def test_example(self):
ref = ants.image_read(ants.get_ants_data('r16'))
ref = ants.resample_image(ref, (50, 50), 1, 0)
ref = ants.iMath(ref, 'Normalize')
mi = ants.image_read(ants.get_ants_data('r27'))
mi2 = ants.image_read(ants.get_ants_data('r30'))
mi3 = ants.image_read(ants.get_ants_data('r62'))
mi4 = ants.image_read(ants.get_ants_data('r64'))
mi5 = ants.image_read(ants.get_ants_data('r85'))
refmask = ants.get_mask(ref)
refmask = ants.iMath(refmask, 'ME', 2) # just to speed things up
ilist = [mi, mi2, mi3, mi4, mi5]
seglist = [None] * len(ilist)
for i in range(len(ilist)):
ilist[i] = ants.iMath(ilist[i], 'Normalize')
mytx = ants.registration(fixed=ref,
moving=ilist[i],
typeofTransform=('Affine'))
mywarpedimage = ants.apply_transforms(
fixed=ref,
moving=ilist[i],
transformlist=mytx['fwdtransforms'])
ilist[i] = mywarpedimage
seg = ants.threshold_image(ilist[i], 'Otsu', 3)
seglist[i] = (seg) + ants.threshold_image(seg, 1, 3).morphology(
operation='dilate', radius=3)
r = 2
pp = ants.joint_label_fusion(ref,
refmask,
ilist,
r_search=2,
label_list=seglist,
rad=[r] * ref.dimension)
pp = ants.joint_label_fusion(ref, refmask, ilist, r_search=2, rad=2)
def runeachsess(subj,ses):
targ = os.path.join(derivatives,"sub-"+subj,"ses-"+ses,"anat") # find path to anat folder for this subject and this session
fpaths = glob.glob(targ+"/*T2w.nii.gz") # find the T1
fpaths = ''.join(fpaths)
if len(fpaths) > 0:
img = ants.image_read(fpaths)
mask = ants.get_mask(img)
dn = ants.denoise_image(img,mask,noise_model='Rician')
ants.image_write(dn,fpaths[0:-7]+'_dn.nii.gz',)
def test_example(self):
img = ants.image_read(ants.get_ants_data('r16'), 2)
img = ants.resample_image(img, (64, 64), 1, 0)
mask = ants.get_mask(img)
segs = ants.kmeans_segmentation(img, k=3, kmask=mask)
thick = ants.kelly_kapowski(s=segs['segmentation'],
g=segs['probabilityimages'][1],
w=segs['probabilityimages'][2],
its=45,
r=0.5,
m=1)
def test_decrop_image_example(self):
fi = ants.image_read(ants.get_ants_data('r16'))
mask = ants.get_mask(fi)
cropped = ants.crop_image(fi, mask, 1)
cropped = ants.smooth_image(cropped, 1)
decropped = ants.decrop_image(cropped, fi)
# image not float
cropped = ants.crop_image(fi.clone('unsigned int'), mask, 1)
# full image not float
cropped = ants.crop_image(fi, mask.clone('unsigned int'), 1)
def test_eig_seg_example(self):
mylist = [
ants.image_read(ants.get_ants_data('r16')),
ants.image_read(ants.get_ants_data('r27')),
ants.image_read(ants.get_ants_data('r85'))
]
myseg = ants.eig_seg(ants.get_mask(mylist[0]), mylist)
mylist = [
ants.image_read(ants.get_ants_data('r16')),
ants.image_read(ants.get_ants_data('r27')),
ants.image_read(ants.get_ants_data('r85'))
]
myseg = ants.eig_seg(ants.get_mask(mylist[0]), mylist, cthresh=2)
mylist = [
ants.image_read(ants.get_ants_data('r16')),
ants.image_read(ants.get_ants_data('r27')),
ants.image_read(ants.get_ants_data('r85'))
]
myseg = ants.eig_seg(ants.get_mask(mylist[0]),
mylist,
apply_segmentation_to_images=True)
def test_get_neighborhood_in_mask_example(self):
img = ants.image_read(ants.get_ants_data('r16'))
mask = ants.get_mask(img)
mat = ants.get_neighborhood_in_mask(img, mask, radius=(2, 2))
# image not ANTsImage
with self.assertRaises(Exception):
ants.get_neighborhood_in_mask(2, mask, radius=(2, 2))
# mask not ANTsImage
with self.assertRaises(Exception):
ants.get_neighborhood_in_mask(img, 2, radius=(2, 2))
# radius not right length
with self.assertRaises(Exception):
ants.get_neighborhood_in_mask(img, 2, radius=(2, 2, 2, 2))
# radius is just a float/int
mat = ants.get_neighborhood_in_mask(img, mask, radius=2)
# boundary condition == 'image'
mat = ants.get_neighborhood_in_mask(img,
mask,
radius=(2, 2),
boundary_condition='image')
# boundary condition == 'mean'
mat = ants.get_neighborhood_in_mask(img,
mask,
radius=(2, 2),
boundary_condition='mean')
# spatial info
mat = ants.get_neighborhood_in_mask(img,
mask,
radius=(2, 2),
spatial_info=True)
# get_gradient
mat = ants.get_neighborhood_in_mask(img,
mask,
radius=(2, 2),
get_gradient=True)
def test_multiple_inputs(self):
img = ants.image_read(ants.get_ants_data("r16"))
img = ants.resample_image(img, (64, 64), 1, 0)
mask = ants.get_mask(img)
segs1 = ants.atropos(a=img,
m='[0.2,1x1]',
c='[2,0]',
i='kmeans[3]',
x=mask)
# Use probabilities from k-means seg as priors
segs2 = ants.atropos(a=img,
m='[0.2,1x1]',
c='[2,0]',
i=segs1['probabilityimages'],
x=mask)
# multiple inputs
feats = [img, ants.iMath(img, "Laplacian"), ants.iMath(img, "Grad")]
segs3 = ants.atropos(a=feats,
m='[0.2,1x1]',
c='[2,0]',
i=segs1['probabilityimages'],
x=mask)
def test_get_mask_example(self):
image = ants.image_read(ants.get_ants_data('r16'))
mask = ants.get_mask(image)
def test_example(self):
# test ANTsPy/ANTsR example
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)
def test_morphology(self):
fi = ants.image_read(ants.get_ants_data("r16"))
mask = ants.get_mask(fi)
dilated_ball = ants.morphology(mask,
operation="dilate",
radius=3,
mtype="binary",
shape="ball")
eroded_box = ants.morphology(mask,
operation="erode",
radius=3,
mtype="binary",
shape="box")
opened_annulus = ants.morphology(
mask,
operation="open",
radius=5,
mtype="binary",
shape="annulus",
thickness=2,
)
## --- ##
mtype = "binary"
mask = mask.clone()
for operation in {"dilate", "erode", "open", "close"}:
for shape in {"ball", "box", "cross", "annulus", "polygon"}:
morph = ants.morphology(mask,
operation=operation,
radius=3,
mtype=mtype,
shape=shape)
self.assertTrue(
isinstance(morph, ants.core.ants_image.ANTsImage))
# invalid operation
with self.assertRaises(Exception):
ants.morphology(mask,
operation="invalid-operation",
radius=3,
mtype=mtype,
shape=shape)
mtype = "grayscale"
img = ants.image_read(ants.get_ants_data("r16"))
for operation in {"dilate", "erode", "open", "close"}:
morph = ants.morphology(img,
operation=operation,
radius=3,
mtype=mtype)
self.assertTrue(isinstance(morph, ants.core.ants_image.ANTsImage))
# invalid operation
with self.assertRaises(Exception):
ants.morphology(img,
operation="invalid-operation",
radius=3,
mtype="grayscale")
# invalid morphology type
with self.assertRaises(Exception):
ants.morphology(img,
operation="dilate",
radius=3,
mtype="invalid-morphology")
# invalid shape
with self.assertRaises(Exception):
ants.morphology(
mask,
operation="dilate",
radius=3,
mtype="binary",
shape="invalid-shape",
)
id = '2001' t1fn = rdir + 'data/LS' + id + "/unprocessed/3T/T1w_MPR1/LS"+id+"_3T_T1w_MPR1_gdc.nii.gz" # now the bold data boldfnsL = rdir + "data/LS2001/LS2001fmri/unprocessed/3T/rfMRI_REST1_LR/LS2001_3T_rfMRI_REST1_LR_gdc.nii.gz" boldfnsR = rdir + "data/LS2001/LS2001fmri/unprocessed/3T/rfMRI_REST1_RL/LS2001_3T_rfMRI_REST1_RL_gdc.nii.gz" # get the ref data reffns1 = rdir + 'data/LS2001/LS2001fmri/unprocessed/3T/rfMRI_REST1_LR/LS2001_3T_rfMRI_REST1_LR_SBRef_gdc.nii.gz' reffns2 = rdir + 'data/LS2001/LS2001fmri/unprocessed/3T/rfMRI_REST1_RL/LS2001_3T_rfMRI_REST1_RL_SBRef_gdc.nii.gz' ## Distortion correction (without a field map) i1 = ants.image_read( reffns1 ) i2 = ants.image_read( reffns2 ) und = ants.build_template( i1, ( i1, i2 ), 3, gradient_step = 0.5 ) t1 = ants.image_read( t1fn ).n3_bias_field_correction( 8 ).n3_bias_field_correction( 4 ) bmask = ants.get_mask( und, low_thresh = und.mean() * 0.75, high_thresh=1e9, cleanup = 3 ).iMath_fill_holes() # ants.plot( und, bmask, axis=2, overlay_alpha = 0.33 ) # this is a fragile approach - not really recommended - but it is quick t1mask = ants.get_mask( t1, low_thresh = t1.mean() * 1.1, high_thresh=1e9, cleanup = 5 ).iMath_fill_holes() # ants.plot( t1, t1mask, axis=2, overlay_alpha = 0.33 ) t1rig = ants.registration( und * bmask, t1 * t1mask, "BOLDRigid" ) t1reg = ants.registration( und * bmask, t1 * t1mask, "SyNOnly", initialTransform = t1rig['fwdtransforms'], synMetric = 'CC', synSampling = 2, regIterations = (5) ) ########################### # The distortion to the T1 is greatly reduced. # Brain masking # Use the BOLD mask to extract the brain from the t1
def test_morphology(self):
fi = ants.image_read(ants.get_ants_data('r16'))
mask = ants.get_mask(fi)
dilated_ball = ants.morphology(mask,
operation='dilate',
radius=3,
mtype='binary',
shape='ball')
eroded_box = ants.morphology(mask,
operation='erode',
radius=3,
mtype='binary',
shape='box')
opened_annulus = ants.morphology(mask,
operation='open',
radius=5,
mtype='binary',
shape='annulus',
thickness=2)
## --- ##
mtype = 'binary'
mask = mask.clone()
for operation in {'dilate', 'erode', 'open', 'close'}:
for shape in {'ball', 'box', 'cross', 'annulus', 'polygon'}:
morph = ants.morphology(mask,
operation=operation,
radius=3,
mtype=mtype,
shape=shape)
self.assertTrue(
isinstance(morph, ants.core.ants_image.ANTsImage))
# invalid operation
with self.assertRaises(Exception):
ants.morphology(mask,
operation='invalid-operation',
radius=3,
mtype=mtype,
shape=shape)
mtype = 'grayscale'
img = ants.image_read(ants.get_ants_data('r16'))
for operation in {'dilate', 'erode', 'open', 'close'}:
morph = ants.morphology(img,
operation=operation,
radius=3,
mtype=mtype)
self.assertTrue(isinstance(morph, ants.core.ants_image.ANTsImage))
# invalid operation
with self.assertRaises(Exception):
ants.morphology(img,
operation='invalid-operation',
radius=3,
mtype='grayscale')
#invalid morphology type
with self.assertRaises(Exception):
ants.morphology(img,
operation='dilate',
radius=3,
mtype='invalid-morphology')
# invalid shape
with self.assertRaises(Exception):
ants.morphology(mask,
operation='dilate',
radius=3,
mtype='binary',
shape='invalid-shape')
def test_mask_image_example(self):
myimage = ants.image_read(ants.get_ants_data('r16'))
mask = ants.get_mask(myimage)
myimage_mask = ants.mask_image(myimage, mask, 3)
seg = ants.kmeans_segmentation(myimage, 3)
myimage_mask = ants.mask_image(myimage, seg['segmentation'], (1, 3))
def test_labels_to_matrix_example(self):
fi = ants.image_read(ants.get_ants_data('r16')).resample_image(
(60, 60), 1, 0)
mask = ants.get_mask(fi)
labs = ants.kmeans_segmentation(fi, 3)['segmentation']
labmat = ants.labels_to_matrix(labs, mask)
from joblib import Parallel, delayed
import multiprocessing
derivatives = '/Volumes/data/prebiostress/data/derivatives'
layout = BIDSLayout(derivatives) # ignore the error here
# do T1
for subj in layout.get_subjects(): # loop on number of subjects
print(subj)
for ses in layout.get_sessions(): # loop on number of sessions
targ = os.path.join(derivatives,"sub-"+subj,"ses-"+ses,"anat") # find path to anat folder for this subject and this session
fpaths = glob.glob(targ+"/*T1w.nii.gz") # find the T1
fpaths = ''.join(fpaths)
if len(fpaths) > 0:
img = ants.image_read(fpaths)
mask = ants.get_mask(img)
dn = ants.denoise_image(img,mask,noise_model='Gaussian')
ants.image_write(dn,fpaths[0:-7]+'_dn.nii.gz',)
# do T2
def runeachsess(subj,ses):
targ = os.path.join(derivatives,"sub-"+subj,"ses-"+ses,"anat") # find path to anat folder for this subject and this session
fpaths = glob.glob(targ+"/*T2w.nii.gz") # find the T1
fpaths = ''.join(fpaths)
if len(fpaths) > 0:
img = ants.image_read(fpaths)
mask = ants.get_mask(img)
dn = ants.denoise_image(img,mask,noise_model='Rician')
ants.image_write(dn,fpaths[0:-7]+'_dn.nii.gz',)
for subj in layout.get_subjects():
def __init__(self, ants_image=[[0.0]], **options):
import ants
self.mask = ants.get_mask(ants_image, **options)
def get_mask(self):
mask = ants.get_mask(self.get_brain())
mask = ants.iMath(mask, 'ME', 2)
return mask
link = '/ROBEX_Output/'
# Skull stripped data output by ROBEX
mylist = os.listdir('/ROBEX_Output/')
# fixed image is the template to which the registration will be fitted found in the downloaded ANTs package
fixed = ants.image_read('/ANTsPy-master/data/mni.nii.gz')
# This runs the entire dataset through registration
for i in mylist:
#registraction
moving = ants.image_read('/arc/project/ex-rtam-1/BrainExt_AllData/' + i)
mytx = ants.registration(fixed=fixed,
moving=moving,
type_of_transform='SyNAggro')
mywarpedimage = ants.apply_transforms(fixed=fixed,
moving=moving,
transformlist=mytx['fwdtransforms'])
mywarpedimage.to_file('/Output_ants_SyNAggro/Registration/' + i)
#segmentation
mask = ants.get_mask(mywarpedimage)
img_seg = ants.atropos(a=mywarpedimage,
m='[0.2,1x1x1]',
c='[2,0]',
i='kmeans[3]',
x=mask)
gm = img_seg['probabilityimages'][1]
gm.to_file('/Output_ants_SyNAggro/Greymatter/' + i)
continue
def test_mask_image_example(self):
myimage = ants.image_read(ants.get_ants_data("r16"))
mask = ants.get_mask(myimage)
myimage_mask = ants.mask_image(myimage, mask, (2, 3))
seg = ants.kmeans_segmentation(myimage, 3)
myimage_mask = ants.mask_image(myimage, seg["segmentation"], (1, 3))
