def volume_per_brain_region(input_file, include_labels=[], exclude_labels=[],
label_names=[], save_table=False,
output_table='', verbose=False):
"""
Compute volume per labeled region in a nibabel-readable image.
Computing the volume per labeled region is very straightforward:
this function simply multiplies the volume per voxel by the number
of voxels per region.
Note: Results are truncated at three decimal places because we found that
volume label propagation led to differences in the third decimal place.
Parameters
----------
input_file : string
name of image file, consisting of index-labeled pixels/voxels
include_labels : list of integers
labels to include
(if empty, use unique numbers in image volume file)
exclude_labels : list of integers
labels to be excluded
label_names : list of strings
label names corresponding to labels to include
save_table : bool
save output table file with labels and volume values?
output_table : string
name of output table file with labels and volume values
verbose : bool
print statements?
Returns
-------
unique_labels : list of integers
unique label numbers (default -1)
volumes : list of floats
volume for each label (default -1)
output_table : string
name of output volume table file (if output_table not empty)
Examples
--------
>>> import os
>>> import numpy as np
>>> from mindboggle.mio.labels import DKTprotocol
>>> from mindboggle.shapes.volume_shapes import volume_per_brain_region
>>> from mindboggle.mio.fetch_data import prep_tests
>>> urls, fetch_data = prep_tests()
>>> input_file = fetch_data(urls['freesurfer_labels'], '', '.nii.gz')
>>> dkt = DKTprotocol()
>>> include_labels = dkt.label_numbers
>>> exclude_labels = []
>>> label_names = dkt.label_names
>>> save_table = True
>>> output_table = 'volumes.csv'
>>> verbose = False
>>> unique_labels, volumes, table = volume_per_brain_region(input_file,
... include_labels, exclude_labels, label_names, save_table,
... output_table, verbose)
>>> [np.float("{0:.{1}f}".format(x, 5))
... for x in [y for y in volumes if y > 0][0:5]]
[971.99797, 2413.99496, 2192.99543, 8328.98262, 2940.99386]
>>> [np.float("{0:.{1}f}".format(x, 5))
... for x in [y for y in volumes if y > 0][5:10]]
[1997.99583, 10905.97725, 11318.97639, 10789.97749, 2700.99437]
"""
import os
import numpy as np
import nibabel as nb
from io import open
from mindboggle.guts.compute import count_per_label
# Load labeled image volumes:
img = nb.load(input_file)
volume_per_voxel = np.product(img.header.get_zooms())
labels = img.get_data().ravel()
unique_labels, counts = count_per_label(labels, include_labels,
exclude_labels)
volumes = [volume_per_voxel * x for x in counts]
# Output table:
if save_table:
if output_table:
output_table = os.path.join(os.getcwd(), output_table)
else:
output_table = os.path.join(os.getcwd(),
'volume_for_each_label.csv')
fid = open(output_table, 'w', encoding='utf-8')
if len(label_names) == len(unique_labels):
fid.write("name, ID, volume\n")
else:
fid.write("ID, volume\n")
# Loop through labels:
for ilabel, label in enumerate(unique_labels):
if volumes[ilabel]:
if len(label_names) == len(unique_labels):
if verbose:
print('{0} ({1}) volume = {2:2.3f}mm^3\n'.format(
label_names[ilabel], label, volumes[ilabel]))
fid.write('{0}, {1}, {2:2.3f}\n'.format(
label_names[ilabel], label, volumes[ilabel]))
else:
if verbose:
print('{0} volume = {1:2.3f}mm^3\n'.format(
label, volumes[ilabel]))
fid.write('{0}, {1:2.3f}\n'.format(label, volumes[ilabel]))
else:
output_table = ''
return unique_labels, volumes, output_table
def volume_per_brain_region(input_file, include_labels=[], exclude_labels=[],
label_names=[], save_table=False,
output_table='', verbose=False):
"""
Compute volume per labeled region in a nibabel-readable image.
Note: Results are truncated at three decimal places because we found that
volume label propagation led to differences in the third decimal place.
Parameters
----------
input_file : string
name of image file, consisting of index-labeled pixels/voxels
include_labels : list of integers
labels to include
(if empty, use unique numbers in image volume file)
exclude_labels : list of integers
labels to be excluded
label_names : list of strings
label names corresponding to labels to include
save_table : bool
save output table file with labels and volume values?
output_table : string
name of output table file with labels and volume values
verbose : bool
print statements?
Returns
-------
unique_labels : list of integers
unique label numbers (default -1)
volumes : list of floats
volume for each label (default -1)
output_table : string
name of output volume table file (if output_table not empty)
Examples
--------
>>> import os
>>> import numpy as np
>>> from mindboggle.mio.labels import DKTprotocol
>>> from mindboggle.shapes.volume_shapes import volume_per_brain_region
>>> from mindboggle.mio.fetch_data import prep_tests
>>> urls, fetch_data = prep_tests()
>>> input_file = fetch_data(urls['freesurfer_labels'])
>>> dkt = DKTprotocol()
>>> include_labels = dkt.label_numbers
>>> exclude_labels = []
>>> label_names = dkt.label_names
>>> save_table = True
>>> output_table = 'volumes.csv'
>>> verbose = False
>>> unique_labels, volumes, table = volume_per_brain_region(input_file,
... include_labels, exclude_labels, label_names, save_table,
... output_table, verbose)
>>> print(np.array_str(np.array(volumes[0:5]),
... precision=5, suppress_small=True))
[ 971.99797 2413.99496 2192.99543 8328.98262 2940.99386]
>>> print(np.array_str(np.array(volumes[5:10]),
... precision=5, suppress_small=True))
[ 1997.99583 10905.97725 11318.97639 10789.97749 2700.99437]
"""
import os
import numpy as np
import nibabel as nb
from io import open
from mindboggle.guts.compute import count_per_label
# Load labeled image volumes:
img = nb.load(input_file)
hdr = img.get_header()
volume_per_voxel = np.product(hdr.get_zooms())
labels = img.get_data().ravel()
unique_labels, counts = count_per_label(labels, include_labels,
exclude_labels)
volumes = [volume_per_voxel * x for x in counts]
# Output table:
if save_table:
if output_table:
output_table = os.path.join(os.getcwd(), output_table)
else:
output_table = os.path.join(os.getcwd(),
'volume_for_each_label.csv')
fid = open(output_table, 'w')
if len(label_names) == len(unique_labels):
fid.write("name, ID, volume\n")
else:
fid.write("ID, volume\n")
# Loop through labels:
for ilabel, label in enumerate(unique_labels):
if volumes[ilabel]:
if len(label_names) == len(unique_labels):
if verbose:
print('{0} ({1}) volume = {2:2.3f}mm^3\n'.format(
label_names[ilabel], label, volumes[ilabel]))
fid.write('{0}, {1}, {2:2.3f}\n'.format(
label_names[ilabel], label, volumes[ilabel]))
else:
if verbose:
print('{0} volume = {1:2.3f}mm^3\n'.format(
label, volumes[ilabel]))
fid.write('{0}, {1:2.3f}\n'.format(label, volumes[ilabel]))
else:
output_table = ''
return unique_labels, volumes, output_table
def volume_per_brain_region(input_file,
include_labels=[],
exclude_labels=[],
label_names=[],
save_table=False,
output_table=''):
"""
Compute volume per labeled region in a nibabel-readable image.
Note: Results are truncated at three decimal places because we found that
volume label propagation led to differences in the third decimal place.
Parameters
----------
input_file : string
name of image file, consisting of index-labeled pixels/voxels
include_labels : list of integers
labels to include
(if empty, use unique numbers in image volume file)
exclude_labels : list of integers
labels to be excluded
label_names : list of strings
label names corresponding to labels to include
save_table : Boolean
save output table file with labels and volume values?
output_table : string
name of output table file with labels and volume values
Returns
-------
unique_labels : list of integers
unique label numbers (default -1)
volumes : list of floats
volume for each label (default -1)
output_table : string
name of output volume table file (if output_table not empty)
Examples
--------
>>> import os
>>> from mindboggle.mio.labels import DKTprotocol
>>> from mindboggle.shapes.volume_shapes import volume_per_brain_region
>>> input_file = os.path.join(os.environ['HOME'], 'mindboggled', 'Twins-2-1old', 'labels', 'ants_filled_labels.nii.gz')
>>> dkt = DKTprotocol()
>>> include_labels = dkt.label_numbers
>>> exclude_labels = []
>>> label_names = dkt.label_names
>>> save_table = True
>>> output_table = 'volumes.csv'
>>> unique_labels, volumes, output_table = volume_per_brain_region(input_file, include_labels, exclude_labels, label_names, save_table, output_table)
>>> print(volumes)
"""
import os
import numpy as np
import nibabel as nb
from mindboggle.guts.compute import count_per_label
# Load labeled image volumes:
img = nb.load(input_file)
hdr = img.get_header()
volume_per_voxel = np.product(hdr.get_zooms())
labels = img.get_data().ravel()
unique_labels, counts = count_per_label(labels, include_labels,
exclude_labels)
volumes = [volume_per_voxel * x for x in counts]
# Output table:
if save_table:
if output_table:
output_table = os.path.join(os.getcwd(), output_table)
else:
output_table = os.path.join(os.getcwd(),
'volume_for_each_label.csv')
fid = open(output_table, 'w')
if len(label_names) == len(unique_labels):
fid.write("name, ID, volume\n")
else:
fid.write("ID, volume\n")
# Loop through labels:
for ilabel, label in enumerate(unique_labels):
if len(label_names) == len(unique_labels):
fid.write('{0}, {1}, {2:2.3f}\n'.format(
label_names[ilabel], label, volumes[ilabel]))
else:
fid.write('{0}, {1:2.3f}\n'.format(label, volumes[ilabel]))
else:
output_table = ''
return unique_labels, volumes, output_table
def volume_per_brain_region(input_file, include_labels=[], exclude_labels=[],
label_names=[], save_table=False, output_table=''):
"""
Compute volume per labeled region in a nibabel-readable image.
Note: Results are truncated at three decimal places because we found that
volume label propagation led to differences in the third decimal place.
Parameters
----------
input_file : string
name of image file, consisting of index-labeled pixels/voxels
include_labels : list of integers
labels to include
(if empty, use unique numbers in image volume file)
exclude_labels : list of integers
labels to be excluded
label_names : list of strings
label names corresponding to labels to include
save_table : Boolean
save output table file with labels and volume values?
output_table : string
name of output table file with labels and volume values
Returns
-------
unique_labels : list of integers
unique label numbers (default -1)
volumes : list of floats
volume for each label (default -1)
output_table : string
name of output volume table file (if output_table not empty)
Examples
--------
>>> import os
>>> from mindboggle.mio.labels import DKTprotocol
>>> from mindboggle.shapes.volume_shapes import volume_per_brain_region
>>> input_file = os.path.join(os.environ['HOME'], 'mindboggled', 'Twins-2-1old', 'labels', 'ants_filled_labels.nii.gz')
>>> dkt = DKTprotocol()
>>> include_labels = dkt.label_numbers
>>> exclude_labels = []
>>> label_names = dkt.label_names
>>> save_table = True
>>> output_table = 'volumes.csv'
>>> unique_labels, volumes, output_table = volume_per_brain_region(input_file, include_labels, exclude_labels, label_names, save_table, output_table)
>>> print(volumes)
"""
import os
import numpy as np
import nibabel as nb
from mindboggle.guts.compute import count_per_label
# Load labeled image volumes:
img = nb.load(input_file)
hdr = img.get_header()
volume_per_voxel = np.product(hdr.get_zooms())
labels = img.get_data().ravel()
unique_labels, counts = count_per_label(labels,
include_labels, exclude_labels)
volumes = [volume_per_voxel * x for x in counts]
# Output table:
if save_table:
if output_table:
output_table = os.path.join(os.getcwd(), output_table)
else:
output_table = os.path.join(os.getcwd(),
'volume_for_each_label.csv')
fid = open(output_table, 'w')
if len(label_names) == len(unique_labels):
fid.write("name, ID, volume\n")
else:
fid.write("ID, volume\n")
# Loop through labels:
for ilabel, label in enumerate(unique_labels):
if len(label_names) == len(unique_labels):
fid.write('{0}, {1}, {2:2.3f}\n'.format(
label_names[ilabel], label, volumes[ilabel]))
else:
fid.write('{0}, {1:2.3f}\n'.format(label, volumes[ilabel]))
else:
output_table = ''
return unique_labels, volumes, output_table
