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)
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 brain_age(t1,
do_preprocessing=True,
number_of_simulations=0,
sd_affine=0.01,
antsxnet_cache_directory=None,
verbose=False):
"""
Estimate BrainAge from a T1-weighted MR image using the DeepBrainNet
architecture and weights described here:
https://github.com/vishnubashyam/DeepBrainNet
and described in the following article:
https://academic.oup.com/brain/article-abstract/doi/10.1093/brain/awaa160/5863667?redirectedFrom=fulltext
Preprocessing on the training data consisted of:
* n4 bias correction,
* 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
do_preprocessing = True
Arguments
---------
t1 : ANTsImage
raw or preprocessed 3-D T1-weighted brain image.
do_preprocessing : boolean
See description above.
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
-------
List consisting of the segmentation image and probability images for
each label.
Example
-------
>>> image = ants.image_read("t1.nii.gz")
>>> deep = brain_age(image)
>>> print("Predicted age: ", deep['predicted_age']
"""
from ..utilities import preprocess_brain_image
from ..utilities import get_pretrained_network
from ..utilities import randomly_transform_image_data
if t1.dimension != 3:
raise ValueError( "Image dimension must be 3." )
if antsxnet_cache_directory == None:
antsxnet_cache_directory = "ANTsXNet"
################################
#
# Preprocess images
#
################################
t1_preprocessed = t1
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=True,
antsxnet_cache_directory=antsxnet_cache_directory,
verbose=verbose)
t1_preprocessed = t1_preprocessing["preprocessed_image"] * t1_preprocessing['brain_mask']
t1_preprocessed = (t1_preprocessed - t1_preprocessed.min()) / (t1_preprocessed.max() - t1_preprocessed.min())
################################
#
# Load model and weights
#
################################
model_weights_file_name = get_pretrained_network("brainAgeDeepBrainNet", antsxnet_cache_directory=antsxnet_cache_directory)
model = keras.models.load_model(model_weights_file_name)
# The paper only specifies that 80 slices are used for prediction. I just picked
# a reasonable range spanning the center of the brain
which_slices = list(range(45, 125))
batchX = np.zeros((len(which_slices), *t1_preprocessed.shape[0:2], 3))
input_image = list()
input_image.append(t1_preprocessed)
input_image_list = list()
input_image_list.append(input_image)
if number_of_simulations > 0:
data_augmentation = randomly_transform_image_data(
reference_image=t1_preprocessed,
input_image_list=input_image_list,
number_of_simulations=number_of_simulations,
transform_type='affine',
sd_affine=sd_affine,
input_image_interpolator='linear')
brain_age_per_slice = None
for i in range(number_of_simulations + 1):
batch_image = t1_preprocessed
if i > 0:
batch_image = data_augmentation['simulated_images'][i-1][0]
for j in range(len(which_slices)):
slice = (ants.slice_image(batch_image, axis=2, idx=which_slices[j])).numpy()
batchX[j,:,:,0] = slice
batchX[j,:,:,1] = slice
batchX[j,:,:,2] = slice
if verbose == True:
print("Brain age (DeepBrainNet): predicting brain age per slice (batch = ", i, ")")
if i == 0:
brain_age_per_slice = model.predict(batchX, verbose=verbose)
else:
prediction = model.predict(batchX, verbose=verbose)
brain_age_per_slice = brain_age_per_slice + (prediction - brain_age_per_slice) / (i+1)
predicted_age = statistics.median(brain_age_per_slice)[0]
return_dict = {'predicted_age' : predicted_age,
'brain_age_per_slice' : brain_age_per_slice}
return(return_dict)
def el_bicho(ventilation_image,
mask,
use_coarse_slices_only=True,
antsxnet_cache_directory=None,
verbose=False):
"""
Perform functional lung segmentation using hyperpolarized gases.
https://pubmed.ncbi.nlm.nih.gov/30195415/
Arguments
---------
ventilation_image : ANTsImage
input ventilation image.
mask : ANTsImage
input mask.
use_coarse_slices_only : boolean
If True, apply network only in the dimension of greatest slice thickness.
If False, apply to all dimensions and average 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
-------
Ventilation segmentation and corresponding probability images
Example
-------
>>> image = ants.image_read("ventilation.nii.gz")
>>> mask = ants.image_read("mask.nii.gz")
>>> lung_seg = el_bicho(image, mask, use_coarse_slices=True, verbose=False)
"""
from ..architectures import create_unet_model_2d
from ..utilities import get_pretrained_network
from ..utilities import pad_or_crop_image_to_size
if ventilation_image.dimension != 3:
raise ValueError("Image dimension must be 3.")
if ventilation_image.shape != mask.shape:
raise ValueError(
"Ventilation image and mask size are not the same size.")
if antsxnet_cache_directory == None:
antsxnet_cache_directory = "ANTsXNet"
################################
#
# Preprocess image
#
################################
template_size = (256, 256)
classes = (0, 1, 2, 3, 4)
number_of_classification_labels = len(classes)
image_modalities = ("Ventilation", "Mask")
channel_size = len(image_modalities)
preprocessed_image = (ventilation_image -
ventilation_image.mean()) / ventilation_image.std()
ants.set_direction(preprocessed_image, np.identity(3))
mask_identity = ants.image_clone(mask)
ants.set_direction(mask_identity, np.identity(3))
################################
#
# Build models and load weights
#
################################
unet_model = create_unet_model_2d(
(*template_size, channel_size),
number_of_outputs=number_of_classification_labels,
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=1e-5,
additional_options=("attentionGating"))
if verbose == True:
print("El Bicho: retrieving model weights.")
weights_file_name = get_pretrained_network(
"elBicho", 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)), )
if use_coarse_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 += 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()
mask_slice = pad_or_crop_image_to_size(
ants.slice_image(mask_identity, dimensions_to_predict[d], i),
template_size)
batchX[slice_count, :, :, 1] = mask_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_images = list()
for l in range(number_of_classification_labels):
probability_images.append(ants.image_clone(mask) * 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)
for l in range(number_of_classification_labels):
prediction_per_dimension = prediction[which_batch_slices, :, :, l]
prediction_array = np.transpose(
np.squeeze(prediction_per_dimension),
permutations[dimensions_to_predict[d]])
prediction_image = ants.copy_image_info(
ventilation_image,
pad_or_crop_image_to_size(ants.from_numpy(prediction_array),
ventilation_image.shape))
probability_images[l] = probability_images[l] + (
prediction_image - probability_images[l]) / (d + 1)
current_start_slice = current_end_slice + 1
################################
#
# Convert probability images to segmentation
#
################################
image_matrix = ants.image_list_to_matrix(
probability_images[1:(len(probability_images))], mask * 0 + 1)
background_foreground_matrix = np.stack([
ants.image_list_to_matrix([probability_images[0]], mask * 0 + 1),
np.expand_dims(np.sum(image_matrix, axis=0), axis=0)
])
foreground_matrix = np.argmax(background_foreground_matrix, axis=0)
segmentation_matrix = (np.argmax(image_matrix, axis=0) +
1) * foreground_matrix
segmentation_image = ants.matrix_to_images(
np.expand_dims(segmentation_matrix, axis=0), mask * 0 + 1)[0]
return_dict = {
'segmentation_image': segmentation_image,
'probability_images': probability_images
}
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)
'sub-10', 'sub-11', 'sub-12', 'sub-13', 'sub-14', 'sub-15', 'sub-16'
])
num_subs = 15
mask_thresh = .1
gm_mask = ants.image_read(
os.path.join(
root,
'sub-01/anat/sub-01_space-MNI152NLin2009cAsym_label-GM_probseg.nii.gz')
)
funcROI = ants.image_read(
os.path.join(
root,
'sub-01/func/sub-01_task-sherlockPart1_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz'
))
epi3d = ants.slice_image(funcROI, axis=3, idx=0)
reg = ants.registration(fixed=epi3d,
moving=gm_mask,
type_of_transform='Rigid',
reg_iterations=[100, 100, 20])
gm_mask_size = reg['warpedmovout']
for sub in sublist:
sub_dir = root + sub + '/func/'
data_dir = root + sub + '/anat/'
sub_mask = ants.image_read(
os.path.join(
data_dir,
sub + '_space-MNI152NLin2009cAsym_label-GM_probseg.nii.gz'))
funcROI = ants.image_read(
os.path.join(
def ew_david(flair,
t1,
do_preprocessing=True,
do_slicewise=True,
antsxnet_cache_directory=None,
verbose=False):
"""
Perform White matter hypterintensity probabilistic segmentation
using deep learning
Preprocessing on the training data consisted of:
* n4 bias correction,
* 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{doPreprocessing = 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?
do_slicewise : boolean
apply 2-D modal along direction of maximal slice thickness.
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 ..architectures import create_unet_model_3d
from ..utilities import get_pretrained_network
from ..utilities import preprocess_brain_image
from ..utilities import extract_image_patches
from ..utilities import reconstruct_image_from_patches
from ..utilities import pad_or_crop_image_to_size
if flair.dimension != 3:
raise ValueError( "Image dimension must be 3." )
if t1.dimension != 3:
raise ValueError( "Image dimension must be 3." )
if antsxnet_cache_directory == None:
antsxnet_cache_directory = "ANTsXNet"
if do_slicewise == False:
################################
#
# Preprocess images
#
################################
t1_preprocessed = t1
t1_preprocessing = None
if do_preprocessing == True:
t1_preprocessing = preprocess_brain_image(t1,
truncate_intensity=(0.01, 0.99),
do_brain_extraction=True,
template="croppedMni152",
template_transform_type="AffineFast",
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),
do_brain_extraction=False,
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, nn_unet_activation_style=False, add_attention_gating=True)
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
#
################################
t1_preprocessed = t1
t1_preprocessing = None
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"]
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"]
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:
flair_preprocessed = ants.resample_image(flair_preprocessed, resampling_params, use_voxels=False, interp_type=0)
t1_preprocessed = ants.resample_image(t1_preprocessed, resampling_params, use_voxels=False, interp_type=0)
flair_preprocessed = (flair_preprocessed - flair_preprocessed.mean()) / flair_preprocessed.std()
t1_preprocessed = (t1_preprocessed - t1_preprocessed.mean()) / t1_preprocessed.std()
################################
#
# Build model and load weights
#
################################
template_size = (256, 256)
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_2d((*template_size, channel_size),
number_of_outputs = number_of_classification_labels,
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 = 1e-5, nn_unet_activation_style=True, add_attention_gating=True)
if verbose == True:
print("ewDavid: retrieving model weights.")
weights_file_name = get_pretrained_network("ewDavidWmhSegmentationSlicewiseWeights",
antsxnet_cache_directory=antsxnet_cache_directory)
unet_model.load_weights(weights_file_name)
################################
#
# Extract slices
#
################################
use_coarse_slices_only = True
spacing = ants.get_spacing(flair_preprocessed)
dimensions_to_predict = (spacing.index(max(spacing)),)
if use_coarse_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.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 = flair_preprocessed.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, dimensions_to_predict[d], i), template_size)
batchX[slice_count,:,:,0] = flair_slice.numpy()
t1_slice = pad_or_crop_image_to_size(ants.slice_image(t1_preprocessed, dimensions_to_predict[d], i), template_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_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(flair_preprocessed) * 0
current_start_slice = 0
for d in range(len(dimensions_to_predict)):
current_end_slice = current_start_slice + flair_preprocessed.shape[dimensions_to_predict[d]] - 1
which_batch_slices = range(current_start_slice, current_end_slice)
prediction_per_dimension = prediction[which_batch_slices,:,:,1]
prediction_array = np.transpose(np.squeeze(prediction_per_dimension), permutations[dimensions_to_predict[d]])
prediction_image = ants.copy_image_info(flair_preprocessed,
pad_or_crop_image_to_size(ants.from_numpy(prediction_array),
flair_preprocessed.shape))
prediction_image_average = prediction_image_average + (prediction_image - prediction_image_average) / (d + 1)
current_start_slice = current_end_slice + 1
if do_resampling:
prediction_image_average = ants.resample_image_to_target(prediction_image_average, flair)
return(prediction_image_average)
def tid_neural_image_assessment(image,
mask=None,
patch_size=101,
stride_length=None,
padding_size=0,
dimensions_to_predict=0,
antsxnet_cache_directory=None,
which_model="tidsQualityAssessment",
verbose=False):
"""
Perform MOS-based assessment of an image.
Use a ResNet architecture to estimate image quality in 2D or 3D using subjective
QC image databases described in
https://www.sciencedirect.com/science/article/pii/S0923596514001490
or
https://doi.org/10.1109/TIP.2020.2967829
where the image assessment is either "global", i.e., a single number or an image
based on the specified patch size. In the 3-D case, neighboring slices are used
for each estimate. Note that parameters should be kept as consistent as possible
in order to enable comparison. Patch size should be roughly 1/12th to 1/4th of
image size to enable locality. A global estimate can be gained by setting
patch_size = "global".
Arguments
---------
image : ANTsImage (2-D or 3-D)
input image.
mask : ANTsImage (2-D or 3-D)
optional mask for designating calculation ROI.
patch_size : integer
prime number of patch_size. 101 is good. Otherwise, choose "global" for a single
global estimate of quality.
stride_length : integer or vector of image dimension length
optional value to speed up computation (typically less than patch size).
padding_size : positive or negative integer or vector of image dimension length
de(padding) to remove edge effects.
dimensions_to_predict : integer or vector
if image dimension is 3, this parameter specifies which dimensions should be used for
prediction. If more than one dimension is specified, the results are averaged.
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
~/.keras/ANTsXNet/.
which_model : string
model type e.g. string tidsQualityAssessment, koniqMS, koniqMS2 or koniqMS3 where
the former predicts mean opinion score (MOS) and MOS standard deviation and
the latter koniq models predict mean opinion score (MOS) and sharpness.
verbose : boolean
Print progress to the screen.
Returns
-------
List of QC results predicting both both human rater's mean and standard
deviation of the MOS ("mean opinion scores") or sharpness depending on the
selected network. Both aggregate and spatial scores are returned, the latter
in the form of an image.
Example
-------
>>> image = ants.image_read(ants.get_data("r16"))
>>> mask = ants.get_mask(image)
>>> tid = tid_neural_image_assessment(image, mask=mask, patch_size=101, stride_length=7)
"""
from ..utilities import get_pretrained_network
from ..utilities import pad_or_crop_image_to_size
from ..utilities import extract_image_patches
from ..utilities import reconstruct_image_from_patches
def is_prime(n):
if n == 2 or n == 3:
return True
if n < 2 or n % 2 == 0:
return False
if n < 9:
return True
if n % 3 == 0:
return False
r = int(n**0.5)
f = 5
while f <= r:
if n % f == 0:
return False
if n % (f + 2) == 0:
return False
f += 6
return True
valid_models = ("tidsQualityAssessment", "koniqMS", "koniqMS2", "koniqMS3")
if not which_model in valid_models:
raise ValueError("Please pass valid model")
if antsxnet_cache_directory == None:
antsxnet_cache_directory = "ANTsXNet"
if verbose == True:
print("Neural QA: retreiving model and weights.")
is_koniq = "koniq" in which_model
model_and_weights_file_name = get_pretrained_network(
which_model, antsxnet_cache_directory=antsxnet_cache_directory)
tid_model = tf.keras.models.load_model(model_and_weights_file_name,
compile=False)
padding_size_vector = padding_size
if isinstance(padding_size, int):
padding_size_vector = np.repeat(padding_size, image.dimension)
elif len(padding_size) == 1:
padding_size_vector = np.repeat(padding_size[0], image.dimension)
if isinstance(dimensions_to_predict, int):
dimensions_to_predict = (dimensions_to_predict, )
padded_image_size = image.shape + padding_size_vector
padded_image = pad_or_crop_image_to_size(image, padded_image_size)
number_of_channels = 3
if stride_length is None and patch_size != "global":
stride_length = round(min(patch_size) / 2)
if image.dimension == 3:
stride_length = (stride_length, stride_length, 1)
###############
#
# Global
#
###############
if patch_size == 'global':
if which_model == "tidsQualityAssessment":
evaluation_image = ants.iMath(padded_image, "Normalize") * 255
if is_koniq:
evaluation_image = ants.iMath(padded_image,
"Normalize") * 2.0 - 1.0
if image.dimension == 2:
batchX = np.zeros((1, evaluation_image.shape, number_of_channels))
for k in range(3):
batchX[0, :, :, k] = evaluation_image.numpy()
predicted_data = tid_model.predict(batchX, verbose=verbose)
if which_model == "tidsQualityAssessment":
return_dict = {
'MOS': None,
'MOS.standardDeviation': None,
'MOS.mean': predicted_data[0, 0],
'MOS.standardDeviationMean': predicted_data[0, 1]
}
return (return_dict)
elif is_koniq:
return_dict = {
'MOS.mean': predicted_data[0, 0],
'sharpness.mean': predicted_data[0, 1]
}
return (return_dict)
elif image.dimension == 3:
mos_mean = 0
mos_standard_deviation = 0
x = tuple(range(image.dimension))
for d in range(len(dimensions_to_predict)):
not_padded_image_size = list(padded_image_size)
del (not_padded_image_size[dimensions_to_predict[d]])
batchX = np.zeros(
(padded_image_size[dimensions_to_predict[d]],
(*tuple(not_padded_image_size)), number_of_channels))
batchX[0, :, :,
0] = (ants.slice_image(
evaluation_image,
axis=0,
idx=dimensions_to_predict[d])).numpy()
batchX[0, :, :,
1] = (ants.slice_image(
evaluation_image,
axis=0,
idx=dimensions_to_predict[d])).numpy()
batchX[0, :, :,
2] = (ants.slice_image(
evaluation_image,
axis=1,
idx=dimensions_to_predict[d])).numpy()
for i in range(1, padded_image_size[dimensions_to_predict[d]] -
1):
batchX[i, :, :, 0] = (ants.slice_image(
evaluation_image,
axis=i - 1,
idx=dimensions_to_predict[d])).numpy()
batchX[i, :, :,
1] = (ants.slice_image(
evaluation_image,
axis=i,
idx=dimensions_to_predict[d])).numpy()
batchX[i, :, :, 2] = (ants.slice_image(
evaluation_image,
axis=i + 1,
idx=dimensions_to_predict[d])).numpy()
batchX[padded_image_size[dimensions_to_predict[d]], :, :,
0] = (ants.slice_image(
evaluation_image,
axis=padded_image_size[dimensions_to_predict[d]] -
1,
idx=dimensions_to_predict[d])).numpy()
batchX[padded_image_size[dimensions_to_predict[d]], :, :,
1] = (ants.slice_image(
evaluation_image,
axis=padded_image_size[dimensions_to_predict[d]],
idx=dimensions_to_predict[d])).numpy()
batchX[padded_image_size[dimensions_to_predict[d]], :, :,
2] = (ants.slice_image(
evaluation_image,
axis=padded_image_size[dimensions_to_predict[d]],
idx=dimensions_to_predict[d])).numpy()
predicted_data = tid_model.predict(batchX, verbose=verbose)
mos_mean += predicted_data[0, 0]
mos_standard_deviation += predicted_data[0, 1]
mos_mean /= len(dimensions_to_predict)
mos_standard_deviation /= len(dimensions_to_predict)
if which_model == "tidsQualityAssessment":
return_dict = {
'MOS.mean': mos_mean,
'MOS.standardDeviationMean': mos_standard_deviation
}
return (return_dict)
elif is_koniq:
return_dict = {
'MOS.mean': mos_mean,
'sharpness.mean': mos_standard_deviation
}
return (return_dict)
###############
#
# Patchwise
#
###############
else:
evaluation_image = padded_image
if not is_prime(patch_size):
print(
"patch_size should be a prime number: 13, 17, 19, 23, 29, 31, 37, 41, 43, 47, 53, 59, 61, 67, 71, 73, 79, 83, 89, 97..."
)
stride_length_vector = stride_length
if isinstance(stride_length, int):
if image.dimension == 2:
stride_length_vector = (stride_length, stride_length)
elif len(stride_length) == 1:
if image.dimension == 2:
stride_length_vector = (stride_length[0], stride_length[0])
patch_size_vector = (patch_size, patch_size)
if image.dimension == 2:
dimensions_to_predict = (1, )
permutations = list()
mos = image * 0
mos_standard_deviation = image * 0
for d in range(len(dimensions_to_predict)):
if image.dimension == 3:
permutations.append((0, 1, 2))
permutations.append((0, 2, 1))
permutations.append((1, 2, 0))
if dimensions_to_predict[d] == 0:
patch_size_vector = (patch_size, patch_size,
number_of_channels)
if isinstance(stride_length, int):
stride_length_vector = (stride_length, stride_length,
1)
elif dimensions_to_predict[d] == 1:
patch_size_vector = (patch_size, number_of_channels,
patch_size)
if isinstance(stride_length, int):
stride_length_vector = (stride_length, 1,
stride_length)
elif dimensions_to_predict[d] == 2:
patch_size_vector = (number_of_channels, patch_size,
patch_size)
if isinstance(stride_length, int):
stride_length_vector = (1, stride_length,
stride_length)
else:
raise ValueError(
"dimensions_to_predict elements should be 1, 2, and/or 3 for 3-D image."
)
patches = extract_image_patches(evaluation_image,
patch_size=patch_size_vector,
stride_length=stride_length_vector,
return_as_array=False)
batchX = np.zeros(
(len(patches), patch_size, patch_size, number_of_channels))
is_good_patch = np.repeat(False, len(patches))
for i in range(len(patches)):
if patches[i].var() > 0:
is_good_patch[i] = True
patch_image = patches[i]
patch_image = patch_image - patch_image.min()
if patch_image.max() > 0:
if which_model == "tidsQualityAssessment":
patch_image = patch_image / patch_image.max() * 255
elif is_koniq:
patch_image = patch_image / patch_image.max(
) * 2.0 - 1.0
if image.dimension == 2:
for j in range(number_of_channels):
batchX[i, :, :, j] = patch_image
elif image.dimension == 3:
batchX[i, :, :, :] = np.transpose(
np.squeeze(patch_image.numpy()),
permutations[dimensions_to_predict[d]])
good_batchX = batchX[is_good_patch, :, :, :]
predicted_data = tid_model.predict(good_batchX, verbose=verbose)
patches_mos = list()
patches_mos_standard_deviation = list()
zero_patch_image = patch_image * 0
count = 0
for i in range(len(patches)):
if is_good_patch[i]:
patches_mos.append(zero_patch_image +
predicted_data[count, 0])
patches_mos_standard_deviation.append(zero_patch_image +
predicted_data[count,
1])
count += 1
else:
patches_mos.append(zero_patch_image)
patches_mos_standard_deviation.append(zero_patch_image)
mos += pad_or_crop_image_to_size(
reconstruct_image_from_patches(
patches_mos,
evaluation_image,
stride_length=stride_length_vector), image.shape)
mos_standard_deviation += pad_or_crop_image_to_size(
reconstruct_image_from_patches(
patches_mos_standard_deviation,
evaluation_image,
stride_length=stride_length_vector), image.shape)
mos /= len(dimensions_to_predict)
mos_standard_deviation /= len(dimensions_to_predict)
if mask is None:
if which_model == "tidsQualityAssessment":
return_dict = {
'MOS': mos,
'MOS.standardDeviation': mos_standard_deviation,
'MOS.mean': mos.mean(),
'MOS.standardDeviationMean': mos_standard_deviation.mean()
}
return (return_dict)
elif is_koniq:
return_dict = {
'MOS': mos,
'sharpness': mos_standard_deviation,
'MOS.mean': mos.mean(),
'sharpness.mean': mos_standard_deviation.mean()
}
return (return_dict)
else:
if which_model == "tidsQualityAssessment":
return_dict = {
'MOS':
mos * mask,
'MOS.standardDeviation':
mos_standard_deviation * mask,
'MOS.mean': (mos[mask >= 0.5]).mean(),
'MOS.standardDeviationMean':
(mos_standard_deviation[mask >= 0.5]).mean()
}
return (return_dict)
elif is_koniq:
return_dict = {
'MOS': mos * mask,
'sharpness': mos_standard_deviation * mask,
'MOS.mean': (mos[mask >= 0.5]).mean(),
'sharpness.mean':
(mos_standard_deviation[mask >= 0.5]).mean()
}
return (return_dict)
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)
import ants
import os
import numpy as np
root = '/gsfs0/data/poskanzc/Sherlock/preproc/fmriprep/'
all_subjects = ['sub-01', 'sub-02', 'sub-03', 'sub-04', 'sub-05', 'sub-06', 'sub-07', 'sub-08', 'sub-10', 'sub-11', 'sub-12', 'sub-13', 'sub-14', 'sub-15', 'sub-16']
mask = '_CSF_WM_mask_union_bin_shrinked.nii.gz'
for sub in all_subjects:
sub_dir = root + sub +'/func/'
mask_dir = root + sub +'/'+ sub +'_ROIs/' +sub + mask
sub_out_dir = root + sub + '/' + sub + '_ROIs/'
epi = ants.image_read(os.path.join(sub_dir,sub + '_task-sherlockPart1_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz'))
epi3d = ants.slice_image(epi,axis=3,idx=0)
noiseROI = ants.image_read(os.path.join(mask_dir))
reg = ants.registration(fixed=epi3d, moving=noiseROI,type_of_transform='Rigid', reg_iterations = [100,100,20] )
rAnat = reg['warpedmovout']
rAnat.to_filename(os.path.join(sub_out_dir, sub + '_CSF_WM_mask_union_shrinked_funcSize.nii.gz'))
