def planewise_affine(fixed, moving, return_transforms=False):
zshift = get_zshift(fixed, moving)
fixed = to_numpy(fixed)
moving = to_numpy(moving)
size_z = fixed.shape[0]
warped = np.zeros_like(fixed)
transforms = [None]*size_z
for z in prog_percent(list(range(max((0, -zshift)), min((size_z, -zshift + size_z))))):
mov = ants.from_numpy(moving[z + zshift].swapaxes(0, 1))
fix = ants.from_numpy(fixed[z].swapaxes(0, 1))
res = ants.registration(mov, fix,
type_of_transform='Affine',
reg_iterations=[500, 500, 500],
grad_step=.1,
verbose=True)
t = ants.read_transform(res['fwdtransforms'][0])
transforms[z] = t
trans = ants.apply_ants_transform_to_image(t, mov, fix)
warped[z] = trans.numpy().swapaxes(0, 1)
if return_transforms:
return warped, (transforms, zshift)
return warped
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)
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("")
shape_initial_stage = (160, 160, 128)
print(" Initial step 1: bias correction.")
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 randomly_transform_image_data(
reference_image,
input_image_list,
segmentation_image_list=None,
number_of_simulations=10,
transform_type='affine',
sd_affine=0.02,
deformation_transform_type="bspline",
number_of_random_points=1000,
sd_noise=10.0,
number_of_fitting_levels=4,
mesh_size=1,
sd_smoothing=4.0,
input_image_interpolator='linear',
segmentation_image_interpolator='nearestNeighbor'):
"""
Randomly transform image data (optional: with corresponding segmentations).
Apply rigid, affine and/or deformable maps to an input set of training
images. The reference image domain defines the space in which this
happens.
Arguments
---------
reference_image : ANTsImage
Defines the spatial domain for all output images. If the input images do
not match the spatial domain of the reference image, we internally
resample the target to the reference image. This could have unexpected
consequences. Resampling to the reference domain is performed by testing
using ants.image_physical_space_consistency then calling
ants.resample_image_to_target with failure.
input_image_list : list of lists of ANTsImages
List of lists of input images to warp. The internal list sets contain one
or more images (per subject) which are assumed to be mutually aligned. The
outer list contains multiple subject lists which are randomly sampled to
produce output image list.
segmentation_image_list : list of ANTsImages
List of segmentation images corresponding to the input image list (optional).
number_of_simulations : integer
Number of output images.
transform_type : string
One of the following options: "translation", "rigid", "scaleShear", "affine",
"deformation", "affineAndDeformation".
sd_affine : float
Parameter dictating deviation amount from identity for random linear
transformations.
deformation_transform_type : string
"bspline" or "exponential".
number_of_random_points : integer
Number of displacement points for the deformation field.
sd_noise : float
Standard deviation of the displacement field.
number_of_fitting_levels : integer
Number of fitting levels (bspline deformation only).
mesh_size : int or n-D tuple
Determines fitting resolution (bspline deformation only).
sd_smoothing : float
Standard deviation of the Gaussian smoothing in mm (exponential field only).
input_image_interpolator : string
One of the following options "linear", "gaussian", "bspline".
segmentation_image_interpolator : string
One of the following options "nearestNeighbor" or "genericLabel".
Returns
-------
list of lists of transformed images
Example
-------
>>> import ants
>>> image1_list = list()
>>> image1_list.append(ants.image_read(ants.get_ants_data("r16")))
>>> image2_list = list()
>>> image2_list.append(ants.image_read(ants.get_ants_data("r64")))
>>> input_segmentations = list()
>>> input_segmentations.append(ants.threshold_image(image1, "Otsu", 3))
>>> input_segmentations.append(ants.threshold_image(image2, "Otsu", 3))
>>> input_images = list()
>>> input_images.append(image1_list)
>>> input_images.append(image2_list)
>>> data = antspynet.randomly_transform_image_data(image1,
>>> input_images, input_segmentations, sd_affine=0.02,
>>> transform_type = "affineAndDeformation" )
"""
def polar_decomposition(X):
U, d, V = np.linalg.svd(X, full_matrices=False)
P = np.matmul(U, np.matmul(np.diag(d), np.transpose(U)))
Z = np.matmul(U, np.transpose(V))
if np.linalg.det(Z) < 0:
Z = -Z
return ({"P": P, "Z": Z, "Xtilde": np.matmul(P, Z)})
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 create_random_displacement_field_transform(
image,
field_type="bspline",
number_of_random_points=1000,
sd_noise=10.0,
number_of_fitting_levels=4,
mesh_size=1,
sd_smoothing=4.0):
displacement_field = ants.simulate_displacement_field(
image,
field_type=field_type,
number_of_random_points=number_of_random_points,
sd_noise=sd_noise,
enforce_stationary_boundary=True,
number_of_fitting_levels=number_of_fitting_levels,
mesh_size=mesh_size,
sd_smoothing=sd_smoothing)
return (ants.transform_from_displacement_field(displacement_field))
admissible_transforms = ("translation", "rigid", "scaleShear", "affine",
"affineAndDeformation", "deformation")
if not transform_type in admissible_transforms:
raise ValueError(
"The specified transform is not a possible option. Please see help menu."
)
# Get the fixed parameters from the reference image.
fixed_parameters = ants.get_center_of_mass(reference_image)
number_of_subjects = len(input_image_list)
random_indices = np.random.choice(number_of_subjects,
size=number_of_simulations,
replace=True)
simulated_image_list = list()
simulated_segmentation_image_list = list()
simulated_transforms = list()
for i in range(number_of_simulations):
single_subject_image_list = input_image_list[random_indices[i]]
single_subject_segmentation_image = None
if segmentation_image_list is not None:
single_subject_segmentation_image = segmentation_image_list[
random_indices[i]]
if ants.image_physical_space_consistency(
reference_image, single_subject_image_list[0]) is False:
for j in range(len(single_subject_image_list)):
single_subject_image_list.append(
ants.resample_image_to_target(
single_subject_image_list[j],
reference_image,
interp_type=input_image_interpolator))
if single_subject_segmentation_image is not None:
single_subject_segmentation_image = \
ants.resample_image_to_target(single_subject_segmentation_image, reference_image,
interp_type=segmentation_image_interpolator)
transforms = list()
if transform_type == 'deformation':
deformable_transform = create_random_displacement_field_transform(
reference_image, deformation_transform_type,
number_of_random_points, sd_noise, number_of_fitting_levels,
mesh_size, sd_smoothing)
transforms.append(deformable_transform)
elif transform_type == 'affineAndDeformation':
deformable_transform = create_random_displacement_field_transform(
reference_image, deformation_transform_type,
number_of_random_points, sd_noise, number_of_fitting_levels,
mesh_size, sd_smoothing)
linear_transform = create_random_linear_transform(
reference_image, fixed_parameters, 'affine', sd_affine)
transforms.append(deformable_transform)
transforms.append(linear_transform)
else:
linear_transform = create_random_linear_transform(
reference_image, fixed_parameters, transform_type, sd_affine)
transforms.append(linear_transform)
simulated_transforms.append(ants.compose_ants_transforms(transforms))
single_subject_simulated_image_list = list()
for j in range(len(single_subject_image_list)):
single_subject_simulated_image_list.append(
ants.apply_ants_transform_to_image(
simulated_transforms[i],
single_subject_image_list[j],
reference=reference_image))
simulated_image_list.append(single_subject_simulated_image_list)
if single_subject_segmentation_image is not None:
simulated_segmentation_image_list.append(
ants.apply_ants_transform_to_image(
simulated_transforms[i],
single_subject_segmentation_image,
reference=reference_image))
if segmentation_image_list is None:
return ({
'simulated_images': simulated_image_list,
'simulated_transforms': simulated_transforms
})
else:
return ({
'simulated_images': simulated_image_list,
'simulated_segmentation_images': simulated_segmentation_image_list,
'simulated_transforms': simulated_transforms
})
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)
image = ants.image_read(input_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,
interpolation='linear')
end_time = time.time()
elapsed_time = end_time - start_time
print(" (elapsed time: ", elapsed_time, " seconds)")
batchX = np.expand_dims(warped_image.numpy(), axis=0)
batchX = np.expand_dims(batchX, axis=-1)
batchX = (batchX - batchX.mean()) / batchX.std()
print("Prediction and decoding")
start_time = time.time()
predicted_data = unet_model.predict(batchX, verbose=0)
origin = warped_image.origin
spacing = warped_image.spacing
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 ew_david(flair,
t1,
do_preprocessing=True,
which_model="sysu",
which_axes=2,
number_of_simulations=0,
sd_affine=0.01,
antsxnet_cache_directory=None,
verbose=False):
"""
Perform White matter hyperintensity probabilistic segmentation
using deep learning
Preprocessing on the training data consisted of:
* n4 bias correction,
* intensity truncation,
* brain extraction, and
* affine registration to MNI.
The input T1 should undergo the same steps. If the input T1 is the raw
T1, these steps can be performed by the internal preprocessing, i.e. set
\code{do_preprocessing = True}
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, intensity truncation, brain extraction.
which_model : string
one of:
* "sysu" -- same as the original sysu network (without site specific preprocessing),
* "sysu-ri" -- same as "sysu" but using ranked intensity scaling for input images,
* "sysuWithAttention" -- "sysu" with attention gating,
* "sysuWithAttentionAndSite" -- "sysu" with attention gating with site branch (see "sysuWithSite"),
* "sysuPlus" -- "sysu" with attention gating and nn-Unet activation,
* "sysuPlusSeg" -- "sysuPlus" with deep_atropos segmentation in an additional channel, and
* "sysuWithSite" -- "sysu" with global pooling on encoding channels to predict "site".
* "sysuPlusSegWithSite" -- "sysuPlusSeg" combined with "sysuWithSite"
In addition to both modalities, all models have T1-only and flair-only variants except
for "sysuPlusSeg" (which only has a T1-only variant) or "sysu-ri" (which has neither single
modality variant).
which_axes : string or scalar or tuple/vector
apply 2-D model to 1 or more axes. In addition to a scalar
or vector, e.g., which_axes = (0, 2), one can use "max" for the
axis with maximum anisotropy (default) or "all" for all axes.
number_of_simulations : integer
Number of random affine perturbations to transform the input.
sd_affine : float
Define the standard deviation of the affine transformation parameter.
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_unet_model_2d
from ..utilities import deep_atropos
from ..utilities import get_pretrained_network
from ..utilities import preprocess_brain_image
from ..utilities import randomly_transform_image_data
from ..utilities import pad_or_crop_image_to_size
do_t1_only = False
do_flair_only = False
if flair is None and t1 is not None:
do_t1_only = True
elif flair is not None and t1 is None:
do_flair_only = True
use_t1_segmentation = False
if "Seg" in which_model:
if do_flair_only:
raise ValueError("Segmentation requires T1.")
else:
use_t1_segmentation = True
if use_t1_segmentation and do_preprocessing == False:
raise ValueError(
"Using the t1 segmentation requires do_preprocessing=True.")
if antsxnet_cache_directory == None:
antsxnet_cache_directory = "ANTsXNet"
do_slicewise = True
if do_slicewise == False:
raise ValueError("Not available.")
# ################################
# #
# # Preprocess images
# #
# ################################
# t1_preprocessed = t1
# t1_preprocessing = None
# if do_preprocessing == True:
# t1_preprocessing = preprocess_brain_image(t1,
# truncate_intensity=(0.01, 0.99),
# brain_extraction_modality="t1",
# template="croppedMni152",
# template_transform_type="antsRegistrationSyNQuickRepro[a]",
# do_bias_correction=True,
# do_denoising=False,
# antsxnet_cache_directory=antsxnet_cache_directory,
# verbose=verbose)
# t1_preprocessed = t1_preprocessing["preprocessed_image"] * t1_preprocessing['brain_mask']
# flair_preprocessed = flair
# if do_preprocessing == True:
# flair_preprocessing = preprocess_brain_image(flair,
# truncate_intensity=(0.01, 0.99),
# brain_extraction_modality="t1",
# do_bias_correction=True,
# do_denoising=False,
# antsxnet_cache_directory=antsxnet_cache_directory,
# verbose=verbose)
# flair_preprocessed = ants.apply_transforms(fixed=t1_preprocessed,
# moving=flair_preprocessing["preprocessed_image"],
# transformlist=t1_preprocessing['template_transforms']['fwdtransforms'])
# flair_preprocessed = flair_preprocessed * t1_preprocessing['brain_mask']
# ################################
# #
# # Build model and load weights
# #
# ################################
# patch_size = (112, 112, 112)
# stride_length = (t1_preprocessed.shape[0] - patch_size[0],
# t1_preprocessed.shape[1] - patch_size[1],
# t1_preprocessed.shape[2] - patch_size[2])
# classes = ("background", "wmh" )
# number_of_classification_labels = len(classes)
# labels = (0, 1)
# image_modalities = ("T1", "FLAIR")
# channel_size = len(image_modalities)
# unet_model = create_unet_model_3d((*patch_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 = (3, 3, 3), deconvolution_kernel_size = (2, 2, 2),
# weight_decay = 1e-5, additional_options=("attentionGating"))
# weights_file_name = get_pretrained_network("ewDavidWmhSegmentationWeights",
# antsxnet_cache_directory=antsxnet_cache_directory)
# unet_model.load_weights(weights_file_name)
# ################################
# #
# # Do prediction and normalize to native space
# #
# ################################
# if verbose == True:
# print("ew_david: prediction.")
# batchX = np.zeros((8, *patch_size, channel_size))
# t1_preprocessed = (t1_preprocessed - t1_preprocessed.mean()) / t1_preprocessed.std()
# t1_patches = extract_image_patches(t1_preprocessed, patch_size=patch_size,
# max_number_of_patches="all", stride_length=stride_length,
# return_as_array=True)
# batchX[:,:,:,:,0] = t1_patches
# flair_preprocessed = (flair_preprocessed - flair_preprocessed.mean()) / flair_preprocessed.std()
# flair_patches = extract_image_patches(flair_preprocessed, patch_size=patch_size,
# max_number_of_patches="all", stride_length=stride_length,
# return_as_array=True)
# batchX[:,:,:,:,1] = flair_patches
# predicted_data = unet_model.predict(batchX, verbose=verbose)
# probability_images = list()
# for i in range(len(labels)):
# print("Reconstructing image", classes[i])
# reconstructed_image = reconstruct_image_from_patches(predicted_data[:,:,:,:,i],
# domain_image=t1_preprocessed, stride_length=stride_length)
# if do_preprocessing == True:
# probability_images.append(ants.apply_transforms(fixed=t1,
# moving=reconstructed_image,
# transformlist=t1_preprocessing['template_transforms']['invtransforms'],
# whichtoinvert=[True], interpolator="linear", verbose=verbose))
# else:
# probability_images.append(reconstructed_image)
# return(probability_images[1])
else: # do_slicewise
################################
#
# Preprocess images
#
################################
use_rank_intensity_scaling = False
if "-ri" in which_model:
use_rank_intensity_scaling = True
t1_preprocessed = None
t1_preprocessing = None
brain_mask = None
if t1 is not None:
if do_preprocessing == True:
t1_preprocessing = preprocess_brain_image(
t1,
truncate_intensity=(0.01, 0.995),
brain_extraction_modality="t1",
do_bias_correction=False,
do_denoising=False,
antsxnet_cache_directory=antsxnet_cache_directory,
verbose=verbose)
brain_mask = ants.threshold_image(
t1_preprocessing["brain_mask"], 0.5, 1, 1, 0)
t1_preprocessed = t1_preprocessing["preprocessed_image"]
t1_segmentation = None
if use_t1_segmentation:
atropos_seg = deep_atropos(t1,
do_preprocessing=True,
verbose=verbose)
t1_segmentation = atropos_seg['segmentation_image']
flair_preprocessed = None
if flair is not None:
flair_preprocessed = flair
if do_preprocessing == True:
if brain_mask is None:
flair_preprocessing = preprocess_brain_image(
flair,
truncate_intensity=(0.01, 0.995),
brain_extraction_modality="flair",
do_bias_correction=False,
do_denoising=False,
antsxnet_cache_directory=antsxnet_cache_directory,
verbose=verbose)
brain_mask = ants.threshold_image(
flair_preprocessing["brain_mask"], 0.5, 1, 1, 0)
else:
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"]
if t1_preprocessed is not None:
t1_preprocessed = t1_preprocessed * brain_mask
if flair_preprocessed is not None:
flair_preprocessed = flair_preprocessed * brain_mask
if t1_preprocessed is not None:
resampling_params = list(ants.get_spacing(t1_preprocessed))
else:
resampling_params = list(ants.get_spacing(flair_preprocessed))
do_resampling = False
for d in range(len(resampling_params)):
if resampling_params[d] < 0.8:
resampling_params[d] = 1.0
do_resampling = True
resampling_params = tuple(resampling_params)
if do_resampling:
if flair_preprocessed is not None:
flair_preprocessed = ants.resample_image(flair_preprocessed,
resampling_params,
use_voxels=False,
interp_type=0)
if t1_preprocessed is not None:
t1_preprocessed = ants.resample_image(t1_preprocessed,
resampling_params,
use_voxels=False,
interp_type=0)
if t1_segmentation is not None:
t1_segmentation = ants.resample_image(t1_segmentation,
resampling_params,
use_voxels=False,
interp_type=1)
if brain_mask is not None:
brain_mask = ants.resample_image(brain_mask,
resampling_params,
use_voxels=False,
interp_type=1)
################################
#
# Build model and load weights
#
################################
template_size = (208, 208)
image_modalities = ("T1", "FLAIR")
if do_flair_only:
image_modalities = ("FLAIR", )
elif do_t1_only:
image_modalities = ("T1", )
if use_t1_segmentation:
image_modalities = (*image_modalities, "T1Seg")
channel_size = len(image_modalities)
unet_model = None
if which_model == "sysu" or which_model == "sysu-ri":
unet_model = create_unet_model_2d(
(*template_size, channel_size),
number_of_outputs=1,
mode="sigmoid",
number_of_filters=(64, 96, 128, 256, 512),
dropout_rate=0.0,
convolution_kernel_size=(3, 3),
deconvolution_kernel_size=(2, 2),
weight_decay=0,
additional_options=("initialConvolutionKernelSize[5]", ))
elif which_model == "sysuWithAttention":
unet_model = create_unet_model_2d(
(*template_size, channel_size),
number_of_outputs=1,
mode="sigmoid",
number_of_filters=(64, 96, 128, 256, 512),
dropout_rate=0.0,
convolution_kernel_size=(3, 3),
deconvolution_kernel_size=(2, 2),
weight_decay=0,
additional_options=("attentionGating",
"initialConvolutionKernelSize[5]"))
elif which_model == "sysuWithAttentionAndSite":
unet_model = create_unet_model_2d(
(*template_size, channel_size),
number_of_outputs=1,
mode="sigmoid",
scalar_output_size=3,
scalar_output_activation="softmax",
number_of_filters=(64, 96, 128, 256, 512),
dropout_rate=0.0,
convolution_kernel_size=(3, 3),
deconvolution_kernel_size=(2, 2),
weight_decay=0,
additional_options=("attentionGating",
"initialConvolutionKernelSize[5]"))
elif which_model == "sysuWithSite":
unet_model = create_unet_model_2d(
(*template_size, channel_size),
number_of_outputs=1,
mode="sigmoid",
scalar_output_size=3,
scalar_output_activation="softmax",
number_of_filters=(64, 96, 128, 256, 512),
dropout_rate=0.0,
convolution_kernel_size=(3, 3),
deconvolution_kernel_size=(2, 2),
weight_decay=0,
additional_options=("initialConvolutionKernelSize[5]", ))
elif which_model == "sysuPlusSegWithSite":
unet_model = create_unet_model_2d(
(*template_size, channel_size),
number_of_outputs=1,
mode="sigmoid",
scalar_output_size=3,
scalar_output_activation="softmax",
number_of_filters=(64, 96, 128, 256, 512),
dropout_rate=0.0,
convolution_kernel_size=(3, 3),
deconvolution_kernel_size=(2, 2),
weight_decay=0,
additional_options=("nnUnetActivationStyle", "attentionGating",
"initialConvolutionKernelSize[5]"))
else:
unet_model = create_unet_model_2d(
(*template_size, channel_size),
number_of_outputs=1,
mode="sigmoid",
number_of_filters=(64, 96, 128, 256, 512),
dropout_rate=0.0,
convolution_kernel_size=(3, 3),
deconvolution_kernel_size=(2, 2),
weight_decay=0,
additional_options=("nnUnetActivationStyle", "attentionGating",
"initialConvolutionKernelSize[5]"))
if verbose == True:
print("ewDavid: retrieving model weights.")
weights_file_name = None
if which_model == "sysu" and flair is not None and t1 is not None:
weights_file_name = get_pretrained_network(
"ewDavidSysu",
antsxnet_cache_directory=antsxnet_cache_directory)
elif which_model == "sysu-ri" and flair is not None and t1 is not None:
weights_file_name = get_pretrained_network(
"ewDavidSysuRankedIntensity",
antsxnet_cache_directory=antsxnet_cache_directory)
elif which_model == "sysu" and flair is None and t1 is not None:
weights_file_name = get_pretrained_network(
"ewDavidSysuT1Only",
antsxnet_cache_directory=antsxnet_cache_directory)
elif which_model == "sysu" and flair is not None and t1 is None:
weights_file_name = get_pretrained_network(
"ewDavidSysuFlairOnly",
antsxnet_cache_directory=antsxnet_cache_directory)
elif which_model == "sysuWithAttention" and flair is not None and t1 is not None:
weights_file_name = get_pretrained_network(
"ewDavidSysuWithAttention",
antsxnet_cache_directory=antsxnet_cache_directory)
elif which_model == "sysuWithAttention" and flair is None and t1 is not None:
weights_file_name = get_pretrained_network(
"ewDavidSysuWithAttentionT1Only",
antsxnet_cache_directory=antsxnet_cache_directory)
elif which_model == "sysuWithAttention" and flair is not None and t1 is None:
weights_file_name = get_pretrained_network(
"ewDavidSysuWithAttentionFlairOnly",
antsxnet_cache_directory=antsxnet_cache_directory)
elif which_model == "sysuWithAttentionAndSite" and flair is not None and t1 is not None:
weights_file_name = get_pretrained_network(
"ewDavidSysuWithAttentionAndSite",
antsxnet_cache_directory=antsxnet_cache_directory)
elif which_model == "sysuWithAttentionAndSite" and flair is None and t1 is not None:
weights_file_name = get_pretrained_network(
"ewDavidSysuWithAttentionAndSiteT1Only",
antsxnet_cache_directory=antsxnet_cache_directory)
elif which_model == "sysuWithAttentionAndSite" and flair is not None and t1 is None:
weights_file_name = get_pretrained_network(
"ewDavidSysuWithAttentionAndSiteFlairOnly",
antsxnet_cache_directory=antsxnet_cache_directory)
elif which_model == "sysuPlus" and flair is not None and t1 is not None:
weights_file_name = get_pretrained_network(
"ewDavidSysuPlus",
antsxnet_cache_directory=antsxnet_cache_directory)
elif which_model == "sysuPlus" and flair is None and t1 is not None:
weights_file_name = get_pretrained_network(
"ewDavidSysuPlusT1Only",
antsxnet_cache_directory=antsxnet_cache_directory)
elif which_model == "sysuPlus" and flair is not None and t1 is None:
weights_file_name = get_pretrained_network(
"ewDavidSysuPlusFlairOnly",
antsxnet_cache_directory=antsxnet_cache_directory)
elif which_model == "sysuPlusSeg" and flair is not None and t1 is not None:
weights_file_name = get_pretrained_network(
"ewDavidSysuPlusSeg",
antsxnet_cache_directory=antsxnet_cache_directory)
elif which_model == "sysuPlusSeg" and flair is None and t1 is not None:
weights_file_name = get_pretrained_network(
"ewDavidSysuPlusSegT1Only",
antsxnet_cache_directory=antsxnet_cache_directory)
elif which_model == "sysuPlusSegWithSite" and flair is not None and t1 is not None:
weights_file_name = get_pretrained_network(
"ewDavidSysuPlusSegWithSite",
antsxnet_cache_directory=antsxnet_cache_directory)
elif which_model == "sysuPlusSegWithSite" and flair is None and t1 is not None:
weights_file_name = get_pretrained_network(
"ewDavidSysuPlusSegWithSiteT1Only",
antsxnet_cache_directory=antsxnet_cache_directory)
elif which_model == "sysuWithSite" and flair is not None and t1 is not None:
weights_file_name = get_pretrained_network(
"ewDavidSysuWithSite",
antsxnet_cache_directory=antsxnet_cache_directory)
elif which_model == "sysuWithSite" and flair is None and t1 is not None:
weights_file_name = get_pretrained_network(
"ewDavidSysuWithSiteT1Only",
antsxnet_cache_directory=antsxnet_cache_directory)
elif which_model == "sysuWithSite" and flair is not None and t1 is None:
weights_file_name = get_pretrained_network(
"ewDavidSysuWithSiteFlairOnly",
antsxnet_cache_directory=antsxnet_cache_directory)
else:
raise ValueError(
"Incorrect model specification or image combination.")
unet_model.load_weights(weights_file_name)
################################
#
# Data augmentation and extract slices
#
################################
wmh_probability_image = None
if t1 is not None:
wmh_probability_image = ants.image_clone(t1_preprocessed) * 0
else:
wmh_probability_image = ants.image_clone(flair_preprocessed) * 0
wmh_site = np.array([0, 0, 0])
data_augmentation = None
if number_of_simulations > 0:
if do_flair_only:
data_augmentation = randomly_transform_image_data(
reference_image=flair_preprocessed,
input_image_list=[[flair_preprocessed]],
number_of_simulations=number_of_simulations,
transform_type='affine',
sd_affine=sd_affine,
input_image_interpolator='linear')
elif do_t1_only:
if use_t1_segmentation:
data_augmentation = randomly_transform_image_data(
reference_image=t1_preprocessed,
input_image_list=[[t1_preprocessed]],
segmentation_image_list=[t1_segmentation],
number_of_simulations=number_of_simulations,
transform_type='affine',
sd_affine=sd_affine,
input_image_interpolator='linear',
segmentation_image_interpolator='nearestNeighbor')
else:
data_augmentation = randomly_transform_image_data(
reference_image=t1_preprocessed,
input_image_list=[[t1_preprocessed]],
number_of_simulations=number_of_simulations,
transform_type='affine',
sd_affine=sd_affine,
input_image_interpolator='linear')
else:
if use_t1_segmentation:
data_augmentation = randomly_transform_image_data(
reference_image=t1_preprocessed,
input_image_list=[[
flair_preprocessed, t1_preprocessed
]],
segmentation_image_list=[t1_segmentation],
number_of_simulations=number_of_simulations,
transform_type='affine',
sd_affine=sd_affine,
input_image_interpolator='linear',
segmentation_image_interpolator='nearestNeighbor')
else:
data_augmentation = randomly_transform_image_data(
reference_image=t1_preprocessed,
input_image_list=[[
flair_preprocessed, t1_preprocessed
]],
number_of_simulations=number_of_simulations,
transform_type='affine',
sd_affine=sd_affine,
input_image_interpolator='linear')
dimensions_to_predict = list((0, ))
if which_axes == "max":
spacing = ants.get_spacing(wmh_probability_image)
dimensions_to_predict = (spacing.index(max(spacing)), )
elif which_axes == "all":
dimensions_to_predict = list(range(3))
else:
if isinstance(which_axes, int):
dimensions_to_predict = list((which_axes, ))
else:
dimensions_to_predict = list(which_axes)
total_number_of_slices = 0
for d in range(len(dimensions_to_predict)):
total_number_of_slices += wmh_probability_image.shape[
dimensions_to_predict[d]]
batchX = np.zeros(
(total_number_of_slices, *template_size, channel_size))
for n in range(number_of_simulations + 1):
batch_flair = flair_preprocessed
batch_t1 = t1_preprocessed
batch_t1_segmentation = t1_segmentation
batch_brain_mask = brain_mask
if n > 0:
if do_flair_only:
batch_flair = data_augmentation['simulated_images'][n -
1][0]
batch_brain_mask = ants.apply_ants_transform_to_image(
data_augmentation['simulated_transforms'][n - 1],
brain_mask,
flair_preprocessed,
interpolation="nearestneighbor")
elif do_t1_only:
batch_t1 = data_augmentation['simulated_images'][n - 1][0]
batch_brain_mask = ants.apply_ants_transform_to_image(
data_augmentation['simulated_transforms'][n - 1],
brain_mask,
t1_preprocessed,
interpolation="nearestneighbor")
else:
batch_flair = data_augmentation['simulated_images'][n -
1][0]
batch_t1 = data_augmentation['simulated_images'][n - 1][1]
batch_brain_mask = ants.apply_ants_transform_to_image(
data_augmentation['simulated_transforms'][n - 1],
brain_mask,
flair_preprocessed,
interpolation="nearestneighbor")
if use_t1_segmentation:
batch_t1_segmentation = data_augmentation[
'simulated_segmentation_images'][n - 1]
if use_rank_intensity_scaling:
if batch_t1 is not None:
batch_t1 = ants.rank_intensity(batch_t1,
batch_brain_mask) - 0.5
if batch_flair is not None:
batch_flair = ants.rank_intensity(flair_preprocessed,
batch_brain_mask) - 0.5
else:
if batch_t1 is not None:
batch_t1 = (batch_t1 -
batch_t1[batch_brain_mask == 1].mean()
) / batch_t1[batch_brain_mask == 1].std()
if batch_flair is not None:
batch_flair = (
batch_flair -
batch_flair[batch_brain_mask == 1].mean()
) / batch_flair[batch_brain_mask == 1].std()
slice_count = 0
for d in range(len(dimensions_to_predict)):
number_of_slices = None
if batch_t1 is not None:
number_of_slices = batch_t1.shape[dimensions_to_predict[d]]
else:
number_of_slices = batch_flair.shape[
dimensions_to_predict[d]]
if verbose == True:
print("Extracting slices for dimension ",
dimensions_to_predict[d])
for i in range(number_of_slices):
brain_mask_slice = pad_or_crop_image_to_size(
ants.slice_image(batch_brain_mask,
dimensions_to_predict[d], i),
template_size)
channel_count = 0
if batch_flair is not None:
flair_slice = pad_or_crop_image_to_size(
ants.slice_image(batch_flair,
dimensions_to_predict[d], i),
template_size)
flair_slice[brain_mask_slice == 0] = 0
batchX[slice_count, :, :,
channel_count] = flair_slice.numpy()
channel_count += 1
if batch_t1 is not None:
t1_slice = pad_or_crop_image_to_size(
ants.slice_image(batch_t1,
dimensions_to_predict[d], i),
template_size)
t1_slice[brain_mask_slice == 0] = 0
batchX[slice_count, :, :,
channel_count] = t1_slice.numpy()
channel_count += 1
if t1_segmentation is not None:
t1_segmentation_slice = pad_or_crop_image_to_size(
ants.slice_image(batch_t1_segmentation,
dimensions_to_predict[d], i),
template_size)
t1_segmentation_slice[brain_mask_slice == 0] = 0
batchX[slice_count, :, :,
channel_count] = t1_segmentation_slice.numpy(
) / 6 - 0.5
slice_count += 1
################################
#
# Do prediction and then restack into the image
#
################################
if verbose == True:
if n == 0:
print("Prediction")
else:
print("Prediction (simulation " + str(n) + ")")
prediction = unet_model.predict(batchX, verbose=verbose)
permutations = list()
permutations.append((0, 1, 2))
permutations.append((1, 0, 2))
permutations.append((1, 2, 0))
prediction_image_average = ants.image_clone(
wmh_probability_image) * 0
current_start_slice = 0
for d in range(len(dimensions_to_predict)):
current_end_slice = current_start_slice + wmh_probability_image.shape[
dimensions_to_predict[d]]
which_batch_slices = range(current_start_slice,
current_end_slice)
if isinstance(prediction, list):
prediction_per_dimension = prediction[0][
which_batch_slices, :, :, 0]
else:
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(
wmh_probability_image,
pad_or_crop_image_to_size(
ants.from_numpy(prediction_array),
wmh_probability_image.shape))
prediction_image_average = prediction_image_average + (
prediction_image - prediction_image_average) / (d + 1)
current_start_slice = current_end_slice
wmh_probability_image = wmh_probability_image + (
prediction_image_average - wmh_probability_image) / (n + 1)
if isinstance(prediction, list):
wmh_site = wmh_site + (np.mean(prediction[1], axis=0) -
wmh_site) / (n + 1)
if do_resampling:
if t1 is not None:
wmh_probability_image = ants.resample_image_to_target(
wmh_probability_image, t1)
if flair is not None:
wmh_probability_image = ants.resample_image_to_target(
wmh_probability_image, flair)
if isinstance(prediction, list):
return ([wmh_probability_image, wmh_site])
else:
return (wmh_probability_image)
def test_apply_ants_transform_to_image(self):
img = ants.image_read(ants.get_ants_data("r16")).clone('float')
tx = ants.new_ants_transform(dimension=2)
tx.set_parameters((0.9, 0, 0, 1.1, 10, 11))
img2 = ants.apply_ants_transform_to_image(tx, img, img)
start_time = time.time()
image = ants.image_read(input_file_name)
image = (image - image.min()) / (image.max() - image.min())
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()
batchX = np.expand_dims(warped_image.numpy(), axis=0)
batchX = np.expand_dims(batchX, axis=-1)
batchX = (batchX - batchX.mean()) / batchX.std()
end_time = time.time()
elapsed_time = end_time - start_time
print(" (elapsed time: ", elapsed_time, " seconds)")
print("Prediction and decoding")
start_time = time.time()
predicted_data = unet_model.predict(batchX, verbose=0)
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)