def test_cmc_stopping_criterion():
"""This tests that the cmc stopping criterion returns expected
streamline statuses.
"""
gm = np.array([[[1, 1], [0, 0], [0, 0]]])
wm = np.array([[[0, 0], [1, 1], [0, 0]]])
csf = np.array([[[0, 0], [0, 0], [1, 1]]])
include_map = gm
exclude_map = csf
cmc_tc = CmcStoppingCriterion(include_map=include_map,
exclude_map=exclude_map,
step_size=1,
average_voxel_size=1)
cmc_tc_from_pve = CmcStoppingCriterion.from_pve(wm_map=wm,
gm_map=gm,
csf_map=csf,
step_size=1,
average_voxel_size=1)
# Test constructors
for idx in np.ndindex(wm.shape):
idx = np.asarray(idx, dtype="float64")
npt.assert_almost_equal(cmc_tc.get_include(idx),
cmc_tc_from_pve.get_include(idx))
npt.assert_almost_equal(cmc_tc.get_exclude(idx),
cmc_tc_from_pve.get_exclude(idx))
# Test voxel center
for ind in ndindex(wm.shape):
pts = np.array(ind, dtype='float64')
state = cmc_tc.check_point(pts)
if csf[ind] == 1:
npt.assert_equal(state, int(StreamlineStatus.INVALIDPOINT))
elif gm[ind] == 1:
npt.assert_equal(state, int(StreamlineStatus.ENDPOINT))
else:
npt.assert_equal(state, int(StreamlineStatus.TRACKPOINT))
# Test outside points
outside_pts = [[100, 100, 100], [0, -1, 1], [0, 10, 2], [0, 0.5, -0.51],
[0, -0.51, 0.1]]
for pts in outside_pts:
pts = np.array(pts, dtype='float64')
npt.assert_equal(cmc_tc.check_point(pts),
int(StreamlineStatus.OUTSIDEIMAGE))
npt.assert_equal(cmc_tc.get_exclude(pts), 0)
npt.assert_equal(cmc_tc.get_include(pts), 0)
def prep_tracking(self):
"""Uses nibabel and dipy functions in order to load the grey matter, white matter, and csf masks
and use a tissue classifier (act, cmc, or binary) on the include/exclude maps to make a tissueclassifier object
Returns
-------
ActStoppingCriterion, CmcStoppingCriterion, or BinaryStoppingCriterion
The resulting tissue classifier object, depending on which method you use (currently only does act)
"""
if self.track_type == "local":
tiss_class = "bin"
elif self.track_type == "particle":
tiss_class = "cmc"
self.dwi_img = nib.load(self.dwi)
self.data = self.dwi_img.get_data()
# Loads mask and ensures it's a true binary mask
self.mask_img = nib.load(self.nodif_B0_mask)
self.mask = self.mask_img.get_data() > 0
# Load tissue maps and prepare tissue classifier
self.gm_mask = nib.load(self.gm_in_dwi)
self.gm_mask_data = self.gm_mask.get_data()
self.wm_mask = nib.load(self.wm_in_dwi)
self.wm_mask_data = self.wm_mask.get_data()
self.wm_in_dwi_data = nib.load(
self.wm_in_dwi).get_data().astype("bool")
if tiss_class == "act":
self.vent_csf_in_dwi = nib.load(self.vent_csf_in_dwi)
self.vent_csf_in_dwi_data = self.vent_csf_in_dwi.get_data()
self.background = np.ones(self.gm_mask.shape)
self.background[(self.gm_mask_data + self.wm_mask_data +
self.vent_csf_in_dwi_data) > 0] = 0
self.include_map = self.wm_mask_data
self.include_map[self.background > 0] = 0
self.exclude_map = self.vent_csf_in_dwi_data
self.tiss_classifier = ActStoppingCriterion(
self.include_map, self.exclude_map)
elif tiss_class == "bin":
self.tiss_classifier = BinaryStoppingCriterion(self.wm_in_dwi_data)
# self.tiss_classifier = BinaryStoppingCriterion(self.mask)
elif tiss_class == "cmc":
self.vent_csf_in_dwi = nib.load(self.vent_csf_in_dwi)
self.vent_csf_in_dwi_data = self.vent_csf_in_dwi.get_data()
voxel_size = np.average(self.wm_mask.get_header()["pixdim"][1:4])
step_size = 0.2
self.tiss_classifier = CmcStoppingCriterion.from_pve(
self.wm_mask_data,
self.gm_mask_data,
self.vent_csf_in_dwi_data,
step_size=step_size,
average_voxel_size=voxel_size,
)
else:
pass
return self.tiss_classifier
def _cmc_sc(self):
from dipy.tracking.stopping_criterion import CmcStoppingCriterion
f_pve_csf, f_pve_gm, f_pve_wm = load_pve_files(self.subj_folder,
self.pve_file_name)[:3]
pve_csf_data = load_nifti_data(f_pve_csf)
pve_gm_data = load_nifti_data(f_pve_gm)
pve_wm_data = load_nifti_data(f_pve_wm)
cmc_criterion = CmcStoppingCriterion.from_pve(
pve_wm_data,
pve_gm_data,
pve_csf_data,
step_size=self.parameters_dict['step_size'],
average_voxel_size=self.parameters_dict['voxel_size'])
self.classifier = cmc_criterion
def create_cmc_classifier(folder_name):
from dipy.tracking.stopping_criterion import CmcStoppingCriterion
f_pve_csf, f_pve_gm, f_pve_wm = load_pve_files(folder_name)
pve_csf_data = load_nifti_data(f_pve_csf)
pve_gm_data = load_nifti_data(f_pve_gm)
pve_wm_data, _, voxel_size = load_nifti(f_pve_wm, return_voxsize=True)
voxel_size = np.average(voxel_size[1:4])
step_size = 0.2
cmc_criterion = CmcStoppingCriterion.from_pve(
pve_wm_data,
pve_gm_data,
pve_csf_data,
step_size=step_size,
average_voxel_size=voxel_size)
return cmc_criterion, step_size
anatomical images to determine when the tractography stops.
Both stopping criterion use a trilinear interpolation
at the tracking position. CMC stopping criterion uses a probability derived
from the PVE maps to determine if the streamline reaches a 'valid' or 'invalid'
region. ACT uses a fixed threshold on the PVE maps. Both stopping criterion can
be used in conjunction with PFT. In this example, we used CMC.
"""
from dipy.tracking.stopping_criterion import CmcStoppingCriterion
voxel_size = np.average(voxel_size[1:4])
step_size = 0.2
cmc_criterion = CmcStoppingCriterion.from_pve(pve_wm_data,
pve_gm_data,
pve_csf_data,
step_size=step_size,
average_voxel_size=voxel_size)
# Particle Filtering Tractography
pft_streamline_generator = ParticleFilteringTracking(dg,
cmc_criterion,
seeds,
affine,
max_cross=1,
step_size=step_size,
maxlen=1000,
pft_back_tracking_dist=2,
pft_front_tracking_dist=1,
particle_count=15,
return_all=False)
def run(self,
pam_files,
wm_files,
gm_files,
csf_files,
seeding_files,
step_size=0.2,
seed_density=1,
pmf_threshold=0.1,
max_angle=20.,
pft_back=2,
pft_front=1,
pft_count=15,
out_dir='',
out_tractogram='tractogram.trk',
save_seeds=False):
"""Workflow for Particle Filtering Tracking.
This workflow use a saved peaks and metrics (PAM) file as input.
Parameters
----------
pam_files : string
Path to the peaks and metrics files. This path may contain
wildcards to use multiple masks at once.
wm_files : string
Path to white matter partial volume estimate for tracking (CMC).
gm_files : string
Path to grey matter partial volume estimate for tracking (CMC).
csf_files : string
Path to cerebrospinal fluid partial volume estimate for tracking
(CMC).
seeding_files : string
A binary image showing where we need to seed for tracking.
step_size : float, optional
Step size used for tracking (default 0.2mm).
seed_density : int, optional
Number of seeds per dimension inside voxel (default 1).
For example, seed_density of 2 means 8 regularly distributed
points in the voxel. And seed density of 1 means 1 point at the
center of the voxel.
pmf_threshold : float, optional
Threshold for ODF functions (default 0.1).
max_angle : float, optional
Maximum angle between streamline segments (range [0, 90],
default 20).
pft_back : float, optional
Distance in mm to back track before starting the particle filtering
tractography (default 2mm). The total particle filtering
tractography distance is equal to back_tracking_dist +
front_tracking_dist.
pft_front : float, optional
Distance in mm to run the particle filtering tractography after the
the back track distance (default 1mm). The total particle filtering
tractography distance is equal to back_tracking_dist +
front_tracking_dist.
pft_count : int, optional
Number of particles to use in the particle filter (default 15).
out_dir : string, optional
Output directory (default input file directory)
out_tractogram : string, optional
Name of the tractogram file to be saved (default 'tractogram.trk')
save_seeds : bool, optional
If true, save the seeds associated to their streamline
in the 'data_per_streamline' Tractogram dictionary using
'seeds' as the key
References
----------
Girard, G., Whittingstall, K., Deriche, R., & Descoteaux, M. Towards
quantitative connectivity analysis: reducing tractography biases.
NeuroImage, 98, 266-278, 2014.
"""
io_it = self.get_io_iterator()
for pams_path, wm_path, gm_path, csf_path, seeding_path, out_tract \
in io_it:
logging.info(
'Particle Filtering tracking on {0}'.format(pams_path))
pam = load_peaks(pams_path, verbose=False)
wm, affine, voxel_size = load_nifti(wm_path, return_voxsize=True)
gm, _ = load_nifti(gm_path)
csf, _ = load_nifti(csf_path)
avs = sum(voxel_size) / len(voxel_size) # average_voxel_size
stopping_criterion = CmcStoppingCriterion.from_pve(
wm, gm, csf, step_size=step_size, average_voxel_size=avs)
logging.info('stopping criterion done')
seed_mask, _ = load_nifti(seeding_path)
seeds = utils.seeds_from_mask(
seed_mask,
density=[seed_density, seed_density, seed_density],
affine=affine)
logging.info('seeds done')
dg = ProbabilisticDirectionGetter
direction_getter = dg.from_shcoeff(pam.shm_coeff,
max_angle=max_angle,
sphere=pam.sphere,
pmf_threshold=pmf_threshold)
tracking_result = ParticleFilteringTracking(
direction_getter,
stopping_criterion,
seeds,
affine,
step_size=step_size,
pft_back_tracking_dist=pft_back,
pft_front_tracking_dist=pft_front,
pft_max_trial=20,
particle_count=pft_count,
save_seeds=save_seeds)
logging.info('ParticleFilteringTracking initiated')
if save_seeds:
streamlines, seeds = zip(*tracking_result)
seeds = {'seeds': seeds}
else:
streamlines = list(tracking_result)
seeds = {}
sft = StatefulTractogram(streamlines,
seeding_path,
Space.RASMM,
data_per_streamline=seeds)
save_tractogram(sft, out_tract, bbox_valid_check=False)
logging.info('Saved {0}'.format(out_tract))
def track(params_file,
directions="det",
max_angle=30.,
sphere=None,
seed_mask=None,
seed_threshold=0,
n_seeds=1,
random_seeds=False,
rng_seed=None,
stop_mask=None,
stop_threshold=0,
step_size=0.5,
min_length=10,
max_length=1000,
odf_model="DTI",
tracker="local"):
"""
Tractography
Parameters
----------
params_file : str, nibabel img.
Full path to a nifti file containing CSD spherical harmonic
coefficients, or nibabel img with model params.
directions : str
How tracking directions are determined.
One of: {"det" | "prob"}
max_angle : float, optional.
The maximum turning angle in each step. Default: 30
sphere : Sphere object, optional.
The discretization of direction getting. default:
dipy.data.default_sphere.
seed_mask : array, optional.
Float or binary mask describing the ROI within which we seed for
tracking.
Default to the entire volume (all ones).
seed_threshold : float, optional.
A value of the seed_mask below which tracking is terminated.
Default to 0.
n_seeds : int or 2D array, optional.
The seeding density: if this is an int, it is is how many seeds in each
voxel on each dimension (for example, 2 => [2, 2, 2]). If this is a 2D
array, these are the coordinates of the seeds. Unless random_seeds is
set to True, in which case this is the total number of random seeds
to generate within the mask.
random_seeds : bool
Whether to generate a total of n_seeds random seeds in the mask.
Default: False.
rng_seed : int
random seed used to generate random seeds if random_seeds is
set to True. Default: None
stop_mask : array or str, optional.
If array: A float or binary mask that determines a stopping criterion
(e.g. FA).
If tuple: it contains a sequence that is interpreted as:
(pve_wm, pve_gm, pve_csf), each item of which is either a string
(full path) or a nibabel img to be used in particle filtering
tractography.
A tuple is required if tracker is set to "pft".
Defaults to no stopping (all ones).
stop_threshold : float or tuple, optional.
If float, this a value of the stop_mask below which tracking is
terminated (and stop_mask has to be an array).
If str, "CMC" for Continuous Map Criterion [Girard2014]_.
"ACT" for Anatomically-constrained tractography [Smith2012]_.
A string is required if the tracker is set to "pft".
Defaults to 0 (this means that if no stop_mask is passed,
we will stop only at the edge of the image).
step_size : float, optional.
The size (in mm) of a step of tractography. Default: 1.0
min_length: int, optional
The miminal length (mm) in a streamline. Default: 10
max_length: int, optional
The miminal length (mm) in a streamline. Default: 1000
odf_model : str, optional
One of {"DTI", "CSD", "DKI", "MSMT"}. Defaults to use "DTI"
tracker : str, optional
Which strategy to use in tracking. This can be the standard local
tracking ("local") or Particle Filtering Tracking ([Girard2014]_).
One of {"local", "pft"}. Default: "local"
Returns
-------
list of streamlines ()
References
----------
.. [Girard2014] Girard, G., Whittingstall, K., Deriche, R., &
Descoteaux, M. Towards quantitative connectivity analysis: reducing
tractography biases. NeuroImage, 98, 266-278, 2014.
"""
logger = logging.getLogger('AFQ.tractography')
logger.info("Loading Image...")
if isinstance(params_file, str):
params_img = nib.load(params_file)
else:
params_img = params_file
model_params = params_img.get_fdata()
affine = params_img.affine
odf_model = odf_model.upper()
directions = directions.lower()
logger.info("Generating Seeds...")
if isinstance(n_seeds, int):
if seed_mask is None:
seed_mask = np.ones(params_img.shape[:3])
elif seed_mask.dtype != 'bool':
seed_mask = seed_mask > seed_threshold
if random_seeds:
seeds = dtu.random_seeds_from_mask(seed_mask,
seeds_count=n_seeds,
seed_count_per_voxel=False,
affine=affine,
random_seed=rng_seed)
else:
seeds = dtu.seeds_from_mask(seed_mask,
density=n_seeds,
affine=affine)
else:
# If user provided an array, we'll use n_seeds as the seeds:
seeds = n_seeds
if sphere is None:
sphere = dpd.default_sphere
logger.info("Getting Directions...")
if directions == "det":
dg = DeterministicMaximumDirectionGetter
elif directions == "prob":
dg = ProbabilisticDirectionGetter
if odf_model == "DTI" or odf_model == "DKI" or odf_model == "FWDTI":
evals = model_params[..., :3]
evecs = model_params[..., 3:12].reshape(params_img.shape[:3] + (3, 3))
odf = tensor_odf(evals, evecs, sphere)
dg = dg.from_pmf(odf, max_angle=max_angle, sphere=sphere)
elif odf_model == "CSD" or "MSMT":
dg = dg.from_shcoeff(model_params, max_angle=max_angle, sphere=sphere)
if tracker == "local":
if stop_mask is None:
stop_mask = np.ones(params_img.shape[:3])
if stop_mask.dtype == 'bool':
stopping_criterion = ThresholdStoppingCriterion(stop_mask, 0.5)
else:
stopping_criterion = ThresholdStoppingCriterion(
stop_mask, stop_threshold)
my_tracker = VerboseLocalTracking
elif tracker == "pft":
if not isinstance(stop_threshold, str):
raise RuntimeError(
"You are using PFT tracking, but did not provide a string ",
"'stop_threshold' input. ",
"Possible inputs are: 'CMC' or 'ACT'")
if not (isinstance(stop_mask, Iterable) and len(stop_mask) == 3):
raise RuntimeError(
"You are using PFT tracking, but did not provide a length "
"3 iterable for `stop_mask`. "
"Expected a (pve_wm, pve_gm, pve_csf) tuple.")
pves = []
pve_imgs = []
vox_sizes = []
for ii, pve in enumerate(stop_mask):
if isinstance(pve, str):
img = nib.load(pve)
else:
img = pve
pve_imgs.append(img)
pves.append(pve_imgs[-1].get_fdata())
average_voxel_size = np.mean(vox_sizes)
pve_wm_img, pve_gm_img, pve_csf_img = pve_imgs
pve_wm_data, pve_gm_data, pve_csf_data = pves
pve_wm_data = resample(pve_wm_data, model_params[...,
0], pve_wm_img.affine,
params_img.affine).get_fdata()
pve_gm_data = resample(pve_gm_data, model_params[...,
0], pve_gm_img.affine,
params_img.affine).get_fdata()
pve_csf_data = resample(pve_csf_data, model_params[..., 0],
pve_csf_img.affine,
params_img.affine).get_fdata()
vox_sizes.append(np.mean(params_img.header.get_zooms()[:3]))
my_tracker = VerboseParticleFilteringTracking
if stop_threshold == "CMC":
stopping_criterion = CmcStoppingCriterion.from_pve(
pve_wm_data,
pve_gm_data,
pve_csf_data,
step_size=step_size,
average_voxel_size=average_voxel_size)
elif stop_threshold == "ACT":
stopping_criterion = ActStoppingCriterion.from_pve(
pve_wm_data, pve_gm_data, pve_csf_data)
logger.info("Tracking...")
return _tracking(my_tracker,
seeds,
dg,
stopping_criterion,
params_img,
step_size=step_size,
min_length=min_length,
max_length=max_length,
random_seed=rng_seed)
def prep_tissues(t1_mask,
gm_in_dwi,
vent_csf_in_dwi,
wm_in_dwi,
tiss_class,
B0_mask,
cmc_step_size=0.2):
"""
Estimate a tissue classifier for tractography.
Parameters
----------
t1_mask : Nifti1Image
T1w mask img.
gm_in_dwi : Nifti1Image
Grey-matter tissue segmentation Nifti1Image.
vent_csf_in_dwi : Nifti1Image
Ventricular CSF tissue segmentation Nifti1Image.
wm_in_dwi : Nifti1Image
White-matter tissue segmentation Nifti1Image.
tiss_class : str
Tissue classification method.
cmc_step_size : float
Step size from CMC tissue classification method.
Returns
-------
tiss_classifier : obj
Tissue classifier object.
References
----------
.. [1] Zhang, Y., Brady, M. and Smith, S. Segmentation of Brain MR Images
Through a Hidden Markov Random Field Model and the
Expectation-Maximization Algorithm IEEE Transactions on Medical Imaging,
20(1): 45-56, 2001
.. [2] Avants, B. B., Tustison, N. J., Wu, J., Cook, P. A. and Gee, J. C.
An open source multivariate framework for n-tissue segmentation with
evaluation on public data. Neuroinformatics, 9(4): 381-400, 2011.
"""
import gc
from dipy.tracking.stopping_criterion import (
ActStoppingCriterion,
CmcStoppingCriterion,
BinaryStoppingCriterion,
)
from nilearn.masking import intersect_masks
from nilearn.image import math_img
# Load B0 mask
B0_mask_img = math_img("img > 0.01", img=B0_mask)
# Load t1 mask
mask_img = math_img("img > 0.01", img=t1_mask)
# Load tissue maps and prepare tissue classifier
wm_mask_img = math_img("img > 0.01", img=wm_in_dwi)
gm_mask_img = math_img("img > 0.01", img=gm_in_dwi)
vent_csf_in_dwi_img = math_img("img > 0.01", img=vent_csf_in_dwi)
gm_data = np.asarray(gm_mask_img.dataobj, dtype=np.float32)
wm_data = np.asarray(wm_mask_img.dataobj, dtype=np.float32)
vent_csf_in_dwi_data = np.asarray(vent_csf_in_dwi_img.dataobj,
dtype=np.float32)
if tiss_class == "act":
background = np.ones(mask_img.shape)
background[(gm_data + wm_data + vent_csf_in_dwi_data) > 0] = 0
gm_data[background > 0] = 1
tiss_classifier = ActStoppingCriterion(gm_data, vent_csf_in_dwi_data)
del background
elif tiss_class == "wm":
tiss_classifier = BinaryStoppingCriterion(
np.asarray(
intersect_masks(
[
mask_img, wm_mask_img, B0_mask_img,
nib.Nifti1Image(np.invert(
vent_csf_in_dwi_data.astype('bool')).astype('int'),
affine=mask_img.affine)
],
threshold=1,
connected=False,
).dataobj))
elif tiss_class == "cmc":
tiss_classifier = CmcStoppingCriterion.from_pve(
wm_data,
gm_data,
vent_csf_in_dwi_data,
step_size=cmc_step_size,
average_voxel_size=np.average(mask_img.header["pixdim"][1:4]),
)
elif tiss_class == "wb":
tiss_classifier = BinaryStoppingCriterion(
np.asarray(
intersect_masks(
[
mask_img,
B0_mask_img,
nib.Nifti1Image(np.invert(
vent_csf_in_dwi_data.astype('bool')).astype('int'),
affine=mask_img.affine),
],
threshold=1,
connected=False,
).dataobj))
else:
raise ValueError("Tissue classifier cannot be none.")
B0_mask_img.uncache()
mask_img.uncache()
wm_mask_img.uncache()
gm_mask_img.uncache()
del gm_data, wm_data, vent_csf_in_dwi_data
gc.collect()
return tiss_classifier
def main():
parser = _build_args_parser()
args = parser.parse_args()
if args.verbose:
logging.basicConfig(level=logging.DEBUG)
assert_inputs_exist(parser, [args.sh_file, args.seed_file,
args.map_include_file,
args.map_exclude_file])
assert_outputs_exist(parser, args, args.output_file)
if not nib.streamlines.is_supported(args.output_file):
parser.error('Invalid output streamline file format (must be trk or ' +
'tck): {0}'.format(args.output_file))
if not args.min_length > 0:
parser.error('minL must be > 0, {}mm was provided.'
.format(args.min_length))
if args.max_length < args.min_length:
parser.error('maxL must be > than minL, (minL={}mm, maxL={}mm).'
.format(args.min_length, args.max_length))
if args.compress:
if args.compress < 0.001 or args.compress > 1:
logging.warning(
'You are using an error rate of {}.\nWe recommend setting it '
'between 0.001 and 1.\n0.001 will do almost nothing to the '
'tracts while 1 will higly compress/linearize the tracts'
.format(args.compress))
if args.particles <= 0:
parser.error('--particles must be >= 1.')
if args.back_tracking <= 0:
parser.error('PFT backtracking distance must be > 0.')
if args.forward_tracking <= 0:
parser.error('PFT forward tracking distance must be > 0.')
if args.npv and args.npv <= 0:
parser.error('Number of seeds per voxel must be > 0.')
if args.nt and args.nt <= 0:
parser.error('Total number of seeds must be > 0.')
fodf_sh_img = nib.load(args.sh_file)
if not np.allclose(np.mean(fodf_sh_img.header.get_zooms()[:3]),
fodf_sh_img.header.get_zooms()[0], atol=1.e-3):
parser.error(
'SH file is not isotropic. Tracking cannot be ran robustly.')
tracking_sphere = HemiSphere.from_sphere(get_sphere('repulsion724'))
# Check if sphere is unit, since we couldn't find such check in Dipy.
if not np.allclose(np.linalg.norm(tracking_sphere.vertices, axis=1), 1.):
raise RuntimeError('Tracking sphere should be unit normed.')
sh_basis = args.sh_basis
if args.algo == 'det':
dgklass = DeterministicMaximumDirectionGetter
else:
dgklass = ProbabilisticDirectionGetter
theta = get_theta(args.theta, args.algo)
# Reminder for the future:
# pmf_threshold == clip pmf under this
# relative_peak_threshold is for initial directions filtering
# min_separation_angle is the initial separation angle for peak extraction
dg = dgklass.from_shcoeff(
fodf_sh_img.get_fdata(dtype=np.double),
max_angle=theta,
sphere=tracking_sphere,
basis_type=sh_basis,
pmf_threshold=args.sf_threshold,
relative_peak_threshold=args.sf_threshold_init)
map_include_img = nib.load(args.map_include_file)
map_exclude_img = nib.load(args.map_exclude_file)
voxel_size = np.average(map_include_img.get_header()['pixdim'][1:4])
if not args.act:
tissue_classifier = CmcStoppingCriterion(map_include_img.get_fdata(),
map_exclude_img.get_fdata(),
step_size=args.step_size,
average_voxel_size=voxel_size)
else:
tissue_classifier = ActStoppingCriterion(map_include_img.get_fdata(),
map_exclude_img.get_fdata())
if args.npv:
nb_seeds = args.npv
seed_per_vox = True
elif args.nt:
nb_seeds = args.nt
seed_per_vox = False
else:
nb_seeds = 1
seed_per_vox = True
voxel_size = fodf_sh_img.header.get_zooms()[0]
vox_step_size = args.step_size / voxel_size
seed_img = nib.load(args.seed_file)
seeds = track_utils.random_seeds_from_mask(
seed_img.get_fdata(),
np.eye(4),
seeds_count=nb_seeds,
seed_count_per_voxel=seed_per_vox,
random_seed=args.seed)
# Note that max steps is used once for the forward pass, and
# once for the backwards. This doesn't, in fact, control the real
# max length
max_steps = int(args.max_length / args.step_size) + 1
pft_streamlines = ParticleFilteringTracking(
dg,
tissue_classifier,
seeds,
np.eye(4),
max_cross=1,
step_size=vox_step_size,
maxlen=max_steps,
pft_back_tracking_dist=args.back_tracking,
pft_front_tracking_dist=args.forward_tracking,
particle_count=args.particles,
return_all=args.keep_all,
random_seed=args.seed,
save_seeds=args.save_seeds)
scaled_min_length = args.min_length / voxel_size
scaled_max_length = args.max_length / voxel_size
if args.save_seeds:
filtered_streamlines, seeds = \
zip(*((s, p) for s, p in pft_streamlines
if scaled_min_length <= length(s) <= scaled_max_length))
data_per_streamlines = {'seeds': lambda: seeds}
else:
filtered_streamlines = \
(s for s in pft_streamlines
if scaled_min_length <= length(s) <= scaled_max_length)
data_per_streamlines = {}
if args.compress:
filtered_streamlines = (
compress_streamlines(s, args.compress)
for s in filtered_streamlines)
tractogram = LazyTractogram(lambda: filtered_streamlines,
data_per_streamlines,
affine_to_rasmm=seed_img.affine)
filetype = nib.streamlines.detect_format(args.output_file)
header = create_header_from_anat(seed_img, base_filetype=filetype)
# Use generator to save the streamlines on-the-fly
nib.streamlines.save(tractogram, args.output_file, header=header)
def prep_tissues(B0_mask,
gm_in_dwi,
vent_csf_in_dwi,
wm_in_dwi,
tiss_class,
cmc_step_size=0.2):
"""
Estimate a tissue classifier for tractography.
Parameters
----------
B0_mask : str
File path to B0 brain mask.
gm_in_dwi : str
File path to grey-matter tissue segmentation Nifti1Image.
vent_csf_in_dwi : str
File path to ventricular CSF tissue segmentation Nifti1Image.
wm_in_dwi : str
File path to white-matter tissue segmentation Nifti1Image.
tiss_class : str
Tissue classification method.
cmc_step_size : float
Step size from CMC tissue classification method.
Returns
-------
tiss_classifier : obj
Tissue classifier object.
"""
try:
import cPickle as pickle
except ImportError:
import _pickle as pickle
from dipy.tracking.stopping_criterion import ActStoppingCriterion, CmcStoppingCriterion, BinaryStoppingCriterion
# Loads mask and ensures it's a true binary mask
mask_img = nib.load(B0_mask)
# Load tissue maps and prepare tissue classifier
gm_mask_data = nib.load(gm_in_dwi).get_fdata()
wm_mask_data = nib.load(wm_in_dwi).get_fdata()
vent_csf_in_dwi_data = nib.load(vent_csf_in_dwi).get_fdata()
if tiss_class == 'act':
background = np.ones(mask_img.shape)
background[(gm_mask_data + wm_mask_data +
vent_csf_in_dwi_data) > 0] = 0
include_map = gm_mask_data
include_map[background > 0] = 1
tiss_classifier = ActStoppingCriterion(include_map,
vent_csf_in_dwi_data)
del background
del include_map
elif tiss_class == 'bin':
tiss_classifier = BinaryStoppingCriterion(wm_mask_data.astype('bool'))
elif tiss_class == 'cmc':
voxel_size = np.average(mask_img.header['pixdim'][1:4])
tiss_classifier = CmcStoppingCriterion.from_pve(
wm_mask_data,
gm_mask_data,
vent_csf_in_dwi_data,
step_size=cmc_step_size,
average_voxel_size=voxel_size)
elif tiss_class == 'wb':
tiss_classifier = BinaryStoppingCriterion(
mask_img.get_fdata().astype('bool'))
else:
raise ValueError('Tissue Classifier cannot be none.')
del gm_mask_data, wm_mask_data, vent_csf_in_dwi_data
mask_img.uncache()
return tiss_classifier
anatomical images to determine when the tractography stops.
Both stopping criterion use a trilinear interpolation
at the tracking position. CMC stopping criterion uses a probability derived
from the PVE maps to determine if the streamline reaches a 'valid' or 'invalid'
region. ACT uses a fixed threshold on the PVE maps. Both stopping criterion can
be used in conjunction with PFT. In this example, we used CMC.
"""
from dipy.tracking.stopping_criterion import CmcStoppingCriterion
voxel_size = np.average(img_pve_wm.header['pixdim'][1:4])
step_size = 0.2
cmc_criterion = CmcStoppingCriterion.from_pve(img_pve_wm.get_data(),
img_pve_gm.get_data(),
img_pve_csf.get_data(),
step_size=step_size,
average_voxel_size=voxel_size)
# Particle Filtering Tractography
pft_streamline_generator = ParticleFilteringTracking(dg,
cmc_criterion,
seeds,
affine,
max_cross=1,
step_size=step_size,
maxlen=1000,
pft_back_tracking_dist=2,
pft_front_tracking_dist=1,
particle_count=15,
return_all=False)
def prep_tissues(t1_mask, gm_in_dwi, vent_csf_in_dwi, wm_in_dwi, tiss_class, cmc_step_size=0.2):
"""
Estimate a tissue classifier for tractography.
Parameters
----------
t1_mask : str
File path to a T1w mask.
gm_in_dwi : str
File path to grey-matter tissue segmentation Nifti1Image.
vent_csf_in_dwi : str
File path to ventricular CSF tissue segmentation Nifti1Image.
wm_in_dwi : str
File path to white-matter tissue segmentation Nifti1Image.
tiss_class : str
Tissue classification method.
cmc_step_size : float
Step size from CMC tissue classification method.
Returns
-------
tiss_classifier : obj
Tissue classifier object.
References
----------
.. [1] Zhang, Y., Brady, M. and Smith, S. Segmentation of Brain MR Images
Through a Hidden Markov Random Field Model and the Expectation-Maximization
Algorithm IEEE Transactions on Medical Imaging, 20(1): 45-56, 2001
.. [2] Avants, B. B., Tustison, N. J., Wu, J., Cook, P. A. and Gee, J. C.
An open source multivariate framework for n-tissue segmentation with
evaluation on public data. Neuroinformatics, 9(4): 381-400, 2011.
"""
try:
import cPickle as pickle
except ImportError:
import _pickle as pickle
from dipy.tracking.stopping_criterion import ActStoppingCriterion, CmcStoppingCriterion, BinaryStoppingCriterion
from nilearn.masking import intersect_masks
from nilearn.image import math_img
# Loads mask
mask_img = nib.load(t1_mask)
# Load tissue maps and prepare tissue classifier
wm_img = nib.load(wm_in_dwi)
gm_img = nib.load(gm_in_dwi)
gm_mask_data = np.asarray(gm_img.dataobj)
wm_mask_data = np.asarray(wm_img.dataobj)
vent_csf_in_dwi_data = np.asarray(nib.load(vent_csf_in_dwi).dataobj)
if tiss_class == 'act':
background = np.ones(mask_img.shape)
background[(gm_mask_data + wm_mask_data + vent_csf_in_dwi_data) > 0] = 0
gm_mask_data[background > 0] = 1
tiss_classifier = ActStoppingCriterion(gm_mask_data, vent_csf_in_dwi_data)
del background
elif tiss_class == 'bin':
tiss_classifier = BinaryStoppingCriterion(np.asarray(intersect_masks([math_img('img > 0.0', img=mask_img),
math_img('img > 0.0', img=wm_img)],
threshold=1, connected=False).dataobj))
elif tiss_class == 'cmc':
voxel_size = np.average(mask_img.header['pixdim'][1:4])
tiss_classifier = CmcStoppingCriterion.from_pve(wm_mask_data, gm_mask_data, vent_csf_in_dwi_data,
step_size=cmc_step_size, average_voxel_size=voxel_size)
elif tiss_class == 'wb':
tiss_classifier = BinaryStoppingCriterion(np.asarray(mask_img.dataobj).astype('bool'))
else:
raise ValueError('Tissue classifier cannot be none.')
del gm_mask_data, wm_mask_data, vent_csf_in_dwi_data
mask_img.uncache()
gm_img.uncache()
wm_img.uncache()
return tiss_classifier
def prep_tissues(t1_mask,
gm_in_dwi,
vent_csf_in_dwi,
wm_in_dwi,
tiss_class,
cmc_step_size=0.2):
"""
Estimate a tissue classifier for tractography.
Parameters
----------
t1_mask : str
File path to a T1w mask.
gm_in_dwi : str
File path to grey-matter tissue segmentation Nifti1Image.
vent_csf_in_dwi : str
File path to ventricular CSF tissue segmentation Nifti1Image.
wm_in_dwi : str
File path to white-matter tissue segmentation Nifti1Image.
tiss_class : str
Tissue classification method.
cmc_step_size : float
Step size from CMC tissue classification method.
Returns
-------
tiss_classifier : obj
Tissue classifier object.
"""
try:
import cPickle as pickle
except ImportError:
import _pickle as pickle
from dipy.tracking.stopping_criterion import ActStoppingCriterion, CmcStoppingCriterion, BinaryStoppingCriterion
from nilearn.masking import intersect_masks
from nilearn.image import math_img
# Loads mask
mask_img = nib.load(t1_mask)
# Load tissue maps and prepare tissue classifier
wm_img = nib.load(wm_in_dwi)
gm_img = nib.load(gm_in_dwi)
gm_mask_data = np.asarray(gm_img.dataobj)
wm_mask_data = np.asarray(wm_img.dataobj)
vent_csf_in_dwi_data = np.asarray(nib.load(vent_csf_in_dwi).dataobj)
if tiss_class == 'act':
background = np.ones(mask_img.shape)
background[(gm_mask_data + wm_mask_data +
vent_csf_in_dwi_data) > 0] = 0
gm_mask_data[background > 0] = 1
tiss_classifier = ActStoppingCriterion(gm_mask_data,
vent_csf_in_dwi_data)
del background
elif tiss_class == 'bin':
tiss_classifier = BinaryStoppingCriterion(
np.asarray(
intersect_masks([
math_img('img > 0.0', img=mask_img),
math_img('img > 0.0', img=wm_img)
],
threshold=1,
connected=False).dataobj))
elif tiss_class == 'cmc':
voxel_size = np.average(mask_img.header['pixdim'][1:4])
tiss_classifier = CmcStoppingCriterion.from_pve(
wm_mask_data,
gm_mask_data,
vent_csf_in_dwi_data,
step_size=cmc_step_size,
average_voxel_size=voxel_size)
elif tiss_class == 'wb':
tiss_classifier = BinaryStoppingCriterion(
np.asarray(mask_img.dataobj).astype('bool'))
else:
raise ValueError('Tissue Classifier cannot be none.')
del gm_mask_data, wm_mask_data, vent_csf_in_dwi_data
mask_img.uncache()
gm_img.uncache()
wm_img.uncache()
return tiss_classifier
