def computeJacard(aname, bname):
'''
computeJacard('warpedDiff_IBSR_15_segTRI_fill_ana_IBSR_10_ana_strip.nii.gz', '/opt/registration/data/t1/IBSR18/IBSR_10/IBSR_10_segTRI_fill_ana.nii.gz' )
computeJacard('warpedDiff_IBSR_13_segTRI_fill_ana_IBSR_10_ana_strip.nii.gz', '/opt/registration/data/t1/IBSR18/IBSR_10/IBSR_10_segTRI_fill_ana.nii.gz' )
computeJacard('warpedAffine_IBSR_16_segTRI_fill_ana_IBSR_10_ana_strip.nii.gz', '/opt/registration/data/t1/IBSR18/IBSR_10/IBSR_10_segTRI_fill_ana.nii.gz')
computeJacard('warpedAffine_IBSR_10_segTRI_fill_ana_IBSR_01_ana_strip.nii.gz', '/opt/registration/data/t1/IBSR18/IBSR_10/IBSR_10_seg_ana.nii.gz',None)
computeJacard('warpedDiff_IBSR_01_segTRI_fill_ana_t1_icbm_normal_1mm_pn0_rf0_peeled.nii.gz', 'data/phantom_1.0mm_normal_crisp.rawb.nii.gz')
'''
baseA=rcommon.getBaseFileName(aname)
baseB=rcommon.getBaseFileName(bname)
oname="jacard_"+baseA+"_"+baseB+".txt"
if(os.path.exists(oname)):
print 'Jacard overlap found. Skipped computation.'
jacard=np.loadtxt(oname)
return jacard
nib_A=nib.load(aname)
affineA=nib_A.get_affine()
A=nib_A.get_data().squeeze().astype(np.int32)
A=np.copy(A, order='C')
print "A range:",A.min(), A.max()
nib_B=nib.load(bname)
newB=nib.Nifti1Image(nib_B.get_data(),affineA)
newB.to_filename(bname)
B=nib_B.get_data().squeeze().astype(np.int32)
B=np.copy(B, order='C')
print "B range:",B.min(), B.max()
nlabels=1+np.max([A.max(), B.max()])
jacard=np.array(tf.compute_jacard(A,B, nlabels))
print "Jacard range:",jacard.min(), jacard.max()
np.savetxt(oname,jacard)
return jacard
def fullJacardAllPairs(names, segIndex, warpedPreffix):
nlines=len(names)
sumJacard=None
sumJacard2=None
minScore=None
worstPair=None
nsamples=0.0
for i in range(nlines):
if not names[i]:
continue
registrationReference=names[i][0]
reference=names[i][segIndex]
for j in range(nlines):
if i==j:
continue
if not names[j]:
continue
target=names[j][segIndex]
###############
baseReference=rcommon.getBaseFileName(registrationReference)
baseTarget=rcommon.getBaseFileName(target)
warpedName=warpedPreffix+baseTarget+'_'+baseReference+'.nii.gz'
jacard=computeJacard(reference, warpedName)
nsamples+=1
if sumJacard==None:
sumJacard=jacard
sumJacard2=jacard**2
worstPair=(i,j)
minScore=np.sum(jacard)
else:
lenOld=len(sumJacard)
lenNew=len(jacard)
extendedShape=(np.max([lenOld, lenNew]),)
newSum=np.zeros(shape=extendedShape, dtype=np.float64)
newSum2=np.zeros(shape=extendedShape, dtype=np.float64)
newSum[:lenOld]=sumJacard[...]
newSum[:lenNew]+=jacard[...]
newSum2[:lenOld]=sumJacard2[...]
newSum2[:lenNew]+=jacard[...]**2
sumJacard=newSum
sumJacard2=newSum2
optSum=np.sum(jacard)
if optSum<minScore:
minScore=optSum
worstPair=(i,j)
meanJacard=sumJacard/nsamples
variance=sumJacard2/nsamples-meanJacard**2#E[X^2] - E[X]^2
std=np.sqrt(variance)
return meanJacard, std, worstPair, minScore
def warpANTSAffine(targetName, referenceName, affineName, oname, interpolationType='trilinear'):
baseName=rcommon.getBaseFileName(targetName)
nib_target=nib.load(targetName)
nib_reference=nib.load(referenceName)
M=nib_target.get_affine()
F=nib_reference.get_affine()
referenceShape=np.array(nib_reference.shape, dtype=np.int32)
######Load and compose affine#####
if not affineName:
T=np.eye(4)
else:
T=rcommon.readAntsAffine(affineName)
affineComposition=np.linalg.inv(M).dot(T.dot(F))
######################
if interpolationType=='NN':
target=nib_target.get_data().squeeze().astype(np.int32)
target=np.copy(target, order='C')
warped=np.array(tf.warp_discrete_volumeNNAffine(target, referenceShape, affineComposition)).astype(np.int16)
else:
target=nib_target.get_data().squeeze().astype(np.float64)
target=np.copy(target, order='C')
warped=np.array(tf.warp_volume_affine(target, referenceShape, affineComposition)).astype(np.int16)
warped=nib.Nifti1Image(warped, F)
if not oname:
oname="warped"+baseName+"nii.gz"
warped.to_filename(oname)
def save_registration_results(init_affine, displacement, inverse, params):
r'''
Warp the target image using the obtained deformation field
'''
fixed = nib.load(params.reference)
fixed_affine = fixed.get_affine()
reference_shape = np.array(fixed.shape, dtype=np.int32)
warp_dir = params.warp_dir
base_moving = rcommon.getBaseFileName(params.target)
base_fixed = rcommon.getBaseFileName(params.reference)
moving = nib.load(params.target).get_data().squeeze().astype(np.float64)
moving = moving.copy(order='C')
warped = np.array(tf.warp_volume(moving, displacement)).astype(np.int16)
img_warped = nib.Nifti1Image(warped, fixed_affine)
img_warped.to_filename('warpedDiff_'+base_moving+'_'+base_fixed+'.nii.gz')
#---warp the target image using the affine transformation only---
moving = nib.load(params.target).get_data().squeeze().astype(np.float64)
moving = moving.copy(order='C')
warped = np.array(
tf.warp_volume_affine(moving, reference_shape, init_affine)
).astype(np.int16)
img_warped = nib.Nifti1Image(warped, fixed_affine)
img_warped.to_filename('warpedAffine_'+base_moving+'_'+base_fixed+'.nii.gz')
#---warp all volumes in the warp directory using NN interpolation
names = [os.path.join(warp_dir, name) for name in os.listdir(warp_dir)]
for name in names:
to_warp = nib.load(name).get_data().squeeze().astype(np.int32)
to_warp = to_warp.copy(order='C')
base_warp = rcommon.getBaseFileName(name)
warped = np.array(
tf.warp_discrete_volumeNN(to_warp, displacement)).astype(np.int16)
img_warped = nib.Nifti1Image(warped, fixed_affine)
img_warped.to_filename('warpedDiff_'+base_warp+'_'+base_fixed+'.nii.gz')
#---finally, the optional output
if params.output_list == None:
return
if 'lattice' in params.output_list:
save_deformed_lattice_3d(
displacement,
'latticeDispDiff_'+base_moving+'_'+base_fixed+'.nii.gz')
if 'inv_lattice' in params.output_list:
save_deformed_lattice_3d(
inverse, 'invLatticeDispDiff_'+base_moving+'_'+base_fixed+'.nii.gz')
if 'displacement' in params.output_list:
np.save('dispDiff_'+base_moving+'_'+base_fixed+'.npy', displacement)
if 'inverse' in params.output_list:
np.save('invDispDiff_'+base_moving+'_'+base_fixed+'.npy', inverse)
def changeExtension(fname, newExt):
'''
changeExtension('/opt/registration/data/myfile.nii.gz', '.ext')
changeExtension('/opt/registration/data/myfile.nii.gz', '_suffix.ext')
'''
directory=os.path.dirname(fname)
if directory:
directory+='/'
basename=rcommon.getBaseFileName(fname)
return directory+basename+newExt
def warpNonlinear(targetName, referenceName, dispName, oname, interpolationType='trilinear'):
baseName=rcommon.getBaseFileName(targetName)
displacement=np.load(dispName)
nib_target = nib.load(targetName)
if interpolationType=='NN':
target=nib_target.get_data().squeeze().astype(np.int32)
target=np.copy(target, order='C')
warped=np.array(tf.warp_discrete_volumeNN(target, displacement))
else:
target=nib_target.get_data().squeeze().astype(np.float64)
target=np.copy(target, order='C')
warped=np.array(tf.warp_volume(target, displacement))
referenceAffine=nib.load(referenceName).get_affine()
warped=nib.Nifti1Image(warped, referenceAffine)
if not oname:
oname="warped"+baseName+"nii.gz"
warped.to_filename(oname)
def testEstimateMultimodalSyN3DMultiScale(fnameMoving, fnameFixed, fnameAffine,
warpDir, lambdaParam):
'''
testEstimateMultimodalDiffeomorphicField3DMultiScale('IBSR_01_ana_strip.nii.gz', 't1_icbm_normal_1mm_pn0_rf0_peeled.nii.gz', 'IBSR_01_ana_strip_t1_icbm_normal_1mm_pn0_rf0_peeledAffine.txt', 100)
'''
print 'Registering', fnameMoving, 'to', fnameFixed, 'with lambda=', lambdaParam
sys.stdout.flush()
moving = nib.load(fnameMoving)
fixed = nib.load(fnameFixed)
referenceShape = np.array(fixed.shape, dtype=np.int32)
M = moving.get_affine()
F = fixed.get_affine()
if not fnameAffine:
T = np.eye(4)
else:
T = rcommon.readAntsAffine(fnameAffine)
initAffine = np.linalg.inv(M).dot(T.dot(F))
print initAffine
moving = moving.get_data().squeeze().astype(np.float64)
fixed = fixed.get_data().squeeze().astype(np.float64)
moving = np.copy(moving, order='C')
fixed = np.copy(fixed, order='C')
moving = (moving - moving.min()) / (moving.max() - moving.min())
fixed = (fixed - fixed.min()) / (fixed.max() - fixed.min())
level = 2
maskMoving = moving > 0
maskFixed = fixed > 0
movingPyramid = [
img for img in rcommon.pyramid_gaussian_3D(moving, level, maskMoving)
]
fixedPyramid = [
img for img in rcommon.pyramid_gaussian_3D(fixed, level, maskFixed)
]
#maxOuterIter=[25,50,100,100, 100, 100]
maxOuterIter = [2, 2, 2, 2, 2, 2]
baseMoving = rcommon.getBaseFileName(fnameMoving)
baseFixed = rcommon.getBaseFileName(fnameFixed)
# if(os.path.exists('disp_'+baseMoving+'_'+baseFixed+'.npy')):
# displacement=np.load('disp_'+baseMoving+'_'+baseFixed+'.npy')
# else:
displacement, directInverse = estimateMultimodalSyN3DMultiScale(
movingPyramid, fixedPyramid, initAffine, lambdaParam, maxOuterIter, 0)
tf.prepend_affine_to_displacement_field(displacement, initAffine)
# np.save('disp_'+baseMoving+'_'+baseFixed+'.npy', displacement)
#####Warp all requested volumes
#---first the target using tri-linear interpolation---
moving = nib.load(fnameMoving).get_data().squeeze().astype(np.float64)
moving = np.copy(moving, order='C')
warped = np.array(tf.warp_volume(moving, displacement)).astype(np.int16)
imgWarped = nib.Nifti1Image(warped, F)
imgWarped.to_filename('warpedDiff_' + baseMoving + '_' + baseFixed +
'.nii.gz')
#---warp using affine only
moving = nib.load(fnameMoving).get_data().squeeze().astype(np.int32)
moving = np.copy(moving, order='C')
warped = np.array(
tf.warp_discrete_volumeNNAffine(moving, referenceShape,
initAffine)).astype(np.int16)
imgWarped = nib.Nifti1Image(
warped, F) #The affine transformation is the reference's one
imgWarped.to_filename('warpedAffine_' + baseMoving + '_' + baseFixed +
'.nii.gz')
#---now the rest of the targets using nearest neighbor
names = [os.path.join(warpDir, name) for name in os.listdir(warpDir)]
for name in names:
#---warp using the non-linear deformation
toWarp = nib.load(name).get_data().squeeze().astype(np.int32)
toWarp = np.copy(toWarp, order='C')
baseWarp = rcommon.getBaseFileName(name)
warped = np.array(tf.warp_discrete_volumeNN(
toWarp, displacement)).astype(np.int16)
imgWarped = nib.Nifti1Image(
warped, F) #The affine transformation is the reference's one
imgWarped.to_filename('warpedDiff_' + baseWarp + '_' + baseFixed +
'.nii.gz')
#---warp using affine inly
warped = np.array(
tf.warp_discrete_volumeNNAffine(toWarp, referenceShape,
initAffine)).astype(np.int16)
imgWarped = nib.Nifti1Image(
warped, F) #The affine transformation is the reference's one
imgWarped.to_filename('warpedAffine_' + baseWarp + '_' + baseFixed +
'.nii.gz')
#---finally, the deformed lattices (forward, inverse and resdidual)---
lambdaParam = 0.9
maxIter = 100
tolerance = 1e-4
print 'Computing inverse...'
inverse = np.array(
tf.invert_vector_field3D(displacement, lambdaParam, maxIter,
tolerance))
residual = np.array(tf.compose_vector_fields3D(displacement, inverse))
saveDeformedLattice3D(
displacement,
'latticeDispDiff_' + baseMoving + '_' + baseFixed + '.nii.gz')
saveDeformedLattice3D(
inverse, 'latticeInvDiff_' + baseMoving + '_' + baseFixed + '.nii.gz')
saveDeformedLattice3D(
residual, 'latticeResdiff_' + baseMoving + '_' + baseFixed + '.nii.gz')
residual = np.sqrt(np.sum(residual**2, 3))
print "Mean residual norm:", residual.mean(), " (", residual.std(
), "). Max residual norm:", residual.max()
def testEstimateMultimodalSyN3DMultiScale(fnameMoving, fnameFixed, fnameAffine, warpDir, lambdaParam):
'''
testEstimateMultimodalDiffeomorphicField3DMultiScale('IBSR_01_ana_strip.nii.gz', 't1_icbm_normal_1mm_pn0_rf0_peeled.nii.gz', 'IBSR_01_ana_strip_t1_icbm_normal_1mm_pn0_rf0_peeledAffine.txt', 100)
'''
print 'Registering', fnameMoving, 'to', fnameFixed,'with lambda=',lambdaParam
sys.stdout.flush()
moving = nib.load(fnameMoving)
fixed= nib.load(fnameFixed)
referenceShape=np.array(fixed.shape, dtype=np.int32)
M=moving.get_affine()
F=fixed.get_affine()
if not fnameAffine:
T=np.eye(4)
else:
T=rcommon.readAntsAffine(fnameAffine)
initAffine=np.linalg.inv(M).dot(T.dot(F))
print initAffine
moving=moving.get_data().squeeze().astype(np.float64)
fixed=fixed.get_data().squeeze().astype(np.float64)
moving=np.copy(moving, order='C')
fixed=np.copy(fixed, order='C')
moving=(moving-moving.min())/(moving.max()-moving.min())
fixed=(fixed-fixed.min())/(fixed.max()-fixed.min())
level=2
maskMoving=moving>0
maskFixed=fixed>0
movingPyramid=[img for img in rcommon.pyramid_gaussian_3D(moving, level, maskMoving)]
fixedPyramid=[img for img in rcommon.pyramid_gaussian_3D(fixed, level, maskFixed)]
#maxOuterIter=[25,50,100,100, 100, 100]
maxOuterIter=[2,2,2,2,2,2]
baseMoving=rcommon.getBaseFileName(fnameMoving)
baseFixed=rcommon.getBaseFileName(fnameFixed)
# if(os.path.exists('disp_'+baseMoving+'_'+baseFixed+'.npy')):
# displacement=np.load('disp_'+baseMoving+'_'+baseFixed+'.npy')
# else:
displacement, directInverse=estimateMultimodalSyN3DMultiScale(movingPyramid, fixedPyramid, initAffine, lambdaParam, maxOuterIter, 0)
tf.prepend_affine_to_displacement_field(displacement, initAffine)
# np.save('disp_'+baseMoving+'_'+baseFixed+'.npy', displacement)
#####Warp all requested volumes
#---first the target using tri-linear interpolation---
moving=nib.load(fnameMoving).get_data().squeeze().astype(np.float64)
moving=np.copy(moving, order='C')
warped=np.array(tf.warp_volume(moving, displacement)).astype(np.int16)
imgWarped=nib.Nifti1Image(warped, F)
imgWarped.to_filename('warpedDiff_'+baseMoving+'_'+baseFixed+'.nii.gz')
#---warp using affine only
moving=nib.load(fnameMoving).get_data().squeeze().astype(np.int32)
moving=np.copy(moving, order='C')
warped=np.array(tf.warp_discrete_volumeNNAffine(moving, referenceShape, initAffine)).astype(np.int16)
imgWarped=nib.Nifti1Image(warped, F)#The affine transformation is the reference's one
imgWarped.to_filename('warpedAffine_'+baseMoving+'_'+baseFixed+'.nii.gz')
#---now the rest of the targets using nearest neighbor
names=[os.path.join(warpDir,name) for name in os.listdir(warpDir)]
for name in names:
#---warp using the non-linear deformation
toWarp=nib.load(name).get_data().squeeze().astype(np.int32)
toWarp=np.copy(toWarp, order='C')
baseWarp=rcommon.getBaseFileName(name)
warped=np.array(tf.warp_discrete_volumeNN(toWarp, displacement)).astype(np.int16)
imgWarped=nib.Nifti1Image(warped, F)#The affine transformation is the reference's one
imgWarped.to_filename('warpedDiff_'+baseWarp+'_'+baseFixed+'.nii.gz')
#---warp using affine inly
warped=np.array(tf.warp_discrete_volumeNNAffine(toWarp, referenceShape, initAffine)).astype(np.int16)
imgWarped=nib.Nifti1Image(warped, F)#The affine transformation is the reference's one
imgWarped.to_filename('warpedAffine_'+baseWarp+'_'+baseFixed+'.nii.gz')
#---finally, the deformed lattices (forward, inverse and resdidual)---
lambdaParam=0.9
maxIter=100
tolerance=1e-4
print 'Computing inverse...'
inverse=np.array(tf.invert_vector_field3D(displacement, lambdaParam, maxIter, tolerance))
residual=np.array(tf.compose_vector_fields3D(displacement, inverse))
saveDeformedLattice3D(displacement, 'latticeDispDiff_'+baseMoving+'_'+baseFixed+'.nii.gz')
saveDeformedLattice3D(inverse, 'latticeInvDiff_'+baseMoving+'_'+baseFixed+'.nii.gz')
saveDeformedLattice3D(residual, 'latticeResdiff_'+baseMoving+'_'+baseFixed+'.nii.gz')
residual=np.sqrt(np.sum(residual**2,3))
print "Mean residual norm:", residual.mean()," (",residual.std(), "). Max residual norm:", residual.max()
parser.add_argument('--similarity', action='store', metavar='String',
help="Cost-function for assessing image similarity. If a string, one of 'cc': correlation coefficient, 'cr': correlation ratio, 'crl1': L1-norm based correlation ratio, 'mi': mutual information, 'nmi': normalized mutual information, 'slr': supervised log-likelihood ratio. If a callable, it should take a two-dimensional array representing the image joint histogram as an input and return a float.",
default='crl1')
parser.add_argument('--interp', action='store', metavar='String',
help="'Interpolation method.One of 'pv': Partial volume, 'tri':Trilinear, 'rand': Random interpolation'",
default='pv')
params = parser.parse_args()
if __name__ == '__main__':
fmoving = params.in_file
fstatic = params.reference
baseFixed=baseFixed=rcommon.getBaseFileName(fstatic)
print(fmoving + ' --> ' + fstatic)
static=nib.load(fstatic)
static=nib.Nifti1Image(static.get_data().squeeze(), static.get_affine())
static = nifti2nipy(static)
moving=nib.load(fmoving)
moving=nib.Nifti1Image(moving.get_data().squeeze(), moving.get_affine())
moving= nifti2nipy(moving)
similarity = params.similarity #'crl1' 'cc', 'mi', 'nmi', 'cr', 'slr'
interp = params.interp #'pv', 'tri',
renormalize = True
optimizer = 'powell'
print('Setting up registration...')
default='crl1')
parser.add_argument(
'--interp',
action='store',
metavar='String',
help=
"'Interpolation method.One of 'pv': Partial volume, 'tri':Trilinear, 'rand': Random interpolation'",
default='pv')
params = parser.parse_args()
if __name__ == '__main__':
fmoving = params.in_file
fstatic = params.reference
baseFixed = baseFixed = rcommon.getBaseFileName(fstatic)
print(fmoving + ' --> ' + fstatic)
static = nib.load(fstatic)
static = nib.Nifti1Image(static.get_data().squeeze(), static.get_affine())
static = nifti2nipy(static)
moving = nib.load(fmoving)
moving = nib.Nifti1Image(moving.get_data().squeeze(), moving.get_affine())
moving = nifti2nipy(moving)
similarity = params.similarity #'crl1' 'cc', 'mi', 'nmi', 'cr', 'slr'
interp = params.interp #'pv', 'tri',
renormalize = True
optimizer = 'powell'
print('Setting up registration...')
