# Import packages
import os
from CPAC.pipeline import nipype_pipeline_engine as pe
import scipy.ndimage as nd
import numpy as np
import nibabel as nb
[docs]def check_if_file_is_empty(in_file):
"""Raise exception if regressor fie is empty.
Parameters
----------
in_file : nii file (string)
regressor file
Returns
-------
in_file : string
return same file
"""
import nibabel as nb
import numpy as np
nii = nb.load(in_file)
data = nii.get_data()
if data.size == 0 or np.all(data == 0) or np.all(data == np.nan):
raise ValueError('File {0} is empty. Use a lower threshold or turn '
'off regressors.'.format(in_file))
return in_file
[docs]def pick_wm_prob_0(probability_maps):
"""Returns the csf probability map from the list of segmented
probability maps
Parameters
----------
probability_maps : list (string)
List of Probability Maps
Returns
-------
file : string
Path to segment_prob_0.nii.gz is returned
""" # noqa
if isinstance(probability_maps, list):
if len(probability_maps) == 1:
probability_maps = probability_maps[0]
for filename in probability_maps:
if filename.endswith("prob_0.nii.gz"):
return filename
return None
[docs]def pick_wm_prob_1(probability_maps):
"""Returns the gray matter probability map from the list of segmented probability maps
Parameters
----------
probability_maps : list (string)
List of Probability Maps
Returns
-------
file : string
Path to segment_prob_1.nii.gz is returned
""" # noqa
if isinstance(probability_maps, list):
if len(probability_maps) == 1:
probability_maps = probability_maps[0]
for filename in probability_maps:
if filename.endswith("prob_1.nii.gz"):
return filename
return None
[docs]def pick_wm_prob_2(probability_maps):
"""Returns the white matter probability map from the list of segmented probability maps
Parameters
----------
probability_maps : list (string)
List of Probability Maps
Returns
-------
file : string
Path to segment_prob_2.nii.gz is returned
""" # noqa
if isinstance(probability_maps, list):
if len(probability_maps) == 1:
probability_maps = probability_maps[0]
for filename in probability_maps:
if filename.endswith("prob_2.nii.gz"):
return filename
return None
[docs]def pick_wm_class_0(tissue_class_files):
"""Returns the csf tissue class file from the list of segmented tissue class files
Parameters
----------
tissue_class_files : list (string)
List of tissue class files
Returns
-------
file : string
Path to segment_seg_0.nii.gz is returned
""" # noqa
if isinstance(tissue_class_files, list):
if len(tissue_class_files) == 1:
tissue_class_files = tissue_class_files[0]
for filename in tissue_class_files:
if filename.endswith("seg_0.nii.gz"):
return filename
return None
[docs]def pick_wm_class_1(tissue_class_files):
"""Returns the gray matter tissue class file from the list of segmented tissue class files
Parameters
----------
tissue_class_files : list (string)
List of tissue class files
Returns
-------
file : string
Path to segment_seg_1.nii.gz is returned
""" # noqa
if isinstance(tissue_class_files, list):
if len(tissue_class_files) == 1:
tissue_class_files = tissue_class_files[0]
for filename in tissue_class_files:
if filename.endswith("seg_1.nii.gz"):
return filename
return None
[docs]def pick_wm_class_2(tissue_class_files):
"""Returns the white matter tissue class file from the list of segmented tissue class files
Parameters
----------
tissue_class_files : list (string)
List of tissue class files
Returns
-------
file : string
Path to segment_seg_2.nii.gz is returned
""" # noqa
if isinstance(tissue_class_files, list):
if len(tissue_class_files) == 1:
tissue_class_files = tissue_class_files[0]
for filename in tissue_class_files:
if filename.endswith("seg_2.nii.gz"):
return filename
return None
[docs]def mask_erosion(roi_mask=None, skullstrip_mask=None, mask_erosion_mm=None,
mask_erosion_prop=None):
"""Returns eroded segment mask and skull-stripped brain mask
# This functionality is adapted from poldracklab/niworkflows:
# https://github.com/poldracklab/niworkflows/blob/master/niworkflows/interfaces/utils.py
# https://fmriprep.readthedocs.io/
# https://poldracklab.stanford.edu/
# We are temporarily maintaining our own copy for more granular control.
Parameters
----------
roi_mask : string
Path to binarized segment mask
skullstrip_mask : string
Path to skull-stripped brain mask
mask_erosion_prop : float
Proportion of erosion skull-stripped brain mask
Returns
-------
output_roi_mask : string
Path to eroded segment mask
eroded_skullstrip_mask : string
Path to eroded skull-stripped brain mask
"""
skullstrip_mask_img = nb.load(skullstrip_mask)
skullstrip_mask_data = skullstrip_mask_img.get_fdata()
roi_mask_img = nb.load(roi_mask)
roi_mask_data = roi_mask_img.get_fdata()
erode_in = (mask_erosion_mm is not None and mask_erosion_mm > 0 or
mask_erosion_prop is not None and mask_erosion_prop < 1 and
mask_erosion_prop > 0)
if erode_in:
if mask_erosion_mm:
iter_n = max(
int(mask_erosion_mm / max(
skullstrip_mask_img.header.get_zooms()
)), 1)
skullstrip_mask_data = nd.binary_erosion(
skullstrip_mask_data, iterations=iter_n)
else:
orig_vol = np.sum(skullstrip_mask_data > 0)
while (
np.sum(skullstrip_mask_data > 0) / (orig_vol*1.0) >
mask_erosion_prop
):
skullstrip_mask_data = nd.binary_erosion(
skullstrip_mask_data, iterations=1)
roi_mask_data[~skullstrip_mask_data] = 0
hdr = roi_mask_img.get_header()
output_roi_mask_img = nb.Nifti1Image(roi_mask_data, header=hdr,
affine=roi_mask_img.get_affine())
output_roi_mask = os.path.join(os.getcwd(),
'segment_tissue_eroded_mask.nii.gz')
output_roi_mask_img.to_filename(output_roi_mask)
hdr = skullstrip_mask_img.get_header()
output_skullstrip_mask_img = nb.Nifti1Image(
skullstrip_mask_data, header=hdr,
affine=skullstrip_mask_img.get_affine())
eroded_skullstrip_mask = os.path.join(os.getcwd(),
'eroded_skullstrip_mask.nii.gz')
output_skullstrip_mask_img.to_filename(eroded_skullstrip_mask)
return output_roi_mask, eroded_skullstrip_mask
# This functionality is adapted from poldracklab/niworkflows:
# https://github.com/poldracklab/niworkflows/blob/master/niworkflows/interfaces/utils.py
# https://fmriprep.readthedocs.io/
# https://poldracklab.stanford.edu/
# We are temporarily maintaining our own copy for more granular control.
[docs]def erosion(roi_mask=None, erosion_mm=None, erosion_prop=None):
"""Returns eroded tissue segment mask
Parameters
----------
roi_mask : string
Path to binarized segment (ROI) mask
erosion_prop : float
Proportion of erosion segment mask
Returns
-------
eroded_roi_mask : string
Path to eroded segment mask
"""
roi_mask_img = nb.load(roi_mask)
roi_mask_data = roi_mask_img.get_fdata()
orig_vol = np.sum(roi_mask_data > 0)
erode_out = (erosion_mm is not None and erosion_mm > 0 or
erosion_prop is not None and erosion_prop < 1 and
erosion_prop > 0)
if erode_out:
if erosion_mm:
iter_n = max(
int(erosion_mm / max(roi_mask_img.header.get_zooms())),
1
)
iter_n = int(erosion_mm / max(roi_mask_img.header.get_zooms()))
roi_mask_data = nd.binary_erosion(roi_mask_data, iterations=iter_n)
else:
while np.sum(roi_mask_data > 0) / (orig_vol*1.0) > erosion_prop:
roi_mask_data = nd.binary_erosion(roi_mask_data, iterations=1)
hdr = roi_mask_img.get_header()
output_img = nb.Nifti1Image(roi_mask_data, header=hdr,
affine=roi_mask_img.get_affine())
eroded_roi_mask = os.path.join(os.getcwd(), 'segment_tissue_mask.nii.gz')
output_img.to_filename(eroded_roi_mask)
return eroded_roi_mask
[docs]def hardcoded_antsJointLabelFusion(anatomical_brain, anatomical_brain_mask,
template_brain_list,
template_segmentation_list):
"""run antsJointLabelFusion.sh
Parameters
----------
anatomical_brain : string (nifti file)
Target image to be labeled.
anatomical_brain_mask : string (nifti file)
Target mask image
template_brain_list : list
Atlas to be warped to target image.
template_segmentation_list : list
Labels corresponding to atlas.
Returns
-------
multiatlas_Intensity : string (nifti file)
multiatlas_Labels : string (nifti file)
"""
import os
import subprocess
cmd = ["${ANTSPATH}${ANTSPATH:+/}antsJointLabelFusion.sh"]
cmd.append(
" -d 3 -o ants_multiatlas_ -t {0} -x {1} -y b -c 0".format(
anatomical_brain, anatomical_brain_mask))
if (not len(template_brain_list) == len(template_segmentation_list)):
err_msg = '\n\n[!] C-PAC says: Please check ANTs Prior-based ' \
'Segmentation setting. For performing ANTs Prior-based ' \
'segmentation method the number of specified ' \
'segmentations should be identical to the number of atlas ' \
'image sets.\n\n'
raise Exception(err_msg)
else:
for index in range(len(template_brain_list)):
cmd.append(
" -g {0} -l {1}".format(
template_brain_list[index],
template_segmentation_list[index]))
# write out the actual command-line entry for testing/validation later
command_file = os.path.join(os.getcwd(), 'command.txt')
with open(command_file, 'wt') as f:
f.write(' '.join(cmd))
str = ""
bash_cmd = str.join(cmd)
try:
retcode = subprocess.check_output(bash_cmd, shell=True) # noqa F841
except Exception as e:
raise Exception('[!] antsJointLabel segmentation method did not '
'complete successfully.\n\nError '
'details:\n{0}\n{1}\n'.format(e, e.output))
multiatlas_Intensity = None
multiatlas_Labels = None
files = [f for f in os.listdir('.') if os.path.isfile(f)]
for f in files:
if "Intensity" in f:
multiatlas_Intensity = os.getcwd() + "/" + f
if "Labels" in f:
multiatlas_Labels = os.getcwd() + "/" + f
if not multiatlas_Labels:
raise Exception("\n\n[!] No multiatlas labels file found. "
"antsJointLabelFusion may not have completed "
"successfully.\n\n")
return multiatlas_Intensity, multiatlas_Labels
[docs]def pick_tissue_from_labels_file(multiatlas_Labels, csf_label=[4,14,15,24,43],
gm_label=[3,42], wm_label=[2,41]):
"""Pick tissue mask from multiatlas labels file
based off of FreeSurferColorLUT https://surfer.nmr.mgh.harvard.edu/fswiki/FsTutorial/AnatomicalROI/FreeSurferColorLUT
or user provided label value
Parameters
----------
multiatlas_Labels : string (nifti file)
csf_label : list
a list of integer label values corresponding to CSF in multiatlas file
gm_label : list
a list of integer label value corresponding to Gray Matter in multiatlas file
wm_label : list
a list of integer label value corresponding to White Matter in multiatlas file
Returns
-------
csf_mask : string (nifti file)
gm_mask : string (nifti file)
wm_mask : string (nifti file)
""" # noqa
import os
import nibabel as nb
import numpy as np
img = nb.load(multiatlas_Labels)
data = img.get_data()
# pick tissue mask from multiatlas labels file
# based off of FreeSurferColorLUT or user provided label values
# hard-coded csf/gm/wm label values are based off of FreeSurferColorLUT
# FreeSurfer Ventricle Labels:
# Left-Lateral-Ventricle 4, 3rd-Ventricle 14, 4th-Ventricle 15, Right-Lateral-Ventricle 43
csf = np.zeros(np.size(data))
csf[np.where(np.in1d(data, np.array(csf_label)))] = 1
csf = csf.reshape(data.shape)
gm = np.zeros(np.size(data))
gm[np.where(np.in1d(data, np.array(gm_label)))] = 1
gm = gm.reshape(data.shape)
wm = np.zeros(np.size(data))
wm[np.where(np.in1d(data, np.array(wm_label)))] = 1
wm = wm.reshape(data.shape)
save_img_csf = nb.Nifti1Image(csf, header=img.header, affine=img.affine)
save_img_gm = nb.Nifti1Image(gm, header=img.header, affine=img.affine)
save_img_wm = nb.Nifti1Image(wm, header=img.header, affine=img.affine)
save_img_csf.to_filename('csf_mask.nii.gz')
save_img_gm.to_filename('gm_mask.nii.gz')
save_img_wm.to_filename('wm_mask.nii.gz')
csf_mask = os.path.join(os.getcwd(), 'csf_mask.nii.gz')
gm_mask = os.path.join(os.getcwd(), 'gm_mask.nii.gz')
wm_mask = os.path.join(os.getcwd(), 'wm_mask.nii.gz')
return csf_mask, gm_mask, wm_mask
