def setUp(self):
img2d = ants.image_read(ants.get_ants_data('r16'))
img3d = ants.image_read(ants.get_ants_data('mni'))
tx2d = ants.create_ants_transform(precision='float', dimension=2)
tx3d = ants.create_ants_transform(precision='float', dimension=3)
self.imgs = [img2d, img3d]
self.txs = [tx2d, tx3d]
self.pixeltypes = ['unsigned char', 'unsigned int', 'float']
def setUp(self):
img2d = ants.image_read(ants.get_ants_data('r16'))
img3d = ants.image_read(ants.get_ants_data('mni'))
tx2d = ants.create_ants_transform(precision='float', dimension=2)
tx3d = ants.create_ants_transform(precision='float', dimension=3)
self.imgs = [img2d, img3d]
self.txs = [tx2d, tx3d]
self.pixeltypes = ['unsigned char', 'unsigned int', 'float']
self.matrix_offset_types = {'AffineTransform', 'CenteredAffineTransform',
'Euler2DTransform', 'Euler3DTransform',
'Rigid2DTransform', 'QuaternionRigidTransform',
'Similarity2DTransform', 'CenteredSimilarity2DTransform',
'Similarity3DTransform', 'CenteredRigid2DTransform',
'CenteredEuler3DTransform', 'Rigid3DTransform'}
def create_random_linear_transform(image,
fixed_parameters,
transform_type='affine',
sd_affine=1.0):
transform = ants.create_ants_transform(
transform_type="AffineTransform",
precision='float',
dimension=image.dimension)
ants.set_ants_transform_fixed_parameters(transform, fixed_parameters)
identity_parameters = ants.get_ants_transform_parameters(transform)
random_epsilon = np.random.normal(loc=0,
scale=sd_affine,
size=len(identity_parameters))
if transform_type == 'translation':
random_epsilon[:(len(identity_parameters) - image.dimension)] = 0
random_parameters = identity_parameters + random_epsilon
random_matrix = np.reshape(
random_parameters[:(len(identity_parameters) - image.dimension)],
newshape=(image.dimension, image.dimension))
decomposition = polar_decomposition(random_matrix)
if transform_type == "rigid":
random_matrix = decomposition['Z']
elif transform_type == "affine":
random_matrix = decomposition['Xtilde']
elif transform_type == "scaleShear":
random_matrix = decomposition['P']
random_parameters[:(len(identity_parameters) - image.dimension)] = \
np.reshape(random_matrix, newshape=(len(identity_parameters) - image.dimension))
ants.set_ants_transform_parameters(transform, random_parameters)
return (transform)
def test_create_ants_transform(self):
for mtype in self.matrix_offset_types:
for ptype in {'float','double'}:
for ndim in {2,3}:
tx = ants.create_ants_transform(transform_type=mtype, precision=ptype, dimension=ndim)
# supported types
tx = ants.create_ants_transform(supported_types=True)
# input params
translation = (3,4,5)
tx = ants.create_ants_transform( transform_type='Euler3DTransform', translation=translation )
translation = np.array([3,4,5])
tx = ants.create_ants_transform( transform_type='Euler3DTransform', translation=translation )
# invalid dimension
with self.assertRaises(Exception):
ants.create_ants_transform(dimension=5)
# unsupported type
with self.assertRaises(Exception):
ants.create_ants_transform(transform_type='unsupported-type')
# bad param arg
with self.assertRaises(Exception):
ants.create_ants_transform( transform_type='Euler3DTransform', translation=ants.image_read(ants.get_ants_data('r16')) )
# bad precision
with self.assertRaises(Exception):
ants.create_ants_transform(precision='unsigned int')
def lung_extraction(image,
modality="proton",
antsxnet_cache_directory=None,
verbose=False):
"""
Perform proton or ct lung extraction using U-net.
Arguments
---------
image : ANTsImage
input image
modality : string
Modality image type. Options include "ct", "proton", "protonLobes",
"maskLobes", and "ventilation".
antsxnet_cache_directory : string
Destination directory for storing the downloaded template and model weights.
Since these can be resused, if is None, these data will be downloaded to a
~/.keras/ANTsXNet/.
verbose : boolean
Print progress to the screen.
Returns
-------
Dictionary of ANTs segmentation and probability images.
Example
-------
>>> output = lung_extraction(lung_image, modality="proton")
"""
from ..architectures import create_unet_model_2d
from ..architectures import create_unet_model_3d
from ..utilities import get_pretrained_network
from ..utilities import get_antsxnet_data
from ..utilities import pad_or_crop_image_to_size
if image.dimension != 3:
raise ValueError( "Image dimension must be 3." )
if antsxnet_cache_directory == None:
antsxnet_cache_directory = "ANTsXNet"
image_mods = [modality]
channel_size = len(image_mods)
weights_file_name = None
unet_model = None
if modality == "proton":
weights_file_name = get_pretrained_network("protonLungMri",
antsxnet_cache_directory=antsxnet_cache_directory)
classes = ("background", "left_lung", "right_lung")
number_of_classification_labels = len(classes)
reorient_template_file_name_path = get_antsxnet_data("protonLungTemplate",
antsxnet_cache_directory=antsxnet_cache_directory)
reorient_template = ants.image_read(reorient_template_file_name_path)
resampled_image_size = reorient_template.shape
unet_model = create_unet_model_3d((*resampled_image_size, channel_size),
number_of_outputs=number_of_classification_labels,
number_of_layers=4, number_of_filters_at_base_layer=16, dropout_rate=0.0,
convolution_kernel_size=(7, 7, 5), deconvolution_kernel_size=(7, 7, 5))
unet_model.load_weights(weights_file_name)
if verbose == True:
print("Lung extraction: normalizing image to the template.")
center_of_mass_template = ants.get_center_of_mass(reorient_template * 0 + 1)
center_of_mass_image = ants.get_center_of_mass(image * 0 + 1)
translation = np.asarray(center_of_mass_image) - np.asarray(center_of_mass_template)
xfrm = ants.create_ants_transform(transform_type="Euler3DTransform",
center=np.asarray(center_of_mass_template), translation=translation)
warped_image = ants.apply_ants_transform_to_image(xfrm, image, reorient_template)
batchX = np.expand_dims(warped_image.numpy(), axis=0)
batchX = np.expand_dims(batchX, axis=-1)
batchX = (batchX - batchX.mean()) / batchX.std()
predicted_data = unet_model.predict(batchX, verbose=int(verbose))
origin = warped_image.origin
spacing = warped_image.spacing
direction = warped_image.direction
probability_images_array = list()
for i in range(number_of_classification_labels):
probability_images_array.append(
ants.from_numpy(np.squeeze(predicted_data[0, :, :, :, i]),
origin=origin, spacing=spacing, direction=direction))
if verbose == True:
print("Lung extraction: renormalize probability mask to native space.")
for i in range(number_of_classification_labels):
probability_images_array[i] = ants.apply_ants_transform_to_image(
ants.invert_ants_transform(xfrm), probability_images_array[i], image)
image_matrix = ants.image_list_to_matrix(probability_images_array, image * 0 + 1)
segmentation_matrix = np.argmax(image_matrix, axis=0)
segmentation_image = ants.matrix_to_images(
np.expand_dims(segmentation_matrix, axis=0), image * 0 + 1)[0]
return_dict = {'segmentation_image' : segmentation_image,
'probability_images' : probability_images_array}
return(return_dict)
if modality == "protonLobes" or modality == "maskLobes":
reorient_template_file_name_path = get_antsxnet_data("protonLungTemplate",
antsxnet_cache_directory=antsxnet_cache_directory)
reorient_template = ants.image_read(reorient_template_file_name_path)
resampled_image_size = reorient_template.shape
spatial_priors_file_name_path = get_antsxnet_data("protonLobePriors",
antsxnet_cache_directory=antsxnet_cache_directory)
spatial_priors = ants.image_read(spatial_priors_file_name_path)
priors_image_list = ants.ndimage_to_list(spatial_priors)
channel_size = 1 + len(priors_image_list)
number_of_classification_labels = 1 + len(priors_image_list)
unet_model = create_unet_model_3d((*resampled_image_size, channel_size),
number_of_outputs=number_of_classification_labels, mode="classification",
number_of_filters_at_base_layer=16, number_of_layers=4,
convolution_kernel_size=(3, 3, 3), deconvolution_kernel_size=(2, 2, 2),
dropout_rate=0.0, weight_decay=0, additional_options=("attentionGating",))
if modality == "protonLobes":
penultimate_layer = unet_model.layers[-2].output
outputs2 = Conv3D(filters=1,
kernel_size=(1, 1, 1),
activation='sigmoid',
kernel_regularizer=regularizers.l2(0.0))(penultimate_layer)
unet_model = Model(inputs=unet_model.input, outputs=[unet_model.output, outputs2])
weights_file_name = get_pretrained_network("protonLobes",
antsxnet_cache_directory=antsxnet_cache_directory)
else:
weights_file_name = get_pretrained_network("maskLobes",
antsxnet_cache_directory=antsxnet_cache_directory)
unet_model.load_weights(weights_file_name)
if verbose == True:
print("Lung extraction: normalizing image to the template.")
center_of_mass_template = ants.get_center_of_mass(reorient_template * 0 + 1)
center_of_mass_image = ants.get_center_of_mass(image * 0 + 1)
translation = np.asarray(center_of_mass_image) - np.asarray(center_of_mass_template)
xfrm = ants.create_ants_transform(transform_type="Euler3DTransform",
center=np.asarray(center_of_mass_template), translation=translation)
warped_image = ants.apply_ants_transform_to_image(xfrm, image, reorient_template)
warped_array = warped_image.numpy()
if modality == "protonLobes":
warped_array = (warped_array - warped_array.mean()) / warped_array.std()
else:
warped_array[warped_array != 0] = 1
batchX = np.zeros((1, *warped_array.shape, channel_size))
batchX[0,:,:,:,0] = warped_array
for i in range(len(priors_image_list)):
batchX[0,:,:,:,i+1] = priors_image_list[i].numpy()
predicted_data = unet_model.predict(batchX, verbose=int(verbose))
origin = warped_image.origin
spacing = warped_image.spacing
direction = warped_image.direction
probability_images_array = list()
for i in range(number_of_classification_labels):
if modality == "protonLobes":
probability_images_array.append(
ants.from_numpy(np.squeeze(predicted_data[0][0, :, :, :, i]),
origin=origin, spacing=spacing, direction=direction))
else:
probability_images_array.append(
ants.from_numpy(np.squeeze(predicted_data[0, :, :, :, i]),
origin=origin, spacing=spacing, direction=direction))
if verbose == True:
print("Lung extraction: renormalize probability images to native space.")
for i in range(number_of_classification_labels):
probability_images_array[i] = ants.apply_ants_transform_to_image(
ants.invert_ants_transform(xfrm), probability_images_array[i], image)
image_matrix = ants.image_list_to_matrix(probability_images_array, image * 0 + 1)
segmentation_matrix = np.argmax(image_matrix, axis=0)
segmentation_image = ants.matrix_to_images(
np.expand_dims(segmentation_matrix, axis=0), image * 0 + 1)[0]
if modality == "protonLobes":
whole_lung_mask = ants.from_numpy(np.squeeze(predicted_data[1][0, :, :, :, 0]),
origin=origin, spacing=spacing, direction=direction)
whole_lung_mask = ants.apply_ants_transform_to_image(
ants.invert_ants_transform(xfrm), whole_lung_mask, image)
return_dict = {'segmentation_image' : segmentation_image,
'probability_images' : probability_images_array,
'whole_lung_mask_image' : whole_lung_mask}
return(return_dict)
else:
return_dict = {'segmentation_image' : segmentation_image,
'probability_images' : probability_images_array}
return(return_dict)
elif modality == "ct":
################################
#
# Preprocess image
#
################################
if verbose == True:
print("Preprocess CT image.")
def closest_simplified_direction_matrix(direction):
closest = np.floor(np.abs(direction) + 0.5)
closest[direction < 0] *= -1.0
return closest
simplified_direction = closest_simplified_direction_matrix(image.direction)
reference_image_size = (128, 128, 128)
ct_preprocessed = ants.resample_image(image, reference_image_size, use_voxels=True, interp_type=0)
ct_preprocessed[ct_preprocessed < -1000] = -1000
ct_preprocessed[ct_preprocessed > 400] = 400
ct_preprocessed.set_direction(simplified_direction)
ct_preprocessed.set_origin((0, 0, 0))
ct_preprocessed.set_spacing((1, 1, 1))
################################
#
# Reorient image
#
################################
reference_image = ants.make_image(reference_image_size,
voxval=0,
spacing=(1, 1, 1),
origin=(0, 0, 0),
direction=np.identity(3))
center_of_mass_reference = np.floor(ants.get_center_of_mass(reference_image * 0 + 1))
center_of_mass_image = np.floor(ants.get_center_of_mass(ct_preprocessed * 0 + 1))
translation = np.asarray(center_of_mass_image) - np.asarray(center_of_mass_reference)
xfrm = ants.create_ants_transform(transform_type="Euler3DTransform",
center=np.asarray(center_of_mass_reference), translation=translation)
ct_preprocessed = ((ct_preprocessed - ct_preprocessed.min()) /
(ct_preprocessed.max() - ct_preprocessed.min()))
ct_preprocessed_warped = ants.apply_ants_transform_to_image(
xfrm, ct_preprocessed, reference_image, interpolation="nearestneighbor")
ct_preprocessed_warped = ((ct_preprocessed_warped - ct_preprocessed_warped.min()) /
(ct_preprocessed_warped.max() - ct_preprocessed_warped.min())) - 0.5
################################
#
# Build models and load weights
#
################################
if verbose == True:
print("Build model and load weights.")
weights_file_name = get_pretrained_network("lungCtWithPriorsSegmentationWeights",
antsxnet_cache_directory=antsxnet_cache_directory)
classes = ("background", "left lung", "right lung", "airways")
number_of_classification_labels = len(classes)
luna16_priors = ants.ndimage_to_list(ants.image_read(get_antsxnet_data("luna16LungPriors")))
for i in range(len(luna16_priors)):
luna16_priors[i] = ants.resample_image(luna16_priors[i], reference_image_size, use_voxels=True)
channel_size = len(luna16_priors) + 1
unet_model = create_unet_model_3d((*reference_image_size, channel_size),
number_of_outputs=number_of_classification_labels, mode="classification",
number_of_layers=4, number_of_filters_at_base_layer=16, dropout_rate=0.0,
convolution_kernel_size=(3, 3, 3), deconvolution_kernel_size=(2, 2, 2),
weight_decay=1e-5, additional_options=("attentionGating",))
unet_model.load_weights(weights_file_name)
################################
#
# Do prediction and normalize to native space
#
################################
if verbose == True:
print("Prediction.")
batchX = np.zeros((1, *reference_image_size, channel_size))
batchX[:,:,:,:,0] = ct_preprocessed_warped.numpy()
for i in range(len(luna16_priors)):
batchX[:,:,:,:,i+1] = luna16_priors[i].numpy() - 0.5
predicted_data = unet_model.predict(batchX, verbose=verbose)
probability_images = list()
for i in range(number_of_classification_labels):
if verbose == True:
print("Reconstructing image", classes[i])
probability_image = ants.from_numpy(np.squeeze(predicted_data[:,:,:,:,i]),
origin=ct_preprocessed_warped.origin, spacing=ct_preprocessed_warped.spacing,
direction=ct_preprocessed_warped.direction)
probability_image = ants.apply_ants_transform_to_image(
ants.invert_ants_transform(xfrm), probability_image, ct_preprocessed)
probability_image = ants.resample_image(probability_image,
resample_params=image.shape, use_voxels=True, interp_type=0)
probability_image = ants.copy_image_info(image, probability_image)
probability_images.append(probability_image)
image_matrix = ants.image_list_to_matrix(probability_images, image * 0 + 1)
segmentation_matrix = np.argmax(image_matrix, axis=0)
segmentation_image = ants.matrix_to_images(
np.expand_dims(segmentation_matrix, axis=0), image * 0 + 1)[0]
return_dict = {'segmentation_image' : segmentation_image,
'probability_images' : probability_images}
return(return_dict)
elif modality == "ventilation":
################################
#
# Preprocess image
#
################################
if verbose == True:
print("Preprocess ventilation image.")
template_size = (256, 256)
image_modalities = ("Ventilation",)
channel_size = len(image_modalities)
preprocessed_image = (image - image.mean()) / image.std()
ants.set_direction(preprocessed_image, np.identity(3))
################################
#
# Build models and load weights
#
################################
unet_model = create_unet_model_2d((*template_size, channel_size),
number_of_outputs=1, mode='sigmoid',
number_of_layers=4, number_of_filters_at_base_layer=32, dropout_rate=0.0,
convolution_kernel_size=(3, 3), deconvolution_kernel_size=(2, 2),
weight_decay=0)
if verbose == True:
print("Whole lung mask: retrieving model weights.")
weights_file_name = get_pretrained_network("wholeLungMaskFromVentilation",
antsxnet_cache_directory=antsxnet_cache_directory)
unet_model.load_weights(weights_file_name)
################################
#
# Extract slices
#
################################
spacing = ants.get_spacing(preprocessed_image)
dimensions_to_predict = (spacing.index(max(spacing)),)
total_number_of_slices = 0
for d in range(len(dimensions_to_predict)):
total_number_of_slices += preprocessed_image.shape[dimensions_to_predict[d]]
batchX = np.zeros((total_number_of_slices, *template_size, channel_size))
slice_count = 0
for d in range(len(dimensions_to_predict)):
number_of_slices = preprocessed_image.shape[dimensions_to_predict[d]]
if verbose == True:
print("Extracting slices for dimension ", dimensions_to_predict[d], ".")
for i in range(number_of_slices):
ventilation_slice = pad_or_crop_image_to_size(ants.slice_image(preprocessed_image, dimensions_to_predict[d], i), template_size)
batchX[slice_count,:,:,0] = ventilation_slice.numpy()
slice_count += 1
################################
#
# Do prediction and then restack into the image
#
################################
if verbose == True:
print("Prediction.")
prediction = unet_model.predict(batchX, verbose=verbose)
permutations = list()
permutations.append((0, 1, 2))
permutations.append((1, 0, 2))
permutations.append((1, 2, 0))
probability_image = ants.image_clone(image) * 0
current_start_slice = 0
for d in range(len(dimensions_to_predict)):
current_end_slice = current_start_slice + preprocessed_image.shape[dimensions_to_predict[d]] - 1
which_batch_slices = range(current_start_slice, current_end_slice)
prediction_per_dimension = prediction[which_batch_slices,:,:,0]
prediction_array = np.transpose(np.squeeze(prediction_per_dimension), permutations[dimensions_to_predict[d]])
prediction_image = ants.copy_image_info(image,
pad_or_crop_image_to_size(ants.from_numpy(prediction_array),
image.shape))
probability_image = probability_image + (prediction_image - probability_image) / (d + 1)
current_start_slice = current_end_slice + 1
return(probability_image)
else:
return ValueError("Unrecognized modality.")
def sysu_media_wmh_segmentation(flair,
t1=None,
do_preprocessing=True,
use_ensemble=True,
use_axial_slices_only=True,
antsxnet_cache_directory=None,
verbose=False):
"""
Perform WMH segmentation using the winning submission in the MICCAI
2017 challenge by the sysu_media team using FLAIR or T1/FLAIR. The
MICCAI challenge is discussed in
https://pubmed.ncbi.nlm.nih.gov/30908194/
with the sysu_media's team entry is discussed in
https://pubmed.ncbi.nlm.nih.gov/30125711/
with the original implementation available here:
https://github.com/hongweilibran/wmh_ibbmTum
Arguments
---------
flair : ANTsImage
input 3-D FLAIR brain image (not skull-stripped).
t1 : ANTsImage
input 3-D T1 brain image (not skull-stripped).
do_preprocessing : boolean
perform n4 bias correction?
use_ensemble : boolean
check whether to use all 3 sets of weights.
use_axial_slices_only : boolean
If True, use original implementation which was trained on axial slices.
If False, use ANTsXNet variant implementation which applies the slice-by-slice
models to all 3 dimensions and averages the results.
antsxnet_cache_directory : string
Destination directory for storing the downloaded template and model weights.
Since these can be resused, if is None, these data will be downloaded to a
~/.keras/ANTsXNet/.
verbose : boolean
Print progress to the screen.
Returns
-------
WMH segmentation probability image
Example
-------
>>> image = ants.image_read("flair.nii.gz")
>>> probability_mask = sysu_media_wmh_segmentation(image)
"""
from ..architectures import create_sysu_media_unet_model_2d
from ..utilities import brain_extraction
from ..utilities import crop_image_center
from ..utilities import get_pretrained_network
from ..utilities import preprocess_brain_image
from ..utilities import pad_or_crop_image_to_size
if flair.dimension != 3:
raise ValueError("Image dimension must be 3.")
if antsxnet_cache_directory == None:
antsxnet_cache_directory = "ANTsXNet"
################################
#
# Preprocess images
#
################################
flair_preprocessed = flair
if do_preprocessing == True:
flair_preprocessing = preprocess_brain_image(
flair,
truncate_intensity=(0.01, 0.99),
do_brain_extraction=False,
do_bias_correction=True,
do_denoising=False,
antsxnet_cache_directory=antsxnet_cache_directory,
verbose=verbose)
flair_preprocessed = flair_preprocessing["preprocessed_image"]
number_of_channels = 1
if t1 is not None:
t1_preprocessed = t1
if do_preprocessing == True:
t1_preprocessing = preprocess_brain_image(
t1,
truncate_intensity=(0.01, 0.99),
do_brain_extraction=False,
do_bias_correction=True,
do_denoising=False,
antsxnet_cache_directory=antsxnet_cache_directory,
verbose=verbose)
t1_preprocessed = t1_preprocessing["preprocessed_image"]
number_of_channels = 2
################################
#
# Estimate mask
#
################################
brain_mask = None
if verbose == True:
print("Estimating brain mask.")
if t1 is not None:
brain_mask = brain_extraction(t1, modality="t1")
else:
brain_mask = brain_extraction(flair, modality="flair")
reference_image = ants.make_image((200, 200, 200),
voxval=1,
spacing=(1, 1, 1),
origin=(0, 0, 0),
direction=np.identity(3))
center_of_mass_reference = ants.get_center_of_mass(reference_image)
center_of_mass_image = ants.get_center_of_mass(brain_mask)
translation = np.asarray(center_of_mass_image) - np.asarray(
center_of_mass_reference)
xfrm = ants.create_ants_transform(
transform_type="Euler3DTransform",
center=np.asarray(center_of_mass_reference),
translation=translation)
flair_preprocessed_warped = ants.apply_ants_transform_to_image(
xfrm, flair_preprocessed, reference_image)
brain_mask_warped = ants.threshold_image(
ants.apply_ants_transform_to_image(xfrm, brain_mask, reference_image),
0.5, 1.1, 1, 0)
if t1 is not None:
t1_preprocessed_warped = ants.apply_ants_transform_to_image(
xfrm, t1_preprocessed, reference_image)
################################
#
# Gaussian normalize intensity based on brain mask
#
################################
mean_flair = flair_preprocessed_warped[brain_mask_warped > 0].mean()
std_flair = flair_preprocessed_warped[brain_mask_warped > 0].std()
flair_preprocessed_warped = (flair_preprocessed_warped -
mean_flair) / std_flair
if number_of_channels == 2:
mean_t1 = t1_preprocessed_warped[brain_mask_warped > 0].mean()
std_t1 = t1_preprocessed_warped[brain_mask_warped > 0].std()
t1_preprocessed_warped = (t1_preprocessed_warped - mean_t1) / std_t1
################################
#
# Build models and load weights
#
################################
number_of_models = 1
if use_ensemble == True:
number_of_models = 3
unet_models = list()
for i in range(number_of_models):
if number_of_channels == 1:
weights_file_name = get_pretrained_network(
"sysuMediaWmhFlairOnlyModel" + str(i),
antsxnet_cache_directory=antsxnet_cache_directory)
else:
weights_file_name = get_pretrained_network(
"sysuMediaWmhFlairT1Model" + str(i),
antsxnet_cache_directory=antsxnet_cache_directory)
unet_models.append(
create_sysu_media_unet_model_2d((200, 200, number_of_channels)))
unet_models[i].load_weights(weights_file_name)
################################
#
# Extract slices
#
################################
dimensions_to_predict = [2]
if use_axial_slices_only == False:
dimensions_to_predict = list(range(3))
total_number_of_slices = 0
for d in range(len(dimensions_to_predict)):
total_number_of_slices += flair_preprocessed_warped.shape[
dimensions_to_predict[d]]
batchX = np.zeros((total_number_of_slices, 200, 200, number_of_channels))
slice_count = 0
for d in range(len(dimensions_to_predict)):
number_of_slices = flair_preprocessed_warped.shape[
dimensions_to_predict[d]]
if verbose == True:
print("Extracting slices for dimension ", dimensions_to_predict[d],
".")
for i in range(number_of_slices):
flair_slice = pad_or_crop_image_to_size(
ants.slice_image(flair_preprocessed_warped,
dimensions_to_predict[d], i), (200, 200))
batchX[slice_count, :, :, 0] = flair_slice.numpy()
if number_of_channels == 2:
t1_slice = pad_or_crop_image_to_size(
ants.slice_image(t1_preprocessed_warped,
dimensions_to_predict[d], i), (200, 200))
batchX[slice_count, :, :, 1] = t1_slice.numpy()
slice_count += 1
################################
#
# Do prediction and then restack into the image
#
################################
if verbose == True:
print("Prediction.")
prediction = unet_models[0].predict(batchX, verbose=verbose)
if number_of_models > 1:
for i in range(1, number_of_models, 1):
prediction += unet_models[i].predict(batchX, verbose=verbose)
prediction /= number_of_models
permutations = list()
permutations.append((0, 1, 2))
permutations.append((1, 0, 2))
permutations.append((1, 2, 0))
prediction_image_average = ants.image_clone(flair_preprocessed_warped) * 0
current_start_slice = 0
for d in range(len(dimensions_to_predict)):
current_end_slice = current_start_slice + flair_preprocessed_warped.shape[
dimensions_to_predict[d]] - 1
which_batch_slices = range(current_start_slice, current_end_slice)
prediction_per_dimension = prediction[which_batch_slices, :, :, :]
prediction_array = np.transpose(np.squeeze(prediction_per_dimension),
permutations[dimensions_to_predict[d]])
prediction_image = ants.copy_image_info(
flair_preprocessed_warped,
pad_or_crop_image_to_size(ants.from_numpy(prediction_array),
flair_preprocessed_warped.shape))
prediction_image_average = prediction_image_average + (
prediction_image - prediction_image_average) / (d + 1)
current_start_slice = current_end_slice + 1
probability_image = ants.apply_ants_transform_to_image(
ants.invert_ants_transform(xfrm), prediction_image_average, flair)
return (probability_image)
print("Reading reorientation template " + reorient_template_file_name)
start_time = time.time()
reorient_template = ants.image_read(reorient_template_file_name)
end_time = time.time()
elapsed_time = end_time - start_time
print(" (elapsed time: ", elapsed_time, " seconds)")
print("Normalizing to template")
start_time = time.time()
center_of_mass_template = ants.get_center_of_mass(reorient_template)
center_of_mass_image = ants.get_center_of_mass(image)
translation = np.asarray(center_of_mass_image) - np.asarray(
center_of_mass_template)
xfrm = ants.create_ants_transform(transform_type="Euler3DTransform",
center=np.asarray(center_of_mass_template),
translation=translation)
warped_image = ants.apply_ants_transform_to_image(xfrm, image,
reorient_template)
warped_image = (warped_image - warped_image.mean()) / warped_image.std()
#########################################
#
# Perform initial (stage 1) segmentation
#
print("************* Initial stage segmentation ***************")
# print(" (warning: steps are somewhat different in the ")
# print(" publication. just getting something to work)")
print("")
def lung_extraction(image,
modality="proton",
output_directory=None,
verbose=None):
"""
Perform proton or ct lung extraction using U-net.
Arguments
---------
image : ANTsImage
input image
modality : string
Modality image type. Options include "ct" and "proton".
output_directory : string
Destination directory for storing the downloaded template and model weights.
Since these can be resused, if is None, these data will be downloaded to a
tempfile.
verbose : boolean
Print progress to the screen.
Returns
-------
Dictionary of ANTs segmentation and probability images.
Example
-------
>>> output = lung_extraction(lung_image, modality="proton")
"""
from ..architectures import create_unet_model_3d
from ..utilities import get_pretrained_network
if image.dimension != 3:
raise ValueError("Image dimension must be 3.")
image_mods = [modality]
channel_size = len(image_mods)
weights_file_name = None
unet_model = None
if modality == "proton":
if output_directory is not None:
weights_file_name = output_directory + "/protonLungSegmentationWeights.h5"
if not os.path.exists(weights_file_name):
if verbose == True:
print("Lung extraction: downloading weights.")
weights_file_name = get_pretrained_network(
"protonLungMri", weights_file_name)
else:
weights_file_name = get_pretrained_network("protonLungMri")
classes = ("background", "left_lung", "right_lung")
number_of_classification_labels = len(classes)
reorient_template_file_name = None
reorient_template_file_exists = False
if output_directory is not None:
reorient_template_file_name = output_directory + "/protonLungTemplate.nii.gz"
if os.path.exists(reorient_template_file_name):
reorient_template_file_exists = True
reorient_template = None
if output_directory is None or reorient_template_file_exists == False:
reorient_template_file = tempfile.NamedTemporaryFile(
suffix=".nii.gz")
reorient_template_file.close()
template_file_name = reorient_template_file.name
template_url = "https://ndownloader.figshare.com/files/22707338"
if not os.path.exists(template_file_name):
if verbose == True:
print("Lung extraction: downloading template.")
r = requests.get(template_url)
with open(template_file_name, 'wb') as f:
f.write(r.content)
reorient_template = ants.image_read(template_file_name)
if output_directory is not None:
shutil.copy(template_file_name, reorient_template_file_name)
else:
reorient_template = ants.image_read(reorient_template_file_name)
resampled_image_size = reorient_template.shape
unet_model = create_unet_model_3d(
(*resampled_image_size, channel_size),
number_of_outputs=number_of_classification_labels,
number_of_layers=4,
number_of_filters_at_base_layer=16,
dropout_rate=0.0,
convolution_kernel_size=(7, 7, 5),
deconvolution_kernel_size=(7, 7, 5))
unet_model.load_weights(weights_file_name)
if verbose == True:
print("Lung extraction: normalizing image to the template.")
center_of_mass_template = ants.get_center_of_mass(reorient_template *
0 + 1)
center_of_mass_image = ants.get_center_of_mass(image * 0 + 1)
translation = np.asarray(center_of_mass_image) - np.asarray(
center_of_mass_template)
xfrm = ants.create_ants_transform(
transform_type="Euler3DTransform",
center=np.asarray(center_of_mass_template),
translation=translation)
warped_image = ants.apply_ants_transform_to_image(
xfrm, image, reorient_template)
batchX = np.expand_dims(warped_image.numpy(), axis=0)
batchX = np.expand_dims(batchX, axis=-1)
batchX = (batchX - batchX.mean()) / batchX.std()
predicted_data = unet_model.predict(batchX, verbose=0)
origin = warped_image.origin
spacing = warped_image.spacing
direction = warped_image.direction
probability_images_array = list()
for i in range(number_of_classification_labels):
probability_images_array.append(
ants.from_numpy(np.squeeze(predicted_data[0, :, :, :, i]),
origin=origin,
spacing=spacing,
direction=direction))
if verbose == True:
print(
"Lung extraction: renormalize probability mask to native space."
)
for i in range(number_of_classification_labels):
probability_images_array[i] = ants.apply_ants_transform_to_image(
ants.invert_ants_transform(xfrm), probability_images_array[i],
image)
image_matrix = ants.image_list_to_matrix(probability_images_array,
image * 0 + 1)
segmentation_matrix = np.argmax(image_matrix, axis=0)
segmentation_image = ants.matrix_to_images(
np.expand_dims(segmentation_matrix, axis=0), image * 0 + 1)[0]
return_dict = {
'segmentation_image': segmentation_image,
'probability_images': probability_images_array
}
return (return_dict)
elif modality == "ct":
if output_directory is not None:
weights_file_name = output_directory + "/ctLungSegmentationWeights.h5"
if not os.path.exists(weights_file_name):
if verbose == True:
print("Lung extraction: downloading weights.")
weights_file_name = get_pretrained_network(
"ctHumanLung", weights_file_name)
else:
weights_file_name = get_pretrained_network("ctHumanLung")
classes = ("background", "left_lung", "right_lung", "trachea")
number_of_classification_labels = len(classes)
reorient_template_file_name = None
reorient_template_file_exists = False
if output_directory is not None:
reorient_template_file_name = output_directory + "/ctLungTemplate.nii.gz"
if os.path.exists(reorient_template_file_name):
reorient_template_file_exists = True
reorient_template = None
if output_directory is None or reorient_template_file_exists == False:
reorient_template_file = tempfile.NamedTemporaryFile(
suffix=".nii.gz")
reorient_template_file.close()
template_file_name = reorient_template_file.name
template_url = "https://ndownloader.figshare.com/files/22707335"
if not os.path.exists(template_file_name):
if verbose == True:
print("Lung extraction: downloading template.")
r = requests.get(template_url)
with open(template_file_name, 'wb') as f:
f.write(r.content)
reorient_template = ants.image_read(template_file_name)
if output_directory is not None:
shutil.copy(template_file_name, reorient_template_file_name)
else:
reorient_template = ants.image_read(reorient_template_file_name)
resampled_image_size = reorient_template.shape
unet_model = create_unet_model_3d(
(*resampled_image_size, channel_size),
number_of_outputs=number_of_classification_labels,
number_of_layers=4,
number_of_filters_at_base_layer=8,
dropout_rate=0.0,
convolution_kernel_size=(3, 3, 3),
deconvolution_kernel_size=(2, 2, 2))
unet_model.load_weights(weights_file_name)
if verbose == True:
print("Lung extraction: normalizing image to the template.")
center_of_mass_template = ants.get_center_of_mass(reorient_template *
0 + 1)
center_of_mass_image = ants.get_center_of_mass(image * 0 + 1)
translation = np.asarray(center_of_mass_image) - np.asarray(
center_of_mass_template)
xfrm = ants.create_ants_transform(
transform_type="Euler3DTransform",
center=np.asarray(center_of_mass_template),
translation=translation)
warped_image = ants.apply_ants_transform_to_image(
xfrm, image, reorient_template)
batchX = np.expand_dims(warped_image.numpy(), axis=0)
batchX = np.expand_dims(batchX, axis=-1)
batchX = (batchX - batchX.mean()) / batchX.std()
predicted_data = unet_model.predict(batchX, verbose=0)
origin = warped_image.origin
spacing = warped_image.spacing
direction = warped_image.direction
probability_images_array = list()
for i in range(number_of_classification_labels):
probability_images_array.append(
ants.from_numpy(np.squeeze(predicted_data[0, :, :, :, i]),
origin=origin,
spacing=spacing,
direction=direction))
if verbose == True:
print(
"Lung extraction: renormalize probability mask to native space."
)
for i in range(number_of_classification_labels):
probability_images_array[i] = ants.apply_ants_transform_to_image(
ants.invert_ants_transform(xfrm), probability_images_array[i],
image)
image_matrix = ants.image_list_to_matrix(probability_images_array,
image * 0 + 1)
segmentation_matrix = np.argmax(image_matrix, axis=0)
segmentation_image = ants.matrix_to_images(
np.expand_dims(segmentation_matrix, axis=0), image * 0 + 1)[0]
return_dict = {
'segmentation_image': segmentation_image,
'probability_images': probability_images_array
}
return (return_dict)
def claustrum_segmentation(t1,
do_preprocessing=True,
use_ensemble=True,
antsxnet_cache_directory=None,
verbose=False):
"""
Claustrum segmentation
Described here:
https://arxiv.org/abs/2008.03465
with the implementation available at:
https://github.com/hongweilibran/claustrum_multi_view
Arguments
---------
t1 : ANTsImage
input 3-D T1 brain image.
do_preprocessing : boolean
perform n4 bias correction.
use_ensemble : boolean
check whether to use all 3 sets of weights.
antsxnet_cache_directory : string
Destination directory for storing the downloaded template and model weights.
Since these can be resused, if is None, these data will be downloaded to a
~/.keras/ANTsXNet/.
verbose : boolean
Print progress to the screen.
Returns
-------
Claustrum segmentation probability image
Example
-------
>>> image = ants.image_read("t1.nii.gz")
>>> probability_mask = claustrum_segmentation(image)
"""
from ..architectures import create_sysu_media_unet_model_2d
from ..utilities import brain_extraction
from ..utilities import get_pretrained_network
from ..utilities import preprocess_brain_image
from ..utilities import pad_or_crop_image_to_size
if t1.dimension != 3:
raise ValueError("Image dimension must be 3.")
if antsxnet_cache_directory == None:
antsxnet_cache_directory = "ANTsXNet"
image_size = (180, 180)
################################
#
# Preprocess images
#
################################
number_of_channels = 1
t1_preprocessed = ants.image_clone(t1)
brain_mask = ants.threshold_image(t1, 0, 0, 0, 1)
if do_preprocessing == True:
t1_preprocessing = preprocess_brain_image(
t1,
truncate_intensity=(0.01, 0.99),
brain_extraction_modality="t1",
do_bias_correction=True,
do_denoising=True,
antsxnet_cache_directory=antsxnet_cache_directory,
verbose=verbose)
t1_preprocessed = t1_preprocessing["preprocessed_image"]
brain_mask = t1_preprocessing["brain_mask"]
reference_image = ants.make_image((170, 256, 256),
voxval=1,
spacing=(1, 1, 1),
origin=(0, 0, 0),
direction=np.identity(3))
center_of_mass_reference = ants.get_center_of_mass(reference_image)
center_of_mass_image = ants.get_center_of_mass(brain_mask)
translation = np.asarray(center_of_mass_image) - np.asarray(
center_of_mass_reference)
xfrm = ants.create_ants_transform(
transform_type="Euler3DTransform",
center=np.asarray(center_of_mass_reference),
translation=translation)
t1_preprocessed_warped = ants.apply_ants_transform_to_image(
xfrm, t1_preprocessed, reference_image)
brain_mask_warped = ants.threshold_image(
ants.apply_ants_transform_to_image(xfrm, brain_mask, reference_image),
0.5, 1.1, 1, 0)
################################
#
# Gaussian normalize intensity based on brain mask
#
################################
mean_t1 = t1_preprocessed_warped[brain_mask_warped > 0].mean()
std_t1 = t1_preprocessed_warped[brain_mask_warped > 0].std()
t1_preprocessed_warped = (t1_preprocessed_warped - mean_t1) / std_t1
t1_preprocessed_warped = t1_preprocessed_warped * brain_mask_warped
################################
#
# Build models and load weights
#
################################
number_of_models = 1
if use_ensemble == True:
number_of_models = 3
if verbose == True:
print("Claustrum: retrieving axial model weights.")
unet_axial_models = list()
for i in range(number_of_models):
weights_file_name = get_pretrained_network(
"claustrum_axial_" + str(i),
antsxnet_cache_directory=antsxnet_cache_directory)
unet_axial_models.append(
create_sysu_media_unet_model_2d((*image_size, number_of_channels),
anatomy="claustrum"))
unet_axial_models[i].load_weights(weights_file_name)
if verbose == True:
print("Claustrum: retrieving coronal model weights.")
unet_coronal_models = list()
for i in range(number_of_models):
weights_file_name = get_pretrained_network(
"claustrum_coronal_" + str(i),
antsxnet_cache_directory=antsxnet_cache_directory)
unet_coronal_models.append(
create_sysu_media_unet_model_2d((*image_size, number_of_channels),
anatomy="claustrum"))
unet_coronal_models[i].load_weights(weights_file_name)
################################
#
# Extract slices
#
################################
dimensions_to_predict = [1, 2]
batch_coronal_X = np.zeros(
(t1_preprocessed_warped.shape[1], *image_size, number_of_channels))
batch_axial_X = np.zeros(
(t1_preprocessed_warped.shape[2], *image_size, number_of_channels))
for d in range(len(dimensions_to_predict)):
number_of_slices = t1_preprocessed_warped.shape[
dimensions_to_predict[d]]
if verbose == True:
print("Extracting slices for dimension ", dimensions_to_predict[d],
".")
for i in range(number_of_slices):
t1_slice = pad_or_crop_image_to_size(
ants.slice_image(t1_preprocessed_warped,
dimensions_to_predict[d], i), image_size)
if dimensions_to_predict[d] == 1:
batch_coronal_X[i, :, :, 0] = np.rot90(t1_slice.numpy(), k=-1)
else:
batch_axial_X[i, :, :, 0] = np.rot90(t1_slice.numpy())
################################
#
# Do prediction and then restack into the image
#
################################
if verbose == True:
print("Coronal prediction.")
prediction_coronal = unet_coronal_models[0].predict(batch_coronal_X,
verbose=verbose)
if number_of_models > 1:
for i in range(1, number_of_models, 1):
prediction_coronal += unet_coronal_models[i].predict(
batch_coronal_X, verbose=verbose)
prediction_coronal /= number_of_models
for i in range(t1_preprocessed_warped.shape[1]):
prediction_coronal[i, :, :, 0] = np.rot90(
np.squeeze(prediction_coronal[i, :, :, 0]))
if verbose == True:
print("Axial prediction.")
prediction_axial = unet_axial_models[0].predict(batch_axial_X,
verbose=verbose)
if number_of_models > 1:
for i in range(1, number_of_models, 1):
prediction_axial += unet_axial_models[i].predict(batch_axial_X,
verbose=verbose)
prediction_axial /= number_of_models
for i in range(t1_preprocessed_warped.shape[2]):
prediction_axial[i, :, :,
0] = np.rot90(np.squeeze(prediction_axial[i, :, :,
0]),
k=-1)
if verbose == True:
print("Restack image and transform back to native space.")
permutations = list()
permutations.append((0, 1, 2))
permutations.append((1, 0, 2))
permutations.append((1, 2, 0))
prediction_image_average = ants.image_clone(t1_preprocessed_warped) * 0
for d in range(len(dimensions_to_predict)):
which_batch_slices = range(
t1_preprocessed_warped.shape[dimensions_to_predict[d]])
prediction_per_dimension = None
if dimensions_to_predict[d] == 1:
prediction_per_dimension = prediction_coronal[
which_batch_slices, :, :, :]
else:
prediction_per_dimension = prediction_axial[
which_batch_slices, :, :, :]
prediction_array = np.transpose(np.squeeze(prediction_per_dimension),
permutations[dimensions_to_predict[d]])
prediction_image = ants.copy_image_info(
t1_preprocessed_warped,
pad_or_crop_image_to_size(ants.from_numpy(prediction_array),
t1_preprocessed_warped.shape))
prediction_image_average = prediction_image_average + (
prediction_image - prediction_image_average) / (d + 1)
probability_image = ants.apply_ants_transform_to_image(
ants.invert_ants_transform(xfrm), prediction_image_average,
t1) * ants.threshold_image(brain_mask, 0.5, 1, 1, 0)
return (probability_image)
def sysu_media_wmh_segmentation(flair,
t1=None,
use_ensemble=True,
antsxnet_cache_directory=None,
verbose=False):
"""
Perform WMH segmentation using the winning submission in the MICCAI
2017 challenge by the sysu_media team using FLAIR or T1/FLAIR. The
MICCAI challenge is discussed in
https://pubmed.ncbi.nlm.nih.gov/30908194/
with the sysu_media's team entry is discussed in
https://pubmed.ncbi.nlm.nih.gov/30125711/
with the original implementation available here:
https://github.com/hongweilibran/wmh_ibbmTum
The original implementation used global thresholding as a quick
brain extraction approach. Due to possible generalization difficulties,
we leave such post-processing steps to the user. For brain or white
matter masking see functions brain_extraction or deep_atropos,
respectively.
Arguments
---------
flair : ANTsImage
input 3-D FLAIR brain image (not skull-stripped).
t1 : ANTsImage
input 3-D T1 brain image (not skull-stripped).
use_ensemble : boolean
check whether to use all 3 sets of weights.
antsxnet_cache_directory : string
Destination directory for storing the downloaded template and model weights.
Since these can be resused, if is None, these data will be downloaded to a
~/.keras/ANTsXNet/.
verbose : boolean
Print progress to the screen.
Returns
-------
WMH segmentation probability image
Example
-------
>>> image = ants.image_read("flair.nii.gz")
>>> probability_mask = sysu_media_wmh_segmentation(image)
"""
from ..architectures import create_sysu_media_unet_model_2d
from ..utilities import get_pretrained_network
from ..utilities import pad_or_crop_image_to_size
from ..utilities import preprocess_brain_image
from ..utilities import binary_dice_coefficient
if flair.dimension != 3:
raise ValueError("Image dimension must be 3.")
if antsxnet_cache_directory == None:
antsxnet_cache_directory = "ANTsXNet"
image_size = (200, 200)
################################
#
# Preprocess images
#
################################
def closest_simplified_direction_matrix(direction):
closest = (np.abs(direction) + 0.5).astype(int).astype(float)
closest[direction < 0] *= -1.0
return closest
simplified_direction = closest_simplified_direction_matrix(flair.direction)
flair_preprocessing = preprocess_brain_image(
flair,
truncate_intensity=None,
brain_extraction_modality=None,
do_bias_correction=False,
do_denoising=False,
antsxnet_cache_directory=antsxnet_cache_directory,
verbose=verbose)
flair_preprocessed = flair_preprocessing["preprocessed_image"]
flair_preprocessed.set_direction(simplified_direction)
flair_preprocessed.set_origin((0, 0, 0))
flair_preprocessed.set_spacing((1, 1, 1))
number_of_channels = 1
t1_preprocessed = None
if t1 is not None:
t1_preprocessing = preprocess_brain_image(
t1,
truncate_intensity=None,
brain_extraction_modality=None,
do_bias_correction=False,
do_denoising=False,
antsxnet_cache_directory=antsxnet_cache_directory,
verbose=verbose)
t1_preprocessed = t1_preprocessing["preprocessed_image"]
t1_preprocessed.set_direction(simplified_direction)
t1_preprocessed.set_origin((0, 0, 0))
t1_preprocessed.set_spacing((1, 1, 1))
number_of_channels = 2
################################
#
# Reorient images
#
################################
reference_image = ants.make_image((256, 256, 256),
voxval=0,
spacing=(1, 1, 1),
origin=(0, 0, 0),
direction=np.identity(3))
center_of_mass_reference = np.floor(
ants.get_center_of_mass(reference_image * 0 + 1))
center_of_mass_image = np.floor(
ants.get_center_of_mass(flair_preprocessed))
translation = np.asarray(center_of_mass_image) - np.asarray(
center_of_mass_reference)
xfrm = ants.create_ants_transform(
transform_type="Euler3DTransform",
center=np.asarray(center_of_mass_reference),
translation=translation)
flair_preprocessed_warped = ants.apply_ants_transform_to_image(
xfrm,
flair_preprocessed,
reference_image,
interpolation="nearestneighbor")
crop_image = ants.image_clone(flair_preprocessed) * 0 + 1
crop_image_warped = ants.apply_ants_transform_to_image(
xfrm, crop_image, reference_image, interpolation="nearestneighbor")
flair_preprocessed_warped = ants.crop_image(flair_preprocessed_warped,
crop_image_warped, 1)
if t1 is not None:
t1_preprocessed_warped = ants.apply_ants_transform_to_image(
xfrm,
t1_preprocessed,
reference_image,
interpolation="nearestneighbor")
t1_preprocessed_warped = ants.crop_image(t1_preprocessed_warped,
crop_image_warped, 1)
################################
#
# Gaussian normalize intensity
#
################################
mean_flair = flair_preprocessed.mean()
std_flair = flair_preprocessed.std()
if number_of_channels == 2:
mean_t1 = t1_preprocessed.mean()
std_t1 = t1_preprocessed.std()
flair_preprocessed_warped = (flair_preprocessed_warped -
mean_flair) / std_flair
if number_of_channels == 2:
t1_preprocessed_warped = (t1_preprocessed_warped - mean_t1) / std_t1
################################
#
# Build models and load weights
#
################################
number_of_models = 1
if use_ensemble == True:
number_of_models = 3
if verbose == True:
print("White matter hyperintensity: retrieving model weights.")
unet_models = list()
for i in range(number_of_models):
if number_of_channels == 1:
weights_file_name = get_pretrained_network(
"sysuMediaWmhFlairOnlyModel" + str(i),
antsxnet_cache_directory=antsxnet_cache_directory)
else:
weights_file_name = get_pretrained_network(
"sysuMediaWmhFlairT1Model" + str(i),
antsxnet_cache_directory=antsxnet_cache_directory)
unet_model = create_sysu_media_unet_model_2d(
(*image_size, number_of_channels))
unet_loss = binary_dice_coefficient(smoothing_factor=1.)
unet_model.compile(optimizer=keras.optimizers.Adam(learning_rate=2e-4),
loss=unet_loss)
unet_model.load_weights(weights_file_name)
unet_models.append(unet_model)
################################
#
# Extract slices
#
################################
dimensions_to_predict = [2]
total_number_of_slices = 0
for d in range(len(dimensions_to_predict)):
total_number_of_slices += flair_preprocessed_warped.shape[
dimensions_to_predict[d]]
batchX = np.zeros(
(total_number_of_slices, *image_size, number_of_channels))
slice_count = 0
for d in range(len(dimensions_to_predict)):
number_of_slices = flair_preprocessed_warped.shape[
dimensions_to_predict[d]]
if verbose == True:
print("Extracting slices for dimension ", dimensions_to_predict[d],
".")
for i in range(number_of_slices):
flair_slice = pad_or_crop_image_to_size(
ants.slice_image(flair_preprocessed_warped,
dimensions_to_predict[d], i), image_size)
batchX[slice_count, :, :, 0] = flair_slice.numpy()
if number_of_channels == 2:
t1_slice = pad_or_crop_image_to_size(
ants.slice_image(t1_preprocessed_warped,
dimensions_to_predict[d], i), image_size)
batchX[slice_count, :, :, 1] = t1_slice.numpy()
slice_count += 1
################################
#
# Do prediction and then restack into the image
#
################################
if verbose == True:
print("Prediction.")
prediction = unet_models[0].predict(np.transpose(batchX,
axes=(0, 2, 1, 3)),
verbose=verbose)
if number_of_models > 1:
for i in range(1, number_of_models, 1):
prediction += unet_models[i].predict(np.transpose(batchX,
axes=(0, 2, 1,
3)),
verbose=verbose)
prediction /= number_of_models
prediction = np.transpose(prediction, axes=(0, 2, 1, 3))
permutations = list()
permutations.append((0, 1, 2))
permutations.append((1, 0, 2))
permutations.append((1, 2, 0))
prediction_image_average = ants.image_clone(flair_preprocessed_warped) * 0
current_start_slice = 0
for d in range(len(dimensions_to_predict)):
current_end_slice = current_start_slice + flair_preprocessed_warped.shape[
dimensions_to_predict[d]]
which_batch_slices = range(current_start_slice, current_end_slice)
prediction_per_dimension = prediction[which_batch_slices, :, :, :]
prediction_array = np.transpose(np.squeeze(prediction_per_dimension),
permutations[dimensions_to_predict[d]])
prediction_image = ants.copy_image_info(
flair_preprocessed_warped,
pad_or_crop_image_to_size(ants.from_numpy(prediction_array),
flair_preprocessed_warped.shape))
prediction_image_average = prediction_image_average + (
prediction_image - prediction_image_average) / (d + 1)
current_start_slice = current_end_slice
probability_image = ants.apply_ants_transform_to_image(
ants.invert_ants_transform(xfrm), prediction_image_average,
flair_preprocessed)
probability_image = ants.copy_image_info(flair, probability_image)
return (probability_image)
def test_get_dimension(self):
tx2d = ants.create_ants_transform(precision='float', dimension=2)
self.assertEqual(tx2d.dimension, 2)
tx3d = ants.create_ants_transform(precision='float', dimension=3)
self.assertEqual(tx3d.dimension, 3)
def brain_extraction(image,
modality="t1",
antsxnet_cache_directory=None,
verbose=False):
"""
Perform brain extraction using U-net and ANTs-based training data. "NoBrainer"
is also possible where brain extraction uses U-net and FreeSurfer training data
ported from the
https://github.com/neuronets/nobrainer-models
Arguments
---------
image : ANTsImage
input image (or list of images for multi-modal scenarios).
modality : string
Modality image type. Options include:
* "t1": T1-weighted MRI---ANTs-trained. Update from "t1v0".
* "t1v0": T1-weighted MRI---ANTs-trained.
* "t1nobrainer": T1-weighted MRI---FreeSurfer-trained: h/t Satra Ghosh and Jakub Kaczmarzyk.
* "t1combined": Brian's combination of "t1" and "t1nobrainer". One can also specify
"t1combined[X]" where X is the morphological radius. X = 12 by default.
* "flair": FLAIR MRI.
* "t2": T2 MRI.
* "bold": 3-D BOLD MRI.
* "fa": Fractional anisotropy.
* "t1t2infant": Combined T1-w/T2-w infant MRI h/t Martin Styner.
* "t1infant": T1-w infant MRI h/t Martin Styner.
* "t2infant": T2-w infant MRI h/t Martin Styner.
antsxnet_cache_directory : string
Destination directory for storing the downloaded template and model weights.
Since these can be resused, if is None, these data will be downloaded to a
~/.keras/ANTsXNet/.
verbose : boolean
Print progress to the screen.
Returns
-------
ANTs probability brain mask image.
Example
-------
>>> probability_brain_mask = brain_extraction(brain_image, modality="t1")
"""
from ..architectures import create_unet_model_3d
from ..utilities import get_pretrained_network
from ..utilities import get_antsxnet_data
from ..architectures import create_nobrainer_unet_model_3d
classes = ("background", "brain")
number_of_classification_labels = len(classes)
channel_size = 1
if isinstance(image, list):
channel_size = len(image)
if antsxnet_cache_directory == None:
antsxnet_cache_directory = "ANTsXNet"
input_images = list()
if channel_size == 1:
input_images.append(image)
else:
input_images = image
if input_images[0].dimension != 3:
raise ValueError("Image dimension must be 3.")
if "t1combined" in modality:
brain_extraction_t1 = brain_extraction(
image,
modality="t1",
antsxnet_cache_directory=antsxnet_cache_directory,
verbose=verbose)
brain_mask = ants.iMath_get_largest_component(
ants.threshold_image(brain_extraction_t1, 0.5, 10000))
# Need to change with voxel resolution
morphological_radius = 12
if '[' in modality and ']' in modality:
morphological_radius = int(modality.split("[")[1].split("]")[0])
brain_extraction_t1nobrainer = brain_extraction(
image * ants.iMath_MD(brain_mask, radius=morphological_radius),
modality="t1nobrainer",
antsxnet_cache_directory=antsxnet_cache_directory,
verbose=verbose)
brain_extraction_combined = ants.iMath_fill_holes(
ants.iMath_get_largest_component(brain_extraction_t1nobrainer *
brain_mask))
brain_extraction_combined = brain_extraction_combined + ants.iMath_ME(
brain_mask, morphological_radius) + brain_mask
return (brain_extraction_combined)
if modality != "t1nobrainer":
#####################
#
# ANTs-based
#
#####################
weights_file_name_prefix = None
if modality == "t1v0":
weights_file_name_prefix = "brainExtraction"
elif modality == "t1":
weights_file_name_prefix = "brainExtractionT1"
elif modality == "t2":
weights_file_name_prefix = "brainExtractionT2"
elif modality == "flair":
weights_file_name_prefix = "brainExtractionFLAIR"
elif modality == "bold":
weights_file_name_prefix = "brainExtractionBOLD"
elif modality == "fa":
weights_file_name_prefix = "brainExtractionFA"
elif modality == "t1t2infant":
weights_file_name_prefix = "brainExtractionInfantT1T2"
elif modality == "t1infant":
weights_file_name_prefix = "brainExtractionInfantT1"
elif modality == "t2infant":
weights_file_name_prefix = "brainExtractionInfantT2"
else:
raise ValueError("Unknown modality type.")
weights_file_name = get_pretrained_network(
weights_file_name_prefix,
antsxnet_cache_directory=antsxnet_cache_directory)
if verbose == True:
print("Brain extraction: retrieving template.")
reorient_template_file_name_path = get_antsxnet_data(
"S_template3", antsxnet_cache_directory=antsxnet_cache_directory)
reorient_template = ants.image_read(reorient_template_file_name_path)
resampled_image_size = reorient_template.shape
if modality == "t1":
classes = ("background", "head", "brain")
number_of_classification_labels = len(classes)
unet_model = create_unet_model_3d(
(*resampled_image_size, channel_size),
number_of_outputs=number_of_classification_labels,
number_of_layers=4,
number_of_filters_at_base_layer=8,
dropout_rate=0.0,
convolution_kernel_size=(3, 3, 3),
deconvolution_kernel_size=(2, 2, 2),
weight_decay=1e-5)
unet_model.load_weights(weights_file_name)
if verbose == True:
print("Brain extraction: normalizing image to the template.")
center_of_mass_template = ants.get_center_of_mass(reorient_template)
center_of_mass_image = ants.get_center_of_mass(input_images[0])
translation = np.asarray(center_of_mass_image) - np.asarray(
center_of_mass_template)
xfrm = ants.create_ants_transform(
transform_type="Euler3DTransform",
center=np.asarray(center_of_mass_template),
translation=translation)
batchX = np.zeros((1, *resampled_image_size, channel_size))
for i in range(len(input_images)):
warped_image = ants.apply_ants_transform_to_image(
xfrm, input_images[i], reorient_template)
warped_array = warped_image.numpy()
batchX[0, :, :, :, i] = (warped_array -
warped_array.mean()) / warped_array.std()
if verbose == True:
print("Brain extraction: prediction and decoding.")
predicted_data = unet_model.predict(batchX, verbose=verbose)
origin = reorient_template.origin
spacing = reorient_template.spacing
direction = reorient_template.direction
probability_images_array = list()
probability_images_array.append(
ants.from_numpy(np.squeeze(predicted_data[0, :, :, :, 0]),
origin=origin,
spacing=spacing,
direction=direction))
probability_images_array.append(
ants.from_numpy(np.squeeze(predicted_data[0, :, :, :, 1]),
origin=origin,
spacing=spacing,
direction=direction))
if modality == "t1":
probability_images_array.append(
ants.from_numpy(np.squeeze(predicted_data[0, :, :, :, 2]),
origin=origin,
spacing=spacing,
direction=direction))
if verbose == True:
print(
"Brain extraction: renormalize probability mask to native space."
)
probability_image = ants.apply_ants_transform_to_image(
ants.invert_ants_transform(xfrm),
probability_images_array[number_of_classification_labels - 1],
input_images[0])
return (probability_image)
else:
#####################
#
# NoBrainer
#
#####################
if verbose == True:
print("NoBrainer: generating network.")
model = create_nobrainer_unet_model_3d((None, None, None, 1))
weights_file_name = get_pretrained_network(
"brainExtractionNoBrainer",
antsxnet_cache_directory=antsxnet_cache_directory)
model.load_weights(weights_file_name)
if verbose == True:
print(
"NoBrainer: preprocessing (intensity truncation and resampling)."
)
image_array = image.numpy()
image_robust_range = np.quantile(
image_array[np.where(image_array != 0)], (0.02, 0.98))
threshold_value = 0.10 * (image_robust_range[1] - image_robust_range[0]
) + image_robust_range[0]
thresholded_mask = ants.threshold_image(image, -10000, threshold_value,
0, 1)
thresholded_image = image * thresholded_mask
image_resampled = ants.resample_image(thresholded_image,
(256, 256, 256),
use_voxels=True)
image_array = np.expand_dims(image_resampled.numpy(), axis=0)
image_array = np.expand_dims(image_array, axis=-1)
if verbose == True:
print("NoBrainer: predicting mask.")
brain_mask_array = np.squeeze(
model.predict(image_array, verbose=verbose))
brain_mask_resampled = ants.copy_image_info(
image_resampled, ants.from_numpy(brain_mask_array))
brain_mask_image = ants.resample_image(brain_mask_resampled,
image.shape,
use_voxels=True,
interp_type=1)
spacing = ants.get_spacing(image)
spacing_product = spacing[0] * spacing[1] * spacing[2]
minimum_brain_volume = round(649933.7 / spacing_product)
brain_mask_labeled = ants.label_clusters(brain_mask_image,
minimum_brain_volume)
return (brain_mask_labeled)
