def flip_streamlines(tract_filename, ref_anat, out_filename, flip_x, flip_y,
flip_z, flip_mode):
# Detect the format of the tracts file.
tracts_format = tc.detect_format(tract_filename)
tracts_file = tracts_format(tract_filename, anatFile=ref_anat)
tracts = np.array([s for s in tracts_file])
flip_vector = get_axis_flip_vector(flip_x, flip_y, flip_z)
shift_vector = get_shift_vector(flip_mode, ref_anat, tracts)
flipped_tracts = []
for tract in tracts:
mod_tract = tract + shift_vector
mod_tract *= flip_vector
mod_tract -= shift_vector
flipped_tracts.append(mod_tract)
out_hdr = tracts_file.hdr
out_format = tc.detect_format(out_filename)
out_tracts = out_format.create(out_filename, out_hdr, anatFile=ref_anat)
out_tracts += flipped_tracts
out_tracts.close()
def main():
parser = buildArgsParser()
args = parser.parse_args()
in_filenames = args.input
out_filename = args.output
#anat_filename = args.anat
isForcing = args.isForce
isVerbose = args.isVerbose
if isVerbose:
logging.basicConfig(level=logging.DEBUG)
for in_filename in in_filenames:
if not os.path.isfile(in_filename):
parser.error('"{0}" must be an existing file!'.format(in_filename))
if not tractconverter.is_supported(in_filename):
parser.error('Input file must be one of {0}!'.format(",".join(
FORMATS.keys())))
if not tractconverter.is_supported(out_filename):
parser.error('Output file must be one of {0}!'.format(",".join(
FORMATS.keys())))
if os.path.isfile(out_filename):
if isForcing:
logging.info('Overwriting "{0}".'.format(out_filename))
else:
parser.error('"{0}" already exist! Use -f to overwrite it.'.format(
out_filename))
inFormats = [
tractconverter.detect_format(in_filename)
for in_filename in in_filenames
]
outFormat = tractconverter.detect_format(out_filename)
# if anat_filename is not None:
# if not any(map(anat_filename.endswith, EXT_ANAT.split('|'))):
# if isForcing:
# logging.info('Reading "{0}" as a {1} file.'.format(anat_filename.split("/")[-1], EXT_ANAT))
# else:
# parser.error('Anatomy file must be one of {1}!'.format(EXT_ANAT))
# if not os.path.isfile(anat_filename):
# parser.error('"{0}" must be an existing file!'.format(anat_filename))
#TODO: Consider different anat, space.
hdr = {}
hdr[Header.DIMENSIONS] = (1, 1, 1)
hdr[Header.ORIGIN] = (1, 1, 1)
hdr[Header.
NB_FIBERS] = 0 # The actual number of streamlines will be added later.
#Merge inputs to output
inputs = (in_format(in_filename)
for in_filename, in_format in zip(in_filenames, inFormats))
output = outFormat.create(out_filename, hdr)
tractconverter.merge(inputs, output)
def main():
parser = buildArgsParser()
args = parser.parse_args()
in_filename = args.input
out_filename = args.output
anat_filename = args.anat
isForcing = args.isForce
isVerbose = args.isVerbose
if isVerbose:
logging.basicConfig(level=logging.DEBUG)
if not os.path.isfile(in_filename):
parser.error('"{0}" must be an existing file!'.format(in_filename))
if not tractconverter.is_supported(in_filename):
parser.error('Input file must be one of {0}!'.format(",".join(
FORMATS.keys())))
if not tractconverter.is_supported(out_filename):
parser.error('Output file must be one of {0}!'.format(",".join(
FORMATS.keys())))
if os.path.isfile(out_filename):
if isForcing:
if out_filename == in_filename:
parser.error(
'Cannot use the same name for input and output files. Conversion would fail.'
)
else:
logging.info('Overwriting "{0}".'.format(out_filename))
else:
parser.error('"{0}" already exist! Use -f to overwrite it.'.format(
out_filename))
inFormat = tractconverter.detect_format(in_filename)
outFormat = tractconverter.detect_format(out_filename)
#if inFormat == outFormat:
# parser.error('Input and output must be from different types!'.format(",".join(FORMATS.keys())))
if anat_filename is not None:
if not any(map(anat_filename.endswith, EXT_ANAT.split('|'))):
if isForcing:
logging.info('Reading "{0}" as a {1} file.'.format(
anat_filename.split("/")[-1], EXT_ANAT))
else:
parser.error(
'Anatomy file must be one of {1}!'.format(EXT_ANAT))
if not os.path.isfile(anat_filename):
parser.error(
'"{0}" must be an existing file!'.format(anat_filename))
#Convert input to output
input = inFormat(in_filename, anat_filename)
output = outFormat.create(out_filename, input.hdr, anat_filename)
tractconverter.convert(input, output)
def main():
parser = buildArgsParser()
args = parser.parse_args()
#####################################
# Applying options #
#####################################
if args.planes_for_stats:
planes_for_stats = eval(args.planes_for_stats)
else:
planes_for_stats = None
#####################################
# Checking if the files exist #
#####################################
for myFile in [
args.tracts_filename, args.centroid_filename, args.ref_anat_name
]:
if not os.path.isfile(myFile):
parser.error('"{0}" must be a file!'.format(myFile))
if args.output_name:
if os.path.exists(args.output_name):
print(args.output_name, " already exist and will be overwritten.")
#####################################
# Loading tracts #
#####################################
tract_format = tc.detect_format(args.tracts_filename)
tract = tract_format(args.tracts_filename, anatFile=args.ref_anat_name)
streamlines = [i for i in tract]
centroid_format = tc.detect_format(args.centroid_filename)
tmp = centroid_format(args.centroid_filename, anatFile=args.ref_anat_name)
centroid = [i for i in tmp] # should contain only one
if len(centroid) > 1:
print(
'Centroid should contain only one streamline. Here, the file contains more.'
)
print('The first streamline will be used as the centroid.')
centroid = centroid[0]
#####################################
# Loading anat #
# Preparing mask #
#####################################
anat = nib.load(args.ref_anat_name)
affine = anat.get_affine()
shape = anat.get_shape()
if args.mask:
anat = anat.get_data()
else:
anat = np.ones(shape)
hitpoints = compute_cross_sections(streamlines, centroid)
compute_stats(hitpoints, anat, affine, args.output_name, planes_for_stats)
def main():
parser = buildArgsParser()
args = parser.parse_args()
in_filenames = args.input
out_filename = args.output
#anat_filename = args.anat
isForcing = args.isForce
isVerbose = args.isVerbose
if isVerbose:
logging.basicConfig(level=logging.DEBUG)
for in_filename in in_filenames:
if not os.path.isfile(in_filename):
parser.error('"{0}" must be an existing file!'.format(in_filename))
if not tractconverter.is_supported(in_filename):
parser.error('Input file must be one of {0}!'.format(",".join(FORMATS.keys())))
if not tractconverter.is_supported(out_filename):
parser.error('Output file must be one of {0}!'.format(",".join(FORMATS.keys())))
if os.path.isfile(out_filename):
if isForcing:
if any(in_name == out_filename for in_name in in_filenames):
parser.error('Cannot output to a file which is also an input file ({0}).'.format(out_filename))
else:
logging.info('Overwriting "{0}".'.format(out_filename))
else:
parser.error('"{0}" already exist! Use -f to overwrite it.'.format(out_filename))
inFormats = [tractconverter.detect_format(in_filename) for in_filename in in_filenames]
outFormat = tractconverter.detect_format(out_filename)
# if anat_filename is not None:
# if not any(map(anat_filename.endswith, EXT_ANAT.split('|'))):
# if isForcing:
# logging.info('Reading "{0}" as a {1} file.'.format(anat_filename.split("/")[-1], EXT_ANAT))
# else:
# parser.error('Anatomy file must be one of {1}!'.format(EXT_ANAT))
# if not os.path.isfile(anat_filename):
# parser.error('"{0}" must be an existing file!'.format(anat_filename))
#TODO: Consider different anat, space.
hdr = {}
hdr[Header.DIMENSIONS] = (1,1,1)
hdr[Header.ORIGIN] = (1,1,1)
hdr[Header.NB_FIBERS] = 0 # The actual number of streamlines will be added later.
#Merge inputs to output
inputs = (in_format(in_filename) for in_filename, in_format in zip(in_filenames, inFormats))
output = outFormat.create(out_filename, hdr)
tractconverter.merge(inputs, output)
def compression_wrapper(tract_filename, out_filename, error_rate):
tracts_format = tc.detect_format(tract_filename)
tracts_file = tracts_format(tract_filename)
out_hdr = tracts_file.hdr
out_format = tc.detect_format(out_filename)
out_tracts = out_format.create(out_filename, out_hdr)
for s in tracts_file:
# TODO we should chunk this.
out_tracts += np.array(compress_streamlines(list([s]), error_rate))
out_tracts.close()
def main():
parser = buildArgsParser()
args = parser.parse_args()
in_filename = args.input
out_filename = args.output
anat_filename = args.anat
isForcing = args.isForce
isVerbose = args.isVerbose
if isVerbose:
logging.basicConfig(level=logging.DEBUG)
if not os.path.isfile(in_filename):
parser.error('"{0}" must be an existing file!'.format(in_filename))
if not tractconverter.is_supported(in_filename):
parser.error("Input file must be one of {0}!".format(",".join(FORMATS.keys())))
if not tractconverter.is_supported(out_filename):
parser.error("Output file must be one of {0}!".format(",".join(FORMATS.keys())))
if os.path.isfile(out_filename):
if isForcing:
if out_filename == in_filename:
parser.error("Cannot use the same name for input and output files. Conversion would fail.")
else:
logging.info('Overwriting "{0}".'.format(out_filename))
else:
parser.error('"{0}" already exist! Use -f to overwrite it.'.format(out_filename))
inFormat = tractconverter.detect_format(in_filename)
outFormat = tractconverter.detect_format(out_filename)
# if inFormat == outFormat:
# parser.error('Input and output must be from different types!'.format(",".join(FORMATS.keys())))
if anat_filename is not None:
if not any(map(anat_filename.endswith, EXT_ANAT.split("|"))):
if isForcing:
logging.info('Reading "{0}" as a {1} file.'.format(anat_filename.split("/")[-1], EXT_ANAT))
else:
parser.error("Anatomy file must be one of {1}!".format(EXT_ANAT))
if not os.path.isfile(anat_filename):
parser.error('"{0}" must be an existing file!'.format(anat_filename))
# Convert input to output
input = inFormat(in_filename, anat_filename)
output = outFormat.create(out_filename, input.hdr, anat_filename)
tractconverter.convert(input, output)
def main():
parser = buildArgsParser()
args = parser.parse_args()
np.random.seed(int(time.time()))
in_filename = args.input
out_filename = args.output
isForcing = args.isForce
isVerbose = args.isVerbose
if isVerbose:
logging.basicConfig(level=logging.DEBUG)
if not tractconverter.is_supported(args.input):
parser.error('Input file must be one of {0}!'.format(",".join(
tractconverter.FORMATS.keys())))
inFormat = tractconverter.detect_format(in_filename)
outFormat = tractconverter.detect_format(out_filename)
if not inFormat == outFormat:
parser.error('Input and output must be of the same types!'.format(
",".join(tractconverter.FORMATS.keys())))
if os.path.isfile(args.output):
if args.isForce:
logging.info('Overwriting "{0}".'.format(out_filename))
else:
parser.error('"{0}" already exist! Use -f to overwrite it.'.format(
out_filename))
tract = inFormat(in_filename)
streamlines = [i for i in tract]
if args.non_fixed_seed:
rng = np.random.RandomState()
else:
rng = None
results = subsample_streamlines(streamlines, args.minL, args.maxL, args.n,
args.npts, args.arclength, rng)
logging.info('"{0}" contains {1} streamlines.'.format(
out_filename, len(results)))
hdr = tract.hdr
hdr[tractconverter.formats.header.Header.NB_FIBERS] = len(results)
output = outFormat.create(out_filename, hdr)
output += results
def save_streamlines_tractquerier(streamlines, ref_filename, output_filename):
"""
Parameters
----------
streamlines: list
List of tuples (3D points, scalars, properties).
ref_filename: str
File name of a reference image.
output_filename: str
File name to save the streamlines to use in the tract querier.
Return
------
"""
tracts_format = tc.detect_format(output_filename)
if tracts_format not in [tc.formats.trk.TRK, tc.formats.tck.TCK]:
raise ValueError("Invalid output streamline file format " +
"(must be trk or tck): {0}".format(output_filename))
hdr = tc.formats.header.get_header_from_anat(ref_filename)
# This currently creates an invalid .tck file, since they are partly transformed.
# At least, not having the anatFile param applies an identity transform to the
# streamlines when saved.
# Will be fixed with Guillaume Theaud's modifications and
# @marccote nibabel branch.
out_tracts = tracts_format.create(output_filename, hdr)
origin = hdr[tc.formats.header.Header.VOXEL_TO_WORLD][:3, 3]
tracts = [s[0] + origin for s in streamlines]
out_tracts += tracts
out_tracts.close()
def validate_coordinates(anat, streamlines, nifti_compliant=True):
# Check if all points in the tracts are inside the image volume.
ref_img = nb.load(anat)
voxel_dim = ref_img.get_header()['pixdim'][1:4]
if nifti_compliant:
shift_factor = voxel_dim * 0.5
else:
shift_factor = voxel_dim * 0.0
tract_file = streamlines
if isinstance(streamlines, six.string_types):
tc_format = tc.detect_format(streamlines)
tract_file = tc_format(streamlines, anatFile=anat)
# TODO what check to do for .vtk?
if isinstance(tract_file, tc.formats.tck.TCK) \
or isinstance(tract_file, tc.formats.trk.TRK):
for s in tract_file:
strl = np.array(s + shift_factor)
if np.any(strl < 0):
return False
else:
raise TypeError("This function currently only supports TCK and TRK.")
return True
def save_streamlines_fibernavigator(streamlines, ref_filename,
output_filename):
"""
Parameters
----------
streamlines: list
List of tuples (3D points, scalars, properties).
ref_filename: str
File name of a reference image.
output_filename: str
File name to save the streamlines to use in the FiberNavigator.
Return
------
"""
tracts_format = tc.detect_format(output_filename)
if tracts_format not in [tc.formats.trk.TRK, tc.formats.tck.TCK]:
raise ValueError("Invalid output streamline file format " +
"(must be trk or tck): {0}".format(output_filename))
hdr = tc.formats.header.get_header_from_anat(ref_filename)
# anatFile is only important for .tck for now, but has no negative
# impact on other formats.
out_tracts = tracts_format.create(output_filename,
hdr,
anatFile=ref_filename)
tracts = [s[0] for s in streamlines]
out_tracts += tracts
out_tracts.close()
def _lps_allowed(tracts_filename):
tracts_format = tc.detect_format(tracts_filename)
tracts_file = tracts_format(tracts_filename)
if isinstance(tracts_file, tc.formats.vtk.VTK):
return True
return False
def is_trk(streamlines_filename):
tracts_format = tc.detect_format(streamlines_filename)
tracts_file = tracts_format(streamlines_filename)
if isinstance(tracts_file, tc.formats.trk.TRK):
return True
return False
def main():
parser = build_args_parser()
args = parser.parse_args()
if args.verbose:
logging.basicConfig(level=logging.INFO)
logging.warning('The script scil_substract_streamlines is deprecated. '
'Use scil_streamlines_math instead.')
if os.path.isfile(args.output):
if args.force:
logging.info('Overwriting {0}.'.format(args.output))
else:
parser.error(
'{0} already exist! Use -f to overwrite it.'
.format(args.output))
# The first filename contains the streamlines from which all others are
# substracted.
logging.info(
'Loading streamlines from file {0} ...'.format(args.input))
tract_format = tractconverter.detect_format(args.input)
streamlines = list(tract_format(args.input))
# All the other filenames contain the streamlines to be removed.
streamlines_to_remove = []
for filename in args.remove:
logging.info(
'Loading streamlines from file {0} ...'.format(filename))
tract_format = tractconverter.detect_format(filename)
streamlines_to_remove.append(tract_format(filename))
# Remove the streamlines in place.
substract_streamlines(
streamlines,
itertools.chain(*streamlines_to_remove))
# Save the new streamlines.
logging.info('Saving remaining streamlines ...')
tract_format = tractconverter.detect_format(args.input)
input_tract = tract_format(args.input)
hdr = input_tract.hdr
hdr[tractconverter.formats.header.Header.NB_FIBERS] = len(streamlines)
output = tract_format.create(args.output, hdr)
output += streamlines
def format_needs_orientation(tract_fname):
tracts_format = tc.detect_format(tract_fname)
tracts_file = tracts_format(tract_fname)
if isinstance(tracts_file, tc.formats.vtk.VTK):
return True
return False
def guess_orientation(tract_fname):
tracts_format = tc.detect_format(tract_fname)
tracts_file = tracts_format(tract_fname)
if isinstance(tracts_file, tc.formats.tck.TCK):
return 'RAS'
return 'Unknown'
def format_needs_orientation(tract_fname):
tracts_format = tc.detect_format(tract_fname)
tracts_file = tracts_format(tract_fname)
if isinstance(tracts_file, tc.formats.vtk.VTK):
return True
return False
def guess_orientation(tract_fname):
tracts_format = tc.detect_format(tract_fname)
tracts_file = tracts_format(tract_fname)
if isinstance(tracts_file, tc.formats.tck.TCK):
return 'RAS'
return 'Unknown'
def _is_tracts_space_valid(tracts_filename, lps_oriented):
tracts_format = tc.detect_format(tracts_filename)
tracts_file = tracts_format(tracts_filename)
# Compute boundaries of volume
if lps_oriented:
required_mins = np.array([-1.0, -1.0, -1.0])
required_maxs = np.array([179.0, 215.0, 179.0])
else:
required_mins = np.array([-179.0, -215.0, -1.0])
required_maxs = np.array([1.0, 1.0, 179.0])
# We compute them directly in the loop inside the format dependent code
# to avoid 2 loops and to avoid loading everything in memory.
minimas = []
maximas = []
# Load tracts
if isinstance(tracts_file, tc.formats.vtk.VTK) \
or isinstance(tracts_file, tc.formats.tck.TCK):
for s in tracts_file:
minimas.append(np.min(s, axis=0))
maximas.append(np.max(s, axis=0))
elif isinstance(tracts_file, tc.formats.trk.TRK):
# Use nb.trackvis to read directly in correct space
try:
streamlines, _ = nb.trackvis.read(tracts_filename,
as_generator=True,
points_space='rasmm')
except nb.trackvis.HeaderError as er:
msg = "\n------ ERROR ------\n\n" +\
"TrackVis header is malformed or incomplete.\n" +\
"Please make sure all fields are correctly set.\n\n" +\
"The error message reported by Nibabel was:\n" +\
str(er)
return msg
for s in streamlines:
minimas.append(np.min(s[0], axis=0))
maximas.append(np.max(s[0], axis=0))
global_min = np.min(minimas, axis=0)
global_max = np.max(maximas, axis=0)
if np.all(global_min > required_mins) and \
np.all(global_max < required_maxs):
return "Tracts seem to be in the correct space"
elif isinstance(tracts_file, tc.formats.vtk.VTK) and\
np.all(global_min * np.array([-1.0, -1.0, 1.0]) > required_mins) \
and np.all(global_max * np.array([-1.0, -1.0, 1.0]) < required_maxs):
return "Tracts seem to be reverted. Did you use the --lps flag?\n" +\
"If so, it means the tracts are not in the correct space."
return "Tracts do not seem to be in the correct space.\n\n" + \
_print_required_and_found(required_mins, required_maxs,
global_min, global_max, lps_oriented)
def _is_format_supported(tracts_filename):
tracts_format = tc.detect_format(tracts_filename)
tracts_file = tracts_format(tracts_filename)
if isinstance(tracts_file, tc.formats.tck.TCK) \
or isinstance(tracts_file, tc.formats.trk.TRK) \
or isinstance(tracts_file, tc.formats.vtk.VTK):
return True
return False
def label_streamlines(streamlines, labels, labels_Value, affine, hdr, f_name,
data_path):
cc_slice = labels == labels_Value
cc_streamlines = utils.target(streamlines, labels, affine=affine)
cc_streamlines = list(cc_streamlines)
other_streamlines = utils.target(streamlines,
cc_slice,
affine=affine,
include=False)
other_streamlines = list(other_streamlines)
assert len(other_streamlines) + len(cc_streamlines) == len(streamlines)
print("num of roi steamlines is %d", len(cc_streamlines))
# Make display objects
color = line_colors(cc_streamlines)
cc_streamlines_actor = fvtk.line(cc_streamlines,
line_colors(cc_streamlines))
cc_ROI_actor = fvtk.contour(cc_slice,
levels=[1],
colors=[(1., 1., 0.)],
opacities=[1.])
# Add display objects to canvas
r = fvtk.ren()
fvtk.add(r, cc_streamlines_actor)
fvtk.add(r, cc_ROI_actor)
# Save figures
fvtk.record(r, n_frames=1, out_path=f_name + '_roi.png', size=(800, 800))
fvtk.camera(r, [-1, 0, 0], [0, 0, 0], viewup=[0, 0, 1])
fvtk.record(r, n_frames=1, out_path=f_name + '_roi.png', size=(800, 800))
""""""
csd_streamlines_trk = ((sl, None, None) for sl in cc_streamlines)
csd_sl_fname = f_name + '_roi_streamline.trk'
nib.trackvis.write(csd_sl_fname,
csd_streamlines_trk,
hdr,
points_space='voxel')
#nib.save(nib.Nifti1Image(FA, img.get_affine()), 'FA_map2.nii.gz')
print('Saving "_roi_streamline.trk" sucessful.')
import tractconverter as tc
input_format = tc.detect_format(csd_sl_fname)
input = input_format(csd_sl_fname)
output = tc.FORMATS['vtk'].create(csd_sl_fname + ".vtk", input.hdr)
tc.convert(input, output)
return cc_streamlines
def get_tract_count(streamlines):
if isinstance(streamlines, six.string_types):
tc_format = tc.detect_format(streamlines)
tract_file = tc_format(streamlines)
tract_count = tract_file.hdr[tract_header.NB_FIBERS]
elif isinstance(streamlines, list):
tract_count = len(streamlines)
# Need to do it like this since the is no parent class in the formats.
elif isinstance(streamlines, tc.formats.tck.TCK) \
or isinstance(streamlines, tc.formats.trk.TRK) \
or isinstance(streamlines, tc.formats.vtk.VTK):
tract_count = streamlines.hdr[tract_header.NB_FIBERS]
return tract_count
def main():
parser = buildArgsParser()
args = parser.parse_args()
in_filename = args.input
if not os.path.isfile(in_filename):
parser.error('"{0}" must be an existing file!'.format(in_filename))
if not tractconverter.is_supported(in_filename):
parser.error('Input file must be one of {0}!'.format(",".join(FORMATS.keys())))
inFormat = tractconverter.detect_format(in_filename)
#Print info about the input file.
print inFormat(in_filename, None)
def label_streamlines(streamlines,labels,labels_Value,affine,hdr,f_name,data_path):
cc_slice=labels==labels_Value
cc_streamlines = utils.target(streamlines, labels, affine=affine)
cc_streamlines = list(cc_streamlines)
other_streamlines = utils.target(streamlines, cc_slice, affine=affine,
include=False)
other_streamlines = list(other_streamlines)
assert len(other_streamlines) + len(cc_streamlines) == len(streamlines)
print ("num of roi steamlines is %d",len(cc_streamlines))
# Make display objects
color = line_colors(cc_streamlines)
cc_streamlines_actor = fvtk.line(cc_streamlines, line_colors(cc_streamlines))
cc_ROI_actor = fvtk.contour(cc_slice, levels=[1], colors=[(1., 1., 0.)],
opacities=[1.])
# Add display objects to canvas
r = fvtk.ren()
fvtk.add(r, cc_streamlines_actor)
fvtk.add(r, cc_ROI_actor)
# Save figures
fvtk.record(r, n_frames=1, out_path=f_name+'_roi.png',
size=(800, 800))
fvtk.camera(r, [-1, 0, 0], [0, 0, 0], viewup=[0, 0, 1])
fvtk.record(r, n_frames=1, out_path=f_name+'_roi.png',
size=(800, 800))
""""""
csd_streamlines_trk = ((sl, None, None) for sl in cc_streamlines)
csd_sl_fname = f_name+'_roi_streamline.trk'
nib.trackvis.write(csd_sl_fname, csd_streamlines_trk, hdr, points_space='voxel')
#nib.save(nib.Nifti1Image(FA, img.get_affine()), 'FA_map2.nii.gz')
print('Saving "_roi_streamline.trk" sucessful.')
import tractconverter as tc
input_format=tc.detect_format(csd_sl_fname)
input=input_format(csd_sl_fname)
output=tc.FORMATS['vtk'].create(csd_sl_fname+".vtk",input.hdr)
tc.convert(input,output)
return cc_streamlines
def main():
parser = buildArgsParser()
args = parser.parse_args()
in_filename = args.input
if not os.path.isfile(in_filename):
parser.error('"{0}" must be an existing file!'.format(in_filename))
if not tractconverter.is_supported(in_filename):
parser.error('Input file must be one of {0}!'.format(",".join(
FORMATS.keys())))
inFormat = tractconverter.detect_format(in_filename)
#Print info about the input file.
print inFormat(in_filename, None)
def compute_affine_for_dipy_functions(anat, streamlines):
# Determine if we need to send an identity affine or the real
# affine. This depends of the space in which streamlines are given by
# the TractConverter. If we are loading a TCK or TRK file, the streamlines
# will be aligned with a grid starting at the origin of the reference frame
# in millimetric space. In that case, send a "scale" identity to density_map
# to avoid any further transform.
ref_img = nib.load(anat)
voxel_dim = ref_img.get_header()['pixdim'][1:4]
affine_for_dipy = ref_img.get_affine()
tract_file = streamlines
if isinstance(streamlines, six.string_types):
tc_format = tc.detect_format(streamlines)
tract_file = tc_format(streamlines, anatFile=anat)
if isinstance(tract_file, tc.formats.tck.TCK) \
or isinstance(tract_file, tc.formats.trk.TRK):
affine_for_dipy = np.eye(4)
affine_for_dipy[:3, :3] *= np.asarray(voxel_dim)
return affine_for_dipy
def compute_max_values(tracts_filename):
tracts_format = tc.detect_format(tracts_filename)
tracts_file = tracts_format(tracts_filename)
# We compute them directly in the loop inside the format dependent code
# to avoid 2 loops and to avoid loading everything in memory.
minimas = []
maximas = []
# Load tracts
if isinstance(tracts_file, tc.formats.vtk.VTK) \
or isinstance(tracts_file, tc.formats.tck.TCK):
for s in tracts_file:
minimas.append(np.min(s, axis=0))
maximas.append(np.max(s, axis=0))
elif isinstance(tracts_file, tc.formats.trk.TRK):
# Use nb.trackvis to read directly in correct space
try:
streamlines, _ = nb.trackvis.read(tracts_filename,
as_generator=True)
except nb.trackvis.HeaderError as er:
msg = "\n------ ERROR ------\n\n" +\
"TrackVis header is malformed or incomplete.\n" +\
"Please make sure all fields are correctly set.\n\n" +\
"The error message reported by Nibabel was:\n" +\
str(er)
return msg
for s in streamlines:
minimas.append(np.min(s[0], axis=0))
maximas.append(np.max(s[0], axis=0))
global_min = np.min(minimas, axis=0)
global_max = np.max(maximas, axis=0)
print("Min: {0}".format(global_min))
print("Max: {0}".format(global_max))
def count(tract_filename, roi_anat, roi_idx_range):
roi_img = nib.load(roi_anat)
voxel_dim = roi_img.get_header()['pixdim'][1:4]
anat_dim = roi_img.get_header().get_data_shape()
# Detect the format of the tracts file.
# IF TRK, load and shift
# ELSE, load
tracts_format = tc.detect_format(tract_filename)
tracts_file = tracts_format(tract_filename, anatFile=roi_anat)
if tracts_format is tc.FORMATS["trk"]:
tracts = np.array([s - voxel_dim / 2. for s in tracts_file.load_all()])
else:
tracts = np.array([s for s in tracts_file])
_, tes = track_counts(tracts, anat_dim, voxel_dim, True)
# If the data is a 4D volume with only one element in 4th dimension,
# this will make it 3D, to correctly work with the tes variable.
roi_data = roi_img.get_data().squeeze()
if len(roi_data.shape) > 3:
raise ValueError('Tract counting will fail with an anatomy of ' +
'more than 3 dimensions.')
roi_counts = []
for roi_idx in roi_idx_range:
roi_vox_idx = izip(*np.where(roi_data == roi_idx))
tractIdx_per_voxel = [set(tes.get(idx, [])) for idx in roi_vox_idx]
if len(tractIdx_per_voxel) > 0:
unique_streamline_idx = set.union(*tractIdx_per_voxel)
roi_counts.append((roi_idx, len(unique_streamline_idx)))
return roi_counts
def main():
parser = buildArgsParser()
args = parser.parse_args()
in_filename = args.input
out_filename_loops = args.output_loops
out_filename_clean = args.output_clean
streamlines_c = []
loops = []
if not tc.is_supported(args.input):
parser.error('Input file must be one of {0}!'.format(",".join(
tc.FORMATS.keys())))
in_format = tc.detect_format(in_filename)
out_format_clean = tc.detect_format(out_filename_clean)
if not in_format == out_format_clean:
parser.error('Input and output must be of the same types!'.format(
",".join(tc.FORMATS.keys())))
if args.output_loops:
out_format_loops = tc.detect_format(out_filename_loops)
if not in_format == out_format_loops:
parser.error(
'Input and output loops must be of the same types!'.format(
",".join(tc.FORMATS.keys())))
if os.path.isfile(args.output_loops):
if args.isForce:
logging.info('Overwriting "{0}".'.format(out_filename_loops))
else:
parser.error(
'"{0}" already exist! Use -f to overwrite it.'.format(
out_filename_loops))
if os.path.isfile(args.output_clean):
if args.isForce:
logging.info('Overwriting "{0}".'.format(out_filename_clean))
else:
parser.error('"{0}" already exist! Use -f to overwrite it.'.format(
out_filename_clean))
if args.threshold <= 0:
parser.error('"{0}"'.format(args.threshold) + 'must be greater than 0')
if args.angle <= 0:
parser.error('"{0}"'.format(args.angle) + 'must be greater than 0')
tract = in_format(in_filename)
streamlines = [i for i in tract]
if len(streamlines) > 1:
streamlines_c, loops = remove_loops_and_sharp_turns(
streamlines, args.QB, args.angle, args.threshold)
else:
parser.error('Zero or one streamline in ' + '{0}'.format(in_filename) +
'. The file must have more than one streamline.')
hdr = tract.hdr.copy()
nb_points_init = hdr[tc.formats.header.Header.NB_POINTS]
nb_points_clean = 0
if len(streamlines_c) > 0:
hdr[tc.formats.header.Header.NB_FIBERS] = len(streamlines_c)
if in_format is tc.formats.vtk.VTK:
for s in streamlines_c:
nb_points_clean += len(s)
hdr[tc.formats.header.Header.NB_POINTS] = nb_points_clean
output_clean = out_format_clean.create(out_filename_clean, hdr)
output_clean += streamlines_c
output_clean.close()
else:
logging.warning("No clean streamlines in {0}".format(args.input))
if len(loops) == 0:
logging.warning("No loops in {0}".format(args.input))
if args.output_loops and len(loops) > 0:
hdr[tc.formats.header.Header.NB_FIBERS] = len(loops)
if in_format is tc.formats.vtk.VTK:
hdr[tc.formats.header.Header.
NB_POINTS] = nb_points_init - nb_points_clean
output_loops = out_format_loops.create(out_filename_loops, hdr)
output_loops += loops
output_loops.close()
def _get_tracts_over_grid(tract_fname, ref_anat_fname, tract_attributes,
start_at_corner=True):
# TODO move to only get the attribute
# Tract_attributes is a dictionary containing various information
# about a dataset. Currently using:
# - "orientation" (should be LPS or RAS)
tracts_format = tc.detect_format(tract_fname)
tracts_file = tracts_format(tract_fname)
# Get information on the supporting anatomy
ref_img = nb.load(ref_anat_fname)
index_to_world_affine = ref_img.get_header().get_best_affine()
if isinstance(tracts_file, tc.formats.vtk.VTK):
# For VTK files, we need to check the orientation.
# Considered to be in world space. Use the orientation to correct the
# affine to bring back to voxel.
# Since the affine from Nifti goes from voxel to RAS, we need to
# *-1 the 2 first rows if we are in LPS.
orientation = tract_attributes.get("orientation", None)
if orientation is None:
raise AttributeError('Missing the "orientation" attribute for VTK')
elif orientation == "NOT_FOUND":
raise ValueError('Invalid value of "NOT_FOUND" for orientation')
elif orientation == "LPS":
index_to_world_affine[0,:] *= -1.0
index_to_world_affine[1,:] *= -1.0
# Transposed for efficient computations later on.
index_to_world_affine = index_to_world_affine.T.astype('<f4')
world_to_index_affine = linalg.inv(index_to_world_affine)
# Load tracts
if isinstance(tracts_file, tc.formats.tck.TCK)\
or isinstance(tracts_file, tc.formats.vtk.VTK):
if start_at_corner:
shift = 0.5
else:
shift = 0.0
for s in tracts_file:
transformed_s = np.dot(c_[s, np.ones([s.shape[0], 1], dtype='<f4')],
world_to_index_affine)[:, :-1] + shift
yield transformed_s
elif isinstance(tracts_file, tc.formats.trk.TRK):
# Use nb.trackvis to read directly in correct space
# TODO this should be made more robust, using
# all fields in header.
# Currently, load in rasmm space, and then bring back to LPS vox
try:
streamlines, _ = nb.trackvis.read(tract_fname,
as_generator=True,
points_space='rasmm')
except nb.trackvis.HeaderError as er:
print(er)
raise ValueError("\n------ ERROR ------\n\n" +\
"TrackVis header is malformed or incomplete.\n" +\
"Please make sure all fields are correctly set.\n\n" +\
"The error message reported by Nibabel was:\n" +\
str(er))
if start_at_corner:
shift = 0.0
else:
shift = 0.5
for s in streamlines:
transformed_s = np.dot(c_[s[0], np.ones([s[0].shape[0], 1], dtype='<f4')],
world_to_index_affine)[:, :-1] + shift
yield transformed_s
def main():
parser = buildArgsParser()
args = parser.parse_args()
param = {}
if args.isVerbose:
logging.basicConfig(level=logging.DEBUG)
if args.outputTQ:
filename_parts = os.path.splitext(args.output_file)
output_filename = filename_parts[0] + '.tq' + filename_parts[1]
else:
output_filename = args.output_file
out_format = tc.detect_format(output_filename)
if out_format not in [tc.formats.trk.TRK, tc.formats.tck.TCK]:
parser.error("Invalid output streamline file format (must be trk or " +
"tck): {0}".format(output_filename))
return
if os.path.isfile(output_filename):
if args.isForce:
logging.debug('Overwriting "{0}".'.format(output_filename))
else:
parser.error(
'"{0}" already exists! Use -f to overwrite it.'.format(
output_filename))
if not args.min_length > 0:
parser.error('minL must be > 0, {0}mm was provided.'.format(
args.min_length))
if args.max_length < args.min_length:
parser.error(
'maxL must be > than minL, (minL={0}mm, maxL={1}mm).'.format(
args.min_length, args.max_length))
if not np.any([args.nt, args.npv, args.ns]):
args.npv = 1
if args.theta is not None:
theta = gm.math.radians(args.theta)
elif args.curvature > 0:
theta = get_max_angle_from_curvature(args.curvature, args.step_size)
else:
theta = gm.math.radians(45)
if args.mask_interp == 'nn':
mask_interpolation = 'nearest'
elif args.mask_interp == 'tl':
mask_interpolation = 'trilinear'
else:
parser.error("--mask_interp has wrong value. See the help (-h).")
return
param['random'] = args.random
param['skip'] = args.skip
param['algo'] = args.algo
param['mask_interp'] = mask_interpolation
param['field_interp'] = 'nearest'
param['theta'] = theta
param['sf_threshold'] = args.sf_threshold
param['sf_threshold_init'] = args.sf_threshold_init
param['step_size'] = args.step_size
param['rk_order'] = args.rk_order
param['max_length'] = args.max_length
param['min_length'] = args.min_length
param['max_nbr_pts'] = int(param['max_length'] / param['step_size'])
param['min_nbr_pts'] = int(param['min_length'] / param['step_size']) + 1
param['is_single_direction'] = args.is_single_direction
param['nbr_seeds'] = args.nt if args.nt is not None else 0
param['nbr_seeds_voxel'] = args.npv if args.npv is not None else 0
param['nbr_streamlines'] = args.ns if args.ns is not None else 0
param['max_no_dir'] = int(math.ceil(args.maxL_no_dir / param['step_size']))
param['is_all'] = False
param['is_keep_single_pts'] = False
# r+ is necessary for interpolation function in cython who
# need read/write right
param['mmap_mode'] = None if args.isLoadData else 'r+'
logging.debug('Tractography parameters:\n{0}'.format(param))
seed_img = nib.load(args.seed_file)
seed = Seed(seed_img)
if args.npv:
param['nbr_seeds'] = len(seed.seeds) * param['nbr_seeds_voxel']
param['skip'] = len(seed.seeds) * param['skip']
if len(seed.seeds) == 0:
parser.error('"{0}" does not have voxels value > 0.'.format(
args.seed_file))
mask = BinaryMask(Dataset(nib.load(args.mask_file), param['mask_interp']))
dataset = Dataset(nib.load(args.peaks_file), param['field_interp'])
field = MaximaField(dataset, param['sf_threshold'],
param['sf_threshold_init'], param['theta'])
if args.algo == 'det':
tracker = deterministicMaximaTracker(field, param)
elif args.algo == 'prob':
tracker = probabilisticTracker(field, param)
else:
parser.error("--algo has wrong value. See the help (-h).")
return
start = time.time()
if args.compress:
if args.compress < 0.001 or args.compress > 1:
logging.warn(
'You are using an error rate of {}.\n'.format(args.compress) +
'We recommend setting it between 0.001 and 1.\n' +
'0.001 will do almost nothing to the tracts while ' +
'1 will higly compress/linearize the tracts')
streamlines = track(tracker,
mask,
seed,
param,
compress=True,
compression_error_threshold=args.compress,
nbr_processes=args.nbr_processes,
pft_tracker=None)
else:
streamlines = track(tracker,
mask,
seed,
param,
nbr_processes=args.nbr_processes,
pft_tracker=None)
if args.outputTQ:
save_streamlines_tractquerier(streamlines, args.seed_file,
output_filename)
else:
save_streamlines_fibernavigator(streamlines, args.seed_file,
output_filename)
str_ave_length = "%.2f" % compute_average_streamlines_length(streamlines)
str_time = "%.2f" % (time.time() - start)
logging.debug(
str(len(streamlines)) + " streamlines, with an average " +
"length of " + str_ave_length + " mm, done in " + str_time +
" seconds.")
def main():
parser = buildArgsParser()
args = parser.parse_args()
#####################################
# Checking if the files exist #
#####################################
for myFile in [args.tracts_filename, args.ref_anat_name]:
if not os.path.isfile(myFile):
parser.error('"{0}" must be a file!'.format(myFile))
if os.path.exists(args.output_name):
print(args.output_name, " already exist and will be overwritten.")
#####################################
# Loading tracts #
#####################################
tracts_format = tc.detect_format(args.tracts_filename)
tract_file = tracts_format(args.tracts_filename,
anatFile=args.ref_anat_name)
tracts = [i for i in tract_file]
hdr = tract_file.hdr
#####################################
# Checking if needs subsampling #
#####################################
tmp = len(tracts[0])
if all(len(my_tract) == tmp for my_tract in tracts):
nb_of_points = tmp
else:
nb_of_points = args.nb_of_points
#####################################
# Compute QuickBundles #
#####################################
print('Starting the QuickBundles...')
# This feature tells QuickBundles to resample each streamlines on the fly.
feature = ResampleFeature(nb_points=nb_of_points)
# 'qb' is `dipy.segment.clustering.QuickBundles` object.
qb = QuickBundles(threshold=args.dist_thresh, metric=MDF(feature))
# 'clusters' is `dipy.segment.clustering.ClusterMap` object.
clusters = qb.cluster(tracts)
centroids = clusters.centroids
print(' --- done. Number of centroids:', len(centroids))
print(' Number of points per tract:', nb_of_points)
print('Cluster sizes:', list(map(len, clusters)))
#####################################
# Saving #
#####################################
print('Saving...')
out_format = tc.detect_format(args.output_name)
qb_header = hdr
qb_header[Header.NB_FIBERS] = len(centroids)
out_centroids = out_format.create(args.output_name,
qb_header,
anatFile=args.ref_anat_name)
out_centroids += [s for s in centroids]
out_centroids.close()
print(' --- done.')
#converting trk file to vtk file
input_anatomy_ref = '/home/bao/tiensy/Lauren_registration/data_compare_mapping/anatomy/' + subj + '_data_brain.nii.gz'
#it is a link to '/home/bao/Personal/PhD_at_Trento/Research/ALS_Nivedita_Bao/Segmentation_CST_francesca/' + subj + '/DTI/data_brain.nii.gz'
#cst_vtk_fname = '/home/bao/tiensy/Tractography_Mapping/data/trackvis_tractography/ROI_seg_tvis/ROI_seg_tvis_native/' + subj + '_corticospinal_R_tvis.vtk'
cst_ext_vtk_fname = '/home/bao/tiensy/Tractography_Mapping/data/trackvis_tractography/ROI_seg_tvis/ROI_seg_tvis_native/' + subj + '_cst_R_tvis_ext.vtk'
in_format = str(".trk")
out_format = ".vtk"
#input_file = cst_trk_fname
#output_file = cst_vtk_fname
input_file = cst_ext_trk_fname
output_file = cst_ext_vtk_fname
input_format = tractconverter.detect_format(input_file)
in_put = input_format(input_file, input_anatomy_ref)
out_put = tractconverter.FORMATS['vtk'].create(output_file, in_put.hdr, input_anatomy_ref)
tractconverter.convert(in_put, out_put)
print "Done", output_file
'''
input_path = '/home/bao/tiensy/Lauren_registration/data_compare_mapping/tractography/'
output_path = '/home/bao/tiensy/Lauren_registration/data_compare_mapping/tractography/'
for id_obj in np.arange(len(sub)):
def main():
parser = buildArgsParser()
args = parser.parse_args()
if not os.path.isfile(args.tracts):
parser.error("Tracts file: {0} does not exist.".format(args.tracts))
if not os.path.isfile(args.aparc):
parser.error("Label file: {0} does not exist.".format(args.aparc))
if not os.path.isfile(args.labels):
parser.error("Requested region file: {0} does not exist.".format(
args.labels))
if not os.path.isfile(args.lut):
parser.error("Freesurfer LUT file: {0} does not exist.".format(
args.lut))
if not os.path.isfile(args.faimage):
parser.error("FA Image file: {0} does not exist.".format(args.faimage))
if not os.path.isfile(args.mdimage):
parser.error("MD Image file: {0} does not exist.".format(args.mdimage))
# Validate that tracts can be processed
if not validate_coordinates(args.aparc, args.tracts, nifti_compliant=True):
parser.error("The tracts file contains points that are invalid.\n" +
"Use the remove_invalid_coordinates.py script to clean.")
# Load label image
labels_img = nib.load(args.aparc)
full_labels = labels_img.get_data().astype('int')
# Load fibers
tract_format = tc.detect_format(args.tracts)
tract = tract_format(args.tracts, args.aparc)
affine = compute_affine_for_dipy_functions(args.aparc, args.tracts)
#load FA and MD image
fa_img = nib.load(args.faimage)
fa_data = fa_img.get_data()
md_img = nib.load(args.mdimage)
md_data = md_img.get_data()
# ========= processing streamlines =================
fiberlen_range = np.asarray([args.minlen, args.maxlen])
streamlines = [t for t in tract]
print "Subject " + args.sub_id + " has " + str(
len(streamlines)) + " raw streamlines."
f_streamlines = [] #filtered streamlines
lenrecord = []
idx = 0
for sl in streamlines:
# Avoid streamlines having only one point, as they crash the
# Dipy connectivity matrix function.
if sl.shape[0] > 1:
flen = length(sl)
# get fibers having length between 20mm and 200mm
if (flen > fiberlen_range[0]) & (flen < fiberlen_range[1]):
f_streamlines.append(sl)
lenrecord.append(flen)
idx = idx + 1
print "Subject " + args.sub_id + " has " + str(
idx) + " streamlines with lengths between " + str(
args.minlen) + " and " + str(args.maxlen) + "."
# ============= process the parcellation =====================
dilation_para = np.array([args.dilation_dist, args.dilation_windsize])
# Compute the mapping from label name to label id
label_id_mapping = compute_labels_map(args.lut)
# Find which labels were requested by the user.
requested_labels_mapping = compute_requested_labels(
args.labels, label_id_mapping)
# Filter to keep only needed ones
filtered_labels = np.zeros(full_labels.shape, dtype='int')
for label_val in requested_labels_mapping:
if sum(sum(sum(full_labels == label_val))) == 0:
print label_val
print requested_labels_mapping[label_val]
filtered_labels[full_labels == label_val] = label_val
#cortex band dilation
dilated_labels = cortexband_dilation_wm(filtered_labels, full_labels,
dilation_para)
# Reduce the range of labels to avoid a sparse matrix,
# because the ids of labels can range from 0 to the 12000's.
reduced_labels, labels_lut = dpu.reduce_labels(filtered_labels)
reduced_dilated_labels, labels_lut = dpu.reduce_labels(dilated_labels)
# Compute connectivity matrix and extract the fibers
M, grouping = nconnectivity_matrix(f_streamlines,
reduced_dilated_labels,
fiberlen_range,
args.cnpoint,
affine=affine,
symmetric=True,
return_mapping=True,
mapping_as_streamlines=True)
Msize = len(M)
CM_before_outlierremove = M[1:, 1:]
nstream_bf = np.sum(CM_before_outlierremove)
print args.sub_id + ' ' + str(
nstream_bf
) + ' streamlines in the connectivity matrix before outlier removal.'
#===================== process the streamlines =============
print 'Processing streamlines to remove outliers ..............'
outlier_para = 3
average_thrd = 8
M_after_ourlierremove = np.zeros((Msize, Msize))
#downsample streamlines
cell_streamlines = []
cell_id = []
for i in range(1, Msize):
for j in range(i + 1, Msize):
tmp_streamlines = grouping[i, j]
tmp_streamlines = list(tmp_streamlines)
#downsample
tmp_streamlines_downsampled = [
downsample(s, 100) for s in tmp_streamlines
]
#remove outliers, we need to rewrite the QuickBundle method to speed up this process
qb = QuickBundles(threshold=average_thrd)
clusters = qb.cluster(tmp_streamlines_downsampled)
outlier_clusters = clusters < outlier_para #small clusters
nonoutlier_clusters = clusters[np.logical_not(outlier_clusters)]
tmp_nonoutlier_index = []
for tmp_cluster in nonoutlier_clusters:
tmp_nonoutlier_index = tmp_nonoutlier_index + tmp_cluster.indices
clean_streamline_downsampled = [
tmp_streamlines_downsampled[ind]
for ind in tmp_nonoutlier_index
]
cell_streamlines.append(clean_streamline_downsampled)
cell_id.append([i, j])
M_after_ourlierremove[i, j] = len(clean_streamline_downsampled)
CM_after_ourlierremove = M_after_ourlierremove[1:, 1:]
nstream_bf = np.sum(CM_after_ourlierremove)
print args.sub_id + ' ' + str(
nstream_bf
) + ' streamlines in the connectivity matrix after outlier removal.'
#save streamlines and count matrix
cmCountMatrix_fname = args.sub_id + "_" + args.pre + "_cm_count_raw.mat"
cmCountMatrix_processed_fname = args.sub_id + "_" + args.pre + "_cm_count_processed.mat"
cmStreamlineMatrix_fname = args.sub_id + "_" + args.pre + "_cm_streamlines.mat"
reduced_labels_fname = args.sub_id + "_" + args.pre + "_reduced_labels.nii.gz"
dilated_labels_fname = args.sub_id + "_" + args.pre + "_dilated_labels.nii.gz"
RoiInfo_fname = args.sub_id + "_" + args.pre + "_RoiInfo.mat"
# save the raw count matrix
CM = M[1:, 1:]
sio.savemat(cmCountMatrix_fname, {'cm': CM})
sio.savemat(cmCountMatrix_processed_fname, {'cm': CM_after_ourlierremove})
# save the streamline matrix
sio.savemat(cmStreamlineMatrix_fname, {'slines': cell_streamlines})
sio.savemat(RoiInfo_fname, {'ROIinfo': cell_id})
print args.sub_id + 'cell_streamlines.mat, ROIinfo.mat has been saved'
filtered_labels_img = nib.Nifti1Image(filtered_labels,
labels_img.get_affine(),
labels_img.get_header())
nib.save(filtered_labels_img, reduced_labels_fname)
print args.sub_id + 'filtered labels have saved'
dilated_labels_img = nib.Nifti1Image(dilated_labels,
labels_img.get_affine(),
labels_img.get_header())
nib.save(dilated_labels_img, dilated_labels_fname)
print args.sub_id + 'dilated labels have saved'
# ===================== process the streamlines and extract features =============
cm_fa_curve = fa_extraction_use_cellinput(cell_streamlines,
cell_id,
fa_data,
Msize,
affine=affine)
(tmp_cm_fa_mean, tmp_cm_fa_max,
cm_count) = fa_mean_extraction(cm_fa_curve, Msize)
# extract MD values along the streamlines
cm_md_curve = fa_extraction_use_cellinput(cell_streamlines,
cell_id,
md_data,
Msize,
affine=affine)
(tmp_cm_md_mean, tmp_cm_md_max,
testcm) = fa_mean_extraction(cm_md_curve, Msize)
#connected surface area
# extract the connective volume ratio
(tmp_cm_volumn,
tmp_cm_volumn_ratio) = rois_connectedvol_cellinput(reduced_labels,
Msize,
cell_streamlines,
cell_id,
affine=affine)
#fiber length
tmp_connectcm_len = rois_fiberlen_cellinput(Msize, cell_streamlines)
#save cm features
cm_md_mean = tmp_cm_md_mean[1:, 1:]
cm_md_max = tmp_cm_md_max[1:, 1:]
cm_fa_mean = tmp_cm_fa_mean[1:, 1:]
cm_fa_max = tmp_cm_fa_max[1:, 1:]
cm_volumn = tmp_cm_volumn[1:, 1:]
cm_volumn_ratio = tmp_cm_volumn_ratio[1:, 1:]
connectcm_len = tmp_connectcm_len[1:, 1:]
sio.savemat(args.pre + "_cm_processed_mdmean_100.mat",
{'cm_mdmean': cm_md_mean})
sio.savemat(args.pre + "_cm_processed_mdmax_100.mat",
{'cm_mdmax': cm_md_max})
sio.savemat(args.pre + "_cm_processed_famean_100.mat",
{'cm_famean': cm_fa_mean})
sio.savemat(args.pre + "_cm_processed_famax_100.mat",
{'cm_famax': cm_fa_max})
sio.savemat(args.pre + "_cm_processed_volumn_100.mat",
{'cm_volumn': cm_volumn})
sio.savemat(args.pre + "_cm_processed_volumn_ratio_100.mat",
{'cm_volumn_ratio': cm_volumn_ratio})
sio.savemat(args.pre + "_cm_processed_volumn_ratio_100.mat",
{'cm_len': connectcm_len})
# save the diffusion functions matrix
cell_fa = []
for i in range(1, Msize):
for j in range(i + 1, Msize):
tmp_fa = cm_fa_curve[i, j]
tmp_fa = list(tmp_fa)
cell_fa.append(tmp_fa)
sio.savemat(args.pre + "_cm_processed_sfa_100.mat", {'sfa': cell_fa})
print 'cell_fa.mat, fa_roiinfo.mat have been saved'
cell_md = []
for i in range(1, Msize):
for j in range(i + 1, Msize):
tmp_md = cm_md_curve[i, j]
tmp_md = list(tmp_md)
cell_md.append(tmp_md)
sio.savemat(args.pre + "_cm_processed_smd_100.mat", {'smd': cell_md})
def main():
parser = buildArgsParser()
args = parser.parse_args()
if not os.path.isfile(args.tracts):
parser.error("Tracts file: {0} does not exist.".format(args.tracts))
# TODO check scilpy supports
if not os.path.isfile(args.aparc):
parser.error("Label file: {0} does not exist.".format(args.aparc))
if not os.path.isfile(args.labels):
parser.error("Requested region file: {0} does not exist.".format(
args.labels))
if not os.path.isfile(args.lut):
parser.error("Freesurfer LUT file: {0} does not exist.".format(
args.lut))
if os.path.isfile(args.out_matrix) and not args.force_overwrite:
parser.error(
"Output: {0} already exists. To overwrite, use -f.".format(
args.out_matrix))
if os.path.isfile(args.out_row_map) and not args.force_overwrite:
parser.error(
"Output: {0} already exists. To overwrite, use -f.".format(
args.out_row_map))
if os.path.splitext(args.out_matrix)[1] != ".npy":
parser.error("Connectivity matrix must be saved in a .npy file.")
if os.path.splitext(args.out_row_map)[1] != ".pkl":
parser.error("Mapping must be saved in a .pkl file.")
# Validate that tracts can be processed
if not validate_coordinates(args.aparc, args.tracts, nifti_compliant=True):
parser.error("The tracts file contains points that are invalid.\n" +
"Use the remove_invalid_coordinates.py script to clean.")
# Load labels
labels_img = nib.load(args.aparc)
full_labels = labels_img.get_data().astype('int')
# Compute the mapping from label name to label id
label_id_mapping = compute_labels_map(args.lut)
# Find which labels were requested by the user.
requested_labels_mapping = compute_requested_labels(
args.labels, label_id_mapping)
# Filter to keep only needed ones
filtered_labels = np.zeros(full_labels.shape, dtype='int')
for label_val in requested_labels_mapping:
filtered_labels[full_labels == label_val] = label_val
# Reduce the range of labels to avoid a sparse matrix,
# because the ids of labels can range from 0 to the 12000's.
reduced_labels, labels_lut = dpu.reduce_labels(filtered_labels)
# Load tracts
tract_format = tc.detect_format(args.tracts)
tract = tract_format(args.tracts, args.aparc)
streamlines = [t for t in tract]
f_streamlines = []
for sl in streamlines:
# Avoid streamlines having only one point, as they crash the
# Dipy connectivity matrix function.
if sl.shape[0] > 1:
f_streamlines.append(sl)
# Compute affine
affine = compute_affine_for_dipy_functions(args.aparc, args.tracts)
# Compute matrix
M = dpu.connectivity_matrix(f_streamlines,
reduced_labels,
affine=affine,
symmetric=True,
return_mapping=False,
mapping_as_streamlines=False)
# Remove background connectivity
M = M[1:, 1:]
# Save needed files
np.save(args.out_matrix, np.array(M))
# Compute the mapping between row numbers, labels and ids.
sorted_lut = sorted(labels_lut)
row_name_map = {}
# Skip first for BG
for id, lab_val in enumerate(sorted_lut[1:]):
# Find the associated Freesurfer id
free_name = requested_labels_mapping[lab_val]['free_name']
lut_name = requested_labels_mapping[lab_val]['lut_name']
# Find the mean y position of the label to be able to spatially sort.
positions = np.where(full_labels == lab_val)
mean_y = np.mean(positions[1])
row_name_map[id] = {
'free_name': free_name,
'lut_name': lut_name,
'free_label': lab_val,
'mean_y_pos': mean_y
}
with open(args.out_row_map, 'w') as f:
pickle.dump(row_name_map, f)
def main():
parser = buildArgsParser()
args = parser.parse_args()
print( 'Fiber registration...')
#####################################
# Checking if the files exist #
#####################################
for myFile in [args.init_anat_name, args.final_anat_name, args.matrix_name, args.tracts_name]:
if not os.path.isfile(myFile):
parser.error('"{0}" must be a file!'.format(myFile))
if os.path.exists(args.output_name):
print (args.output_name, " already exist and will be overwritten.")
#####################################
# Loading anatomies #
# Applying voxel sizes #
#####################################
init_anat_img = nib.load(args.init_anat_name)
init_shape = np.array(init_anat_img.shape) * np.array(init_anat_img.get_header()['pixdim'][1:4])
#####################################
# Loading tracts #
#####################################
tracts_format = tc.detect_format(args.tracts_name)
tracts_file = tracts_format(args.tracts_name, anatFile=args.init_anat_name )
hdr = tracts_file.hdr
tracts_init = [i for i in tracts_file]
nb_tracts = len(tracts_init)
#####################################
# Loading the matrix #
#####################################
Big_matrix = np.loadtxt(args.matrix_name)
Rot= Big_matrix[0:3,0:3]
Trans = Big_matrix[0:3,3]
#####################################
# Registration #
#####################################
#tracts_final = register(init_shape, nb_tracts, tracts_init, Rot, Trans)
tracts_final = []
for this_tract in range(nb_tracts):
nb_points_in_tract = len(tracts_init[this_tract])
tracts_final.append(np.zeros((nb_points_in_tract,3)))
for this_point in range(nb_points_in_tract):
indice = tracts_init[this_tract][this_point]
indice_flip = np.asarray([init_shape[0] - indice[0], indice[1], indice[2]]) #flipping
indice_registered = np.dot(Rot, [indice_flip[0], indice_flip[1], indice_flip[2]]) + Trans
indice_registered = np.asarray([init_shape[0]-indice_registered[0], indice_registered[1], indice_registered[2]]) #flipping back
tracts_final[this_tract][this_point] = indice_registered
#####################################
# Saving #
#####################################
out_format = tc.detect_format(args.output_name)
out_tracts = out_format.create(args.output_name, hdr, anatFile=args.final_anat_name)
out_tracts += [t for t in tracts_final] # a tester
out_tracts.close()
print('...Done')
def main():
parser = buildArgsParser()
args = parser.parse_args()
if args.isVerbose:
logging.basicConfig(level=logging.DEBUG)
if not args.not_all:
if not args.tdi_file:
args.tdi_file = 'tdi.nii.gz'
if not args.apm_file:
args.apm_file = 'apm.nii.gz'
if not args.cdec_file:
args.cdec_file = 'cdec.nii.gz'
arglist = [args.tdi_file, args.apm_file, args.cdec_file]
if args.not_all and not any(arglist):
parser.error('When using --not_all, you need to specify at least ' +
'one file to output.')
for out in arglist:
if os.path.isfile(out):
if args.overwrite:
logging.info('Overwriting "{0}".'.format(out))
else:
parser.error(
'"{0}" already exists! Use -f to overwrite it.'.format(
out))
ref = nib.load(args.ref_file)
ref_res = ref.get_header()['pixdim'][1]
up_factor = ref_res / args.res
data_shape = np.array(ref.shape) * up_factor
data_shape = list(data_shape.astype('int32'))
logging.info("Reference resolution: " + str(ref_res))
logging.info("Reference shape: " + str(ref.shape))
logging.info("Target resolution: " + str(args.res))
logging.info("Target shape: " + str(data_shape))
cdec_map = np.zeros(data_shape + [3], dtype='float32')
tdi_map = np.zeros(data_shape, dtype='float32')
apm_map = np.zeros(data_shape, dtype='float32')
tract_format = tc.detect_format(args.tract_file)
tract = tract_format(args.tract_file)
streamlines = [i for i in tract]
streamlines_np = np.array(streamlines, dtype=np.object)
for i, streamline in enumerate(streamlines_np):
if not i % 10000:
logging.info(str(i) + "/" + str(streamlines_np.shape[0]))
streamline_length = length(streamline)
dec_vec = np.array(streamline[0] - streamline[-1])
dec_vec_norm = np.linalg.norm(dec_vec)
if dec_vec_norm > 0:
dec_vec = np.abs(dec_vec / dec_vec_norm)
else:
dec_vec[0] = dec_vec[1] = dec_vec[2] = 0
for point in streamline:
pos = point / args.res
ind = tuple(pos.astype('int32'))
if (ind[0] >= 0 and ind[0] < data_shape[0] and ind[1] >= 0
and ind[1] < data_shape[1] and ind[2] >= 0
and ind[2] < data_shape[2]):
tdi_map[ind] += 1
apm_map[ind] += streamline_length
cdec_map[ind] += dec_vec
# devide the sum of streamline length by the streamline density
apm_map /= tdi_map
# normalise the cdec map
cdec_norm = np.sqrt((cdec_map * cdec_map).sum(axis=3))
cdec_map = cdec_map / cdec_norm.reshape(list(cdec_norm.shape) + [1]) * 255
affine = ref.get_affine()
affine[0][0] = affine[1][1] = affine[2][2] = args.res
if args.tdi_file:
tdi_img = nib.Nifti1Image(tdi_map, affine)
tdi_img.get_header().set_zooms([args.res, args.res, args.res])
tdi_img.get_header().set_qform(ref.get_header().get_qform())
tdi_img.get_header().set_sform(ref.get_header().get_sform())
tdi_img.to_filename(args.tdi_file)
if args.apm_file:
apm_img = nib.Nifti1Image(apm_map, affine)
apm_img.get_header().set_zooms([args.res, args.res, args.res])
apm_img.get_header().set_qform(ref.get_header().get_qform())
apm_img.get_header().set_sform(ref.get_header().get_sform())
apm_img.to_filename(args.apm_file)
if args.cdec_file:
cdec_img = nib.Nifti1Image(cdec_map.astype('uint8'), affine)
cdec_img.get_header().set_zooms([args.res, args.res, args.res, 1])
cdec_img.get_header().set_qform(ref.get_header().get_qform())
cdec_img.get_header().set_sform(ref.get_header().get_sform())
cdec_img.to_filename(args.cdec_file)
def main():
parser = buildArgsParser()
args = parser.parse_args()
if not os.path.isfile(args.tracts):
parser.error("Tracts file: {0} does not exist.".format(args.tracts))
if not os.path.isfile(args.org_aparc):
parser.error("Original label file: {0} does not exist.".format(
args.org_aparc))
if not os.path.isfile(args.dilated_aparc):
parser.error("Dilated label file: {0} does not exist.".format(
args.dilated_aparc))
if not os.path.isfile(args.subcortical_labels):
parser.error("Requested region file: {0} does not exist.".format(
args.subcortical_labels))
if not os.path.isfile(args.lut):
parser.error("Freesurfer LUT file: {0} does not exist.".format(
args.lut))
if not os.path.isfile(args.faimage):
parser.error("FA Image file: {0} does not exist.".format(args.faimage))
if not os.path.isfile(args.mdimage):
parser.error("MD Image file: {0} does not exist.".format(args.mdimage))
# Validate that tracts can be processed
if not validate_coordinates(
args.org_aparc, args.tracts, nifti_compliant=True):
parser.error("The tracts file contains points that are invalid.\n" +
"Use the remove_invalid_coordinates.py script to clean.")
# Load label images
org_labels_img = nib.load(args.org_aparc)
org_labels_data = org_labels_img.get_data().astype('int')
dilated_labels_img = nib.load(args.dilated_aparc)
dilated_labels_data = dilated_labels_img.get_data().astype('int')
# Load fibers
tract_format = tc.detect_format(args.tracts)
tract = tract_format(args.tracts, args.org_aparc)
affine = compute_affine_for_dipy_functions(args.org_aparc, args.tracts)
#load FA and MD image
fa_img = nib.load(args.faimage)
fa_data = fa_img.get_data()
md_img = nib.load(args.mdimage)
md_data = md_img.get_data()
# ========= processing streamlines =================
fiberlen_range = np.asarray([args.minlen, args.maxlen])
streamlines = [t for t in tract]
print "Subjeect " + args.sub_id + " has " + str(
len(streamlines)) + " streamlines."
f_streamlines = [] #filtered streamlines
lenrecord = []
idx = 0
for sl in streamlines:
# Avoid streamlines having only one point, as they crash the
# Dipy connectivity matrix function.
if sl.shape[0] > 1:
flen = length(sl)
# get fibers having length between 20mm and 200mm
if (flen > fiberlen_range[0]) & (flen < fiberlen_range[1]):
f_streamlines.append(sl)
lenrecord.append(flen)
idx = idx + 1
print "Subject " + args.sub_id + " has " + str(
idx - 1) + " streamlines after filtering."
# ============= process the parcellation =====================
# Compute the mapping from label name to label id
label_id_mapping = compute_labels_map(args.lut)
# Find which labels were requested by the user.
requested_labels_mapping = compute_requested_labels(
args.subcortical_labels, label_id_mapping)
# Increase aparc_filtered_labels with subcortical regions
# 17 LH_Hippocampus
# 53 RH_Hippocampus
# 11 LH_Caudate
# 50 RH_Caudate
# 12 LH_Putamen
# 51 RH_Putamen
# 13 LH_Pallidum
# 52 RH_Pallidum
# 18 LH_Amygdala
# 54 RH_Amygdala
# 26 LH_Accumbens
# 58 RH_Accumbens
# 10 LH_Thalamus-Proper
# 49 RH_Thalamus-Proper
# 4 LH_Lateral-Ventricle
# 43 RH_Lateral-Ventricle
# 8 LH_Cerebellum-Cortex
# 47 RH_Cerebellum-Cortex
#
# 16 _Brain-Stem (# 7,8 LH_Cerebellum) (# 41 RH_Cerebellum)
sub_cortical_labels = [
17, 53, 11, 50, 12, 51, 13, 52, 18, 54, 26, 58, 10, 49, 4, 43, 8, 47
] # 16
Brain_Stem_cerebellum = [16] #1
aparc_filtered_labels = dilated_labels_data
for label_val in requested_labels_mapping:
if sum(sum(sum(org_labels_data == label_val))) == 0:
print label_val
print requested_labels_mapping[label_val]
aparc_filtered_labels[org_labels_data == label_val] = label_val
for brain_stem_id in Brain_Stem_cerebellum:
if sum(sum(sum(org_labels_data == brain_stem_id))) == 0:
print 'no labels of '
print brain_stem_id
aparc_filtered_labels[
org_labels_data ==
brain_stem_id] = 99 # let the brain stem's label be 30
# Reduce the range of labels to avoid a sparse matrix,
# because the ids of labels can range from 0 to the 12000's.
reduced_dilated_labels, labels_lut = dpu.reduce_labels(
aparc_filtered_labels)
#dilated_labels_fname = args.sub_id + "_" + args.pre + "_dilated_allbrain_labels.nii.gz"
#dilated_labels_img = nib.Nifti1Image(aparc_filtered_labels, org_labels_img.get_affine(),org_labels_img.get_header())
#nib.save(dilated_labels_img,dilated_labels_fname)
#print args.sub_id + 'dilated labels have saved'
#pdb.set_trace()
# Compute connectivity matrix and extract the fibers
M, grouping = nconnectivity_matrix(f_streamlines,
reduced_dilated_labels,
fiberlen_range,
args.cnpoint,
affine=affine,
symmetric=True,
return_mapping=True,
mapping_as_streamlines=True,
keepfiberinroi=True)
Msize = len(M)
CM_before_outlierremove = M[1:, 1:]
nstream_bf = np.sum(CM_before_outlierremove)
print args.sub_id + ' ' + str(
nstream_bf
) + ' streamlines in the connectivity matrix before outlier removal.'
# ===================== process the streamlines =============
print 'Processing streamlines to remove outliers ..............'
outlier_para = 3
average_thrd = 8
M_after_ourlierremove = np.zeros((Msize, Msize))
# downsample streamlines
cell_streamlines = []
cell_id = []
for i in range(1, Msize):
for j in range(i + 1, Msize):
tmp_streamlines = grouping[i, j]
tmp_streamlines = list(tmp_streamlines)
# downsample
tmp_streamlines_downsampled = [
downsample(s, 100) for s in tmp_streamlines
]
# remove outliers, we need to rewrite the QuickBundle method to speed up this process
qb = QuickBundles(threshold=average_thrd)
clusters = qb.cluster(tmp_streamlines_downsampled)
outlier_clusters = clusters < outlier_para # small clusters
nonoutlier_clusters = clusters[np.logical_not(outlier_clusters)]
tmp_nonoutlier_index = []
for tmp_cluster in nonoutlier_clusters:
tmp_nonoutlier_index = tmp_nonoutlier_index + tmp_cluster.indices
clean_streamline_downsampled = [
tmp_streamlines_downsampled[ind]
for ind in tmp_nonoutlier_index
]
cell_streamlines.append(clean_streamline_downsampled)
cell_id.append([i, j])
M_after_ourlierremove[i, j] = len(clean_streamline_downsampled)
CM_after_ourlierremove = M_after_ourlierremove[1:, 1:]
nstream_bf = np.sum(CM_after_ourlierremove)
print args.sub_id + ' ' + str(
nstream_bf
) + ' streamlines in the connectivity matrix after outlier removal.'
#===================== save the data =======================
if (args.saving_indicator == 1): # save the whole brain connectivity
cmCountMatrix_fname = args.sub_id + "_" + args.pre + "_allbrain" + "_cm_count_raw.mat"
cmCountMatrix_processed_fname = args.sub_id + "_" + args.pre + "_allbrain" + "_cm_count_processed.mat"
cmStreamlineMatrix_fname = args.sub_id + "_" + args.pre + "_allbrain" + "_cm_streamlines.mat"
reduced_dilated_labels_fname = args.sub_id + "_" + args.pre + "_allbrain" + "_reduced_dilated_labels.nii.gz"
RoiInfo_fname = args.sub_id + "_" + args.pre + "_allbrain_RoiInfo.mat"
# save the raw count matrix
CM = M[1:, 1:]
sio.savemat(cmCountMatrix_fname, {'cm': CM})
sio.savemat(cmCountMatrix_processed_fname,
{'cm': CM_after_ourlierremove})
# save the streamline matrix
sio.savemat(cmStreamlineMatrix_fname, {'slines': cell_streamlines})
sio.savemat(RoiInfo_fname, {'ROIinfo': cell_id})
print args.sub_id + 'cell_streamlines.mat, ROIinfo.mat has been saved'
filtered_labels_img = nib.Nifti1Image(aparc_filtered_labels,
org_labels_img.get_affine(),
org_labels_img.get_header())
nib.save(filtered_labels_img, reduced_dilated_labels_fname)
print args.sub_id + 'all brain dilated labels have saved'
# ===================== process the streamlines and extract features =============
cm_fa_curve = fa_extraction_use_cellinput(cell_streamlines,
cell_id,
fa_data,
Msize,
affine=affine)
(tmp_cm_fa_mean, tmp_cm_fa_max,
cm_count) = fa_mean_extraction(cm_fa_curve, Msize)
# extract MD values along the streamlines
cm_md_curve = fa_extraction_use_cellinput(cell_streamlines,
cell_id,
md_data,
Msize,
affine=affine)
(tmp_cm_md_mean, tmp_cm_md_max,
testcm) = fa_mean_extraction(cm_md_curve, Msize)
# connected surface area
# extract the connective volume ratio
(tmp_cm_volumn, tmp_cm_volumn_ratio) = rois_connectedvol_cellinput(
reduced_dilated_labels,
Msize,
cell_streamlines,
cell_id,
affine=affine)
# fiber length
tmp_connectcm_len = rois_fiberlen_cellinput(Msize, cell_streamlines)
# save cm features
cm_md_mean = tmp_cm_md_mean[1:, 1:]
cm_md_max = tmp_cm_md_max[1:, 1:]
cm_fa_mean = tmp_cm_fa_mean[1:, 1:]
cm_fa_max = tmp_cm_fa_max[1:, 1:]
cm_volumn = tmp_cm_volumn[1:, 1:]
cm_volumn_ratio = tmp_cm_volumn_ratio[1:, 1:]
connectcm_len = tmp_connectcm_len[1:, 1:]
sio.savemat(args.pre + "_allbrain" + "_cm_processed_mdmean_100.mat",
{'cm_mdmean': cm_md_mean})
sio.savemat(args.pre + "_allbrain" + "_cm_processed_mdmax_100.mat",
{'cm_mdmax': cm_md_max})
sio.savemat(args.pre + "_allbrain" + "_cm_processed_famean_100.mat",
{'cm_famean': cm_fa_mean})
sio.savemat(args.pre + "_allbrain" + "_cm_processed_famax_100.mat",
{'cm_famax': cm_fa_max})
sio.savemat(args.pre + "_allbrain" + "_cm_processed_volumn_100.mat",
{'cm_volumn': cm_volumn})
sio.savemat(
args.pre + "_allbrain" + "_cm_processed_volumn_ratio_100.mat",
{'cm_volumn_ratio': cm_volumn_ratio})
sio.savemat(args.pre + "_allbrain" + "_cm_processed_fiberlen_100.mat",
{'cm_len': connectcm_len})
# save the streamline matrix
cell_fa = []
for i in range(1, Msize):
for j in range(i + 1, Msize):
tmp_fa = cm_fa_curve[i, j]
tmp_fa = list(tmp_fa)
cell_fa.append(tmp_fa)
sio.savemat(args.pre + "_allbrain" + "_cm_processed_sfa_100.mat",
{'sfa': cell_fa})
print args.pre + '_allbrain" + "_cm_processed_sfa_100.mat' + ' has been saved'
cell_md = []
for i in range(1, Msize):
for j in range(i + 1, Msize):
tmp_md = cm_md_curve[i, j]
tmp_md = list(tmp_md)
cell_md.append(tmp_md)
sio.savemat(args.pre + "_allbrain" + "_cm_processed_smd_100.mat",
{'smd': cell_md})
print args.pre + '_allbrain" + "_cm_processed_smd_100.mat' + ' has been saved'
if (
args.saving_indicator == 0
): # save the part of the connection: connection between subcortical region
Nsubcortical_reg = len(sub_cortical_labels) + 1 # should be 19
cmCountMatrix_fname = args.sub_id + "_" + args.pre + "_partbrain_subcort" + "_cm_count_raw.mat"
cmCountMatrix_processed_fname = args.sub_id + "_" + args.pre + "_partbrain_subcort" + "_cm_count_processed.mat"
cmStreamlineMatrix_fname = args.sub_id + "_" + args.pre + "_partbrain_subcort" + "_cm_streamlines.mat"
reduced_dilated_labels_fname = args.sub_id + "_" + args.pre + "_partbrain_subcort" + "_reduced_dilated_labels.nii.gz"
subcortical_RoiInfo_fname = args.sub_id + "_" + args.pre + "_partbrain_subcort_RoiInfo.mat"
# save the raw count matrix
CM = M[1:, 1:]
sio.savemat(cmCountMatrix_fname, {'cm': CM})
sio.savemat(cmCountMatrix_processed_fname,
{'cm': CM_after_ourlierremove})
filtered_labels_img = nib.Nifti1Image(aparc_filtered_labels,
org_labels_img.get_affine(),
org_labels_img.get_header())
nib.save(filtered_labels_img, reduced_dilated_labels_fname)
print args.sub_id + ' all brain dilated labels have saved'
# ===================== process the streamlines and extract features =============
cm_fa_curve = fa_extraction_use_cellinput(cell_streamlines,
cell_id,
fa_data,
Msize,
affine=affine)
(tmp_cm_fa_mean, tmp_cm_fa_max,
cm_count) = fa_mean_extraction(cm_fa_curve, Msize)
# extract MD values along the streamlines
cm_md_curve = fa_extraction_use_cellinput(cell_streamlines,
cell_id,
md_data,
Msize,
affine=affine)
(tmp_cm_md_mean, tmp_cm_md_max,
testcm) = fa_mean_extraction(cm_md_curve, Msize)
# connected surface area
# extract the connective volume ratio
(tmp_cm_volumn, tmp_cm_volumn_ratio) = rois_connectedvol_cellinput(
reduced_dilated_labels,
Msize,
cell_streamlines,
cell_id,
affine=affine)
# fiber length
tmp_connectcm_len = rois_fiberlen_cellinput(Msize, cell_streamlines)
# save cm features
cm_md_mean = tmp_cm_md_mean[1:, 1:]
cm_md_max = tmp_cm_md_max[1:, 1:]
cm_fa_mean = tmp_cm_fa_mean[1:, 1:]
cm_fa_max = tmp_cm_fa_max[1:, 1:]
cm_volumn = tmp_cm_volumn[1:, 1:]
cm_volumn_ratio = tmp_cm_volumn_ratio[1:, 1:]
connectcm_len = tmp_connectcm_len[1:, 1:]
sio.savemat(
args.pre + "_partbrain_subcort" + "_cm_processed_mdmean_100.mat",
{'cm_mdmean': cm_md_mean})
sio.savemat(
args.pre + "_partbrain_subcort" + "_cm_processed_mdmax_100.mat",
{'cm_mdmax': cm_md_max})
sio.savemat(
args.pre + "_partbrain_subcort" + "_cm_processed_famean_100.mat",
{'cm_famean': cm_fa_mean})
sio.savemat(
args.pre + "_partbrain_subcort" + "_cm_processed_famax_100.mat",
{'cm_famax': cm_fa_max})
sio.savemat(
args.pre + "_partbrain_subcort" + "_cm_processed_volumn_100.mat",
{'cm_volumn': cm_volumn})
sio.savemat(
args.pre + "_partbrain_subcort" +
"_cm_processed_volumn_ratio_100.mat",
{'cm_volumn_ratio': cm_volumn_ratio})
sio.savemat(
args.pre + "_partbrain_subcort" + "_cm_processed_fiberlen_100.mat",
{'cm_len': connectcm_len})
# save the streamline matrix
cell_fa = []
cell_id = []
for i in range(1, Nsubcortical_reg):
for j in range(i + 1, Msize):
tmp_fa = cm_fa_curve[i, j]
tmp_fa = list(tmp_fa)
cell_fa.append(tmp_fa)
cell_id.append([i, j])
sio.savemat(
args.pre + "_partbrain_subcort" + "_cm_processed_sfa_100.mat",
{'sfa': cell_fa})
print args.pre + '_partbrain_subcort' + '_cm_processed_sfa_100.mat' + 'has been saved.'
cell_md = []
for i in range(1, Nsubcortical_reg):
for j in range(i + 1, Msize):
tmp_md = cm_md_curve[i, j]
tmp_md = list(tmp_md)
cell_md.append(tmp_md)
sio.savemat(args.pre + "_partbrain" + "_cm_processed_smd_100.mat",
{'smd': cell_md})
print args.pre + '_partbrain' + '_cm_processed_smd_100.mat' + 'has been saved.'
# save the streamline matrix
subcortical_cell_streamlines = []
cell_id = []
idx = 0
for i in range(1, Nsubcortical_reg):
for j in range(i + 1, Msize):
tmp_sls = cell_streamlines[idx]
idx = idx + 1
subcortical_cell_streamlines.append(tmp_sls)
cell_id.append([i, j])
sio.savemat(cmStreamlineMatrix_fname,
{'slines': subcortical_cell_streamlines})
sio.savemat(subcortical_RoiInfo_fname, {'ROIinfo': cell_id})
print cmStreamlineMatrix_fname + ' has been saved'