"""
Atropos segmentation
"""
__all__ = ['atropos']
import os
import glob
import warnings
from tempfile import mktemp
from ..core import ants_image_io as iio2
from .. import utils
[docs]def atropos(a, x, i='Kmeans[3]', m='[0.2,1x1]', c='[5,0]',
priorweight=0.25, **kwargs):
"""
A finite mixture modeling (FMM) segmentation approach with possibilities
for specifying prior constraints. These prior constraints include the
specification of a prior label image, prior probability images (one for
each class), and/or an MRF prior to enforce spatial smoothing of the
labels. Similar algorithms include FAST and SPM. atropos can also perform
multivariate segmentation if you pass a list of images in: e.g. a=(img1,img2).
ANTsR function: `atropos`
Arguments
---------
a : ANTsImage or list/tuple of ANTsImage types
One or more scalar images to segment. If priors are not used,
the intensities of the first image are used to order the classes
in the segmentation output, from lowest to highest intensity. Otherwise
the order of the classes is dictated by the order of the prior images.
x : ANTsImage
mask image.
i : string
initialization usually KMeans[N] for N classes or a list of N prior
probability images. See Atropos in ANTs for full set of options.
m : string
mrf parameters as a string, usually "[smoothingFactor,radius]" where
smoothingFactor determines the amount of smoothing and radius determines
the MRF neighborhood, as an ANTs style neighborhood vector eg "1x1x1"
for a 3D image. The radius must match the dimensionality of the image,
eg 1x1 for 2D and The default in ANTs is smoothingFactor=0.3 and
radius=1. See Atropos for more options.
c : string
convergence parameters, "[numberOfIterations,convergenceThreshold]".
A threshold of 0 runs the full numberOfIterations, otherwise Atropos
tests convergence by comparing the mean maximum posterior probability
over the whole region of interest defined by the mask x.
priorweight : scalar
usually 0 (priors used for initialization only), 0.25 or 0.5.
kwargs : keyword arguments
more parameters, see Atropos help in ANTs
Returns
-------
dictionary with the following key/value pairs:
`segmentation`: ANTsImage
actually segmented image
`probabilityimages` : list of ANTsImage types
one image for each segmentation class
Example
-------
>>> import ants
>>> img = ants.image_read(ants.get_ants_data('r16'))
>>> img = ants.resample_image(img, (64,64), 1, 0)
>>> mask = ants.get_mask(img)
>>> ants.atropos( a = img, m = '[0.2,1x1]', c = '[2,0]', i = 'kmeans[3]', x = mask )
>>> seg2 = ants.atropos( a = img, m = '[0.2,1x1]', c = '[2,0]', i = seg['probabilityimages'], x = mask, priorweight=0.25 )
"""
probs = mktemp(prefix='antsr', suffix='prob%02d.nii.gz')
tdir = probs.replace(os.path.basename(probs),'')
probsbase = os.path.basename(probs)
searchpattern = probsbase.replace('%02d', '*')
ct = 0
if isinstance(i, (list,tuple)) and (len(i) > 1):
while ct < len(i):
probchar = str(ct+1)
if ct < 9:
probchar = '0%s' % probchar
tempfn = probs.replace('%02d', probchar)
iio2.image_write(i[ct], tempfn)
ct += 1
i = 'PriorProbabilityImages[%s,%s,%s]' % (str(len(i)), probs, str(priorweight))
if isinstance(a, (list,tuple)):
outimg = a[0].clone('unsigned int')
else:
outimg = a.clone('unsigned int')
mydim = outimg.dimension
outs = '[%s,%s]' % (utils._ptrstr(outimg.pointer), probs)
mymask = x.clone('unsigned int')
if (not isinstance(a, (list,tuple))) or (len(a) == 1):
myargs = {
'd': mydim,
'a': a,
'm-MULTINAME-0': m,
'o': outs,
'c': c,
'm-MULTINAME-1': m,
'i': i,
'x': mymask
}
for k, v in kwargs.items():
myargs[k] = v
elif isinstance(a, (list, tuple)):
if len(a) > 6:
print('more than 6 input images not really supported, using first 6')
a = a[:6]
myargs = {
'd': mydim,
'm-MULTINAME-0': m,
'o': outs,
'c': c,
'm-MULTINAME-1': m,
'i': i,
'x': mymask
}
for aa_idx, aa in enumerate(a):
myargs['a-MULTINAME-%i'%aa_idx] = aa
processed_args = utils._int_antsProcessArguments(myargs)
libfn = utils.get_lib_fn('Atropos')
retval = libfn(processed_args)
if retval != 0:
raise Exception(f"Atropos exited with non-zero status {retval}. Run with verbose=1 to see error messages")
probsout = glob.glob(os.path.join(tdir,'*'+searchpattern))
if probsout is None or len(probsout) == 0:
raise Exception('No Atropos output probability images found. Run with verbose=1 to see error messages')
probsout.sort()
probimgs = [iio2.image_read(probsout[0])]
for idx in range(1, len(probsout)):
probimgs.append(iio2.image_read(probsout[idx]))
outimg = outimg.clone('float')
return {'segmentation': outimg,
'probabilityimages': probimgs}
