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Post-process Mindboggle-101 volume images for distribution,
using Mindboggle, FreeSurfer, and FSL tools.
This is modified from the original code_postprocess_Mindboggle101.py to
(1) only generate DKT31 (not DKT25) labeling protocol data
(2) use the existing T1 data and transforms (don't generate new ones):
- Convert label volume from FreeSurfer to original space
x Extract brain by masking with manual cortical and automated subcortical labels
- Remove non-DKT31 (non-cortical) labels
- Affine register T1-weighted brain to MNI152 brain
- Transfer whole-head images with affine transform
- Transfer labeled images with affine transform (nearest-neighbor interpolation)
Authors: Arno Klein . arno@mindboggle.info . www.binarybottle.com
(c) 2013-2019 Mindbogglers (www.mindboggle.info), under Apache License Version 2.0
"""
import os
# Paths, template, and label conversion files
mb101_path = os.path.join('/Users', 'arno.klein', 'Data', 'Mindboggle101')
mb_info_path = os.path.join(mb101_path, 'docs')
template = os.path.join(mb101_path, 'MNI152_T1_1mm_brain.nii.gz')
# Loop through subjects
list_file = os.path.join(mb_info_path, 'mindboggle101_list.txt')
fid = open(list_file, 'r')
subjects = fid.readlines()
subjects = [''.join(x.split()) for x in subjects]
def keep_volume_labels(input_file, labels_to_keep, output_file='',
second_file=''):
"""
Keep only given labels in an image volume (or use to mask second volume).
Parameters
----------
input_file : string
labeled nibabel-readable (e.g., nifti) file
labels_to_keep : list of integers
labels to keep
output_file : string
output file name
second_file : string
second nibabel-readable file (keep/erase voxels in this file instead)
Returns
-------
output_file : string
output file name
Examples
--------
>>> # Remove right hemisphere labels
>>> import os
>>> from mindboggle.guts.relabel import keep_volume_labels
>>> from mindboggle.mio.labels import DKTprotocol
>>> from mindboggle.mio.fetch_data import prep_tests
>>> urls, fetch_data = prep_tests()
>>> input_file = fetch_data(urls['freesurfer_labels'], '', '.nii.gz')
>>> second_file = ''
>>> labels_to_keep = list(range(1000, 1036))
>>> output_file = 'keep_volume_labels.nii.gz'
>>> output_file = keep_volume_labels(input_file, labels_to_keep,
... output_file, second_file)
View nifti file (skip test):
>>> from mindboggle.mio.plots import plot_volumes
>>> plot_volumes(output_file) # doctest: +SKIP
"""
import os
import numpy as np
import nibabel as nb
# ------------------------------------------------------------------------
# Load labeled image volume and extract data as 1-D array:
# ------------------------------------------------------------------------
vol = nb.load(input_file)
xfm = vol.get_affine()
data = vol.get_data().ravel()
# ------------------------------------------------------------------------
# If second file specified, erase voxels whose corresponding
# voxels in the input_file have labels not in labels_to_keep:
# ------------------------------------------------------------------------
if second_file:
# Load second image volume and extract data as 1-D array:
vol = nb.load(second_file)
xfm = vol.get_affine()
new_data = vol.get_data().ravel()
if not output_file:
output_file = os.path.join(os.getcwd(),
os.path.basename(second_file))
# ------------------------------------------------------------------------
# If second file not specified, remove labels not in labels_to_keep:
# ------------------------------------------------------------------------
else:
new_data = data.copy()
if not output_file:
output_file = os.path.join(os.getcwd(),
os.path.basename(input_file))
# ------------------------------------------------------------------------
# Erase voxels as specified above:
# ------------------------------------------------------------------------
ulabels = np.unique(data)
for label in ulabels:
label = int(label)
if label not in labels_to_keep:
new_data[np.where(data == label)[0]] = 0
# ------------------------------------------------------------------------
# Reshape to original dimensions:
# ------------------------------------------------------------------------
new_data = np.reshape(new_data, vol.shape)
# ------------------------------------------------------------------------
# Save relabeled file:
# ------------------------------------------------------------------------
img = nb.Nifti1Image(new_data, xfm)
img.to_filename(output_file)
if not os.path.exists(output_file):
raise IOError("keep_volume_labels() did not create " + output_file + ".")
return output_file
for subject in subjects:
print(">>> Process subject: {0}...".format(subject))
subject_path = os.path.join(mb101_path, 'subjects', subject, 'mri')
# Identify original files
full_labels_orig = os.path.join(subject_path, 'aparcNMMjt+aseg.nii.gz')
head = os.path.join(subject_path, 't1weighted.nii.gz')
brain = os.path.join(subject_path, 't1weighted_brain.nii.gz')
# Name all output files
full_labels = os.path.join(subject_path, 'labels.DKT31.manual+aseg.nii.gz')
DKT31_labels = os.path.join(subject_path, 'labels.DKT31.manual.nii.gz')
xfm_matrix = os.path.join(subject_path, 't1weighted_brain.MNI152.affine.txt')
xfm_brain = os.path.join(subject_path, 't1weighted_brain.MNI152.nii.gz')
xfm_head = os.path.join(subject_path, 't1weighted.MNI152.nii.gz')
xfm_DKT31 = os.path.join(subject_path, 'labels.DKT31.manual.MNI152.nii.gz')
xfm_DKT31aseg = os.path.join(subject_path, 'labels.DKT31.manual+aseg.MNI152.nii.gz')
# Remove old labels and affine-transformed files
rm_files = [x for x in os.listdir(subject_path) if 'labels.' in x or '.MNI152.' in x]
for rm_file in rm_files:
os.remove(os.path.join(subject_path, rm_file))
# Convert label volume from FreeSurfer to original space
print("Convert label volume from FreeSurfer to original space...")
cmd = ' '.join(['mri_vol2vol --nearest --mov', full_labels_orig, '--targ', head,
'--regheader --o', full_labels])
print(cmd); os.system(cmd)
# Affine register T1-weighted brain to MNI152 brain using FSL's flirt
print("Affine register T1-weighted brain to MNI152 brain using FSL's flirt...")
cmd = ' '.join(['flirt', '-in', brain, '-ref', template,
'-out', xfm_brain, '-omat', xfm_matrix])
print(cmd); os.system(cmd)
# Transfer whole-head images with affine transform using FSL's flirt
print("Apply affine transform to whole-head using FSL's flirt...")
cmd = ' '.join(['flirt', '-in', head, '-ref', template,
'-applyxfm -init', xfm_matrix, '-out', xfm_head])
print(cmd); os.system(cmd)
# Transfer DKT31- plus FreeSurfer-aseg-labeled images with affine transform (nearest-neighbor interpolation)
print("Apply affine transform to labeled images (with nearest neighbor interpolation)...")
cmd = ' '.join(['flirt', '-in', full_labels, '-ref', template,
'-applyxfm -init', xfm_matrix,
'-interp nearestneighbour -out', xfm_DKT31aseg])
print(cmd); os.system(cmd)
# Remove all but DKT31 (cortical) labels
print("Remove non-DKT31 (cortical) labels...")
DKT31_numbers = [2, 3] + list(range(5, 32)) + [34, 35]
labels_to_keep = [1000 + x for x in DKT31_numbers]
labels_to_keep.extend([2000 + x for x in DKT31_numbers])
output_file = keep_volume_labels(full_labels, labels_to_keep, output_file=DKT31_labels, second_file='')
# Transfer DKT31-labeled images with affine transform (nearest-neighbor interpolation)
cmd = ' '.join(['flirt', '-in', DKT31_labels, '-ref', template,
'-applyxfm -init', xfm_matrix,
'-interp nearestneighbour -out', xfm_DKT31])
print(cmd); os.system(cmd)
# Compress subject directory
#subject_path2 = os.path.join(mb101_path, 'subjects', subject)
#cmd = ' '.join(['tar cvfz', subject_path2+'.tar.gz', subject_path2])
#print(cmd); os.system(cmd)
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