numpy array
Jaccard overlap values
output_file : string
(optional) output file name
save_output : bool
save output file?
Examples
--------
>>> # Compare volume label overlaps in trivial case: brain with itself:
>>> import os
>>> from mindboggle.evaluate.evaluate_labels import evaluate_surface_overlaps
>>> from mindboggle.mio.labels import DKTprotocol
>>> from mindboggle.mio.fetch_data import prep_tests
>>> urls, fetch_data = prep_tests()
>>> table1 = fetch_data(urls['left_vertices_table'])
>>> table2 = fetch_data(urls['left_vertices_table'])
>>> dkt = DKTprotocol()
>>> labels = dkt.cerebrum_cortex_DKT31_numbers
>>> index = 1
>>> output_file = ''
>>> save_output = True
>>> evaluate_surface_overlaps(labels, index, table1, table2,
... output_file=output_file, save_output=save_output) # doctest: +SKIP
"""
import pandas as pd
from mindboggle.guts.compute import compute_overlaps
# Load surface label tables:
df1 = pd.read_csv(table1)
df2 = pd.read_csv(table2)
list1 = df1.iloc[:, index]
list2 = df2.iloc[:, index]
print(list1)
dice_overlaps, jacc_overlaps, output_file = compute_overlaps(
labels, list1, list2, output_file=output_file, save_output=save_output)
return dice_overlaps, jacc_overlaps, output_file
def evaluate_surface_overlaps(labels, index, table1, table2,
output_file='', save_output=True):
"""
Measure surface overlap per label by comparing Mindboggle vertices tables.
Parameters
----------
labels : list
cortical label indices to measure surface overlap
index : integer
index (starting from zero) to column of table containing label indices
table1 : string
table with index labels for scalar values
table2 : string
table with index labels for scalar values
output_file : string
(optional) output file name
Returns
-------
dice_overlaps : numpy array
Dice overlap values
jacc_overlaps : numpy array
Jaccard overlap values
output_file : string
(optional) output file name
save_output : bool
save output file?
Examples
--------
>>> # Compare volume label overlaps in trivial case: brain with itself:
>>> import os
>>> from mindboggle.evaluate.evaluate_labels import evaluate_surface_overlaps
>>> from mindboggle.mio.labels import DKTprotocol
>>> from mindboggle.mio.fetch_data import prep_tests
>>> urls, fetch_data = prep_tests()
>>> table1 = fetch_data(urls['left_vertices_table'], '', '.csv')
>>> table2 = fetch_data(urls['left_vertices_table'], '', '.csv')
>>> dkt = DKTprotocol()
>>> labels = dkt.cerebrum_cortex_DKT31_numbers
>>> index = 1
>>> output_file = ''
>>> save_output = True
>>> evaluate_surface_overlaps(labels, index, table1, table2,
... output_file=output_file, save_output=save_output) # doctest: +SKIP
"""
import pandas as pd
from mindboggle.guts.compute import compute_overlaps
# Load surface label tables:
df1 = pd.read_csv(table1)
df2 = pd.read_csv(table2)
list1 = df1.iloc[:, index]
list2 = df2.iloc[:, index]
print(list1)
dice_overlaps, jacc_overlaps, output_file = compute_overlaps(labels,
list1, list2, output_file=output_file, save_output=save_output)
return dice_overlaps, jacc_overlaps, output_file