def regression_match_image(source_image,
reference_image,
poly_order=1,
truncate=True):
"""
Image intensity normalization using linear regression.
Arguments
---------
source_image : ANTsImage
Image whose intensities are matched to the reference
image.
reference_image : ANTsImage
Defines the reference intensity function.
poly_order : integer
Polynomial order of fit. Default is 1 (linear fit).
truncate : boolean
Turns on/off the clipping of intensities.
Returns
-------
ANTs image (i.e., source_image) matched to the (reference_image).
Example
-------
>>> import ants
>>> source_image = ants.image_read(ants.get_ants_data('r16'))
>>> reference_image = ants.image_read(ants.get_ants_data('r64'))
>>> matched_image = regression_match_image(source_image, reference_image)
"""
if source_image.shape != reference_image.shape:
raise ValueError("Images do not have the same dimension.")
source_intensities = np.expand_dims((source_image.numpy()).flatten(), axis=1)
reference_intensities = np.expand_dims((reference_image.numpy()).flatten(), axis=1)
poly_features = PolynomialFeatures(degree=poly_order)
source_intensities_poly = poly_features.fit_transform(source_intensities)
model = LinearRegression()
model.fit(source_intensities_poly, reference_intensities)
matched_source_intensities = model.predict(source_intensities_poly)
if truncate == True:
min_reference_value = reference_intensities.min()
max_reference_value = reference_intensities.max()
matched_source_intensities[matched_source_intensities < min_reference_value] = min_reference_value
matched_source_intensities[matched_source_intensities > max_reference_value] = max_reference_value
matched_source_image = ants.make_image(source_image.shape, matched_source_intensities)
matched_source_image = ants.copy_image_info(source_image, matched_source_image)
return(matched_source_image)
def test_copy_image_info(self):
for img in self.imgs:
img2 = img.clone()
img2.set_spacing([6.9]*img.dimension)
img2.set_origin([6.9]*img.dimension)
self.assertTrue(not ants.image_physical_space_consistency(img,img2))
img3 = ants.copy_image_info(reference=img, target=img2)
self.assertTrue(ants.image_physical_space_consistency(img,img3))
def evaluate_test_subject(scan_path: str, scan_name: str, options: dict,
model):
# Read the image to analize:
scan = ants.image_read(os.path.join(scan_path, scan_name + ".nii.gz"))
scan_np = scan.numpy()
# define the torch.device
device = torch.device('cuda') if options['gpu_use'] else torch.device(
'cpu')
# Create the patches of the image to evaluate
infer_patches, coordenates = get_inference_patches(
scan_path=scan_path,
input_data=[scan_name + "_norm.nii.gz"],
roi=scan_name + "_ROI.nii.gz",
patch_shape=options['patch_size'],
step=options['sampling_step'],
normalize=options['normalize'])
# Get the shape of the patches
sh = infer_patches.shape
segmentation_pred = np.zeros((sh[0], 4, sh[2], sh[3], sh[4]))
batch_size = options['batch_size']
# model Evaluation
model.eval()
b = 0
with torch.no_grad():
for b in range(0, len(segmentation_pred), batch_size):
x = torch.tensor(infer_patches[b:b + batch_size]).to(device)
pred = model(x)
# save the result back from GPU to CPU --> numpy
segmentation_pred[b:b + batch_size] = pred.cpu().numpy()
# reconstruct image takes the inferred patches, the patches coordenates and the image size as inputs
all_probs = np.zeros(scan_np.shape + (4, ))
for i in range(4):
all_probs[:, :, :, i] = reconstruct_image(segmentation_pred[:, i],
coordenates, scan.shape)
segmented = np.argmax(all_probs, axis=3).astype(np.uint8)
# Create a nifti image
segm_img = ants.from_numpy(segmented)
segm_img = ants.copy_image_info(scan, segm_img)
# Save the segmentation mask
output_name = os.path.join(scan_path, scan_name + '_result.nii.gz')
ants.image_write(segm_img, output_name)
return segm_img
def lung_extraction(image,
modality="proton",
antsxnet_cache_directory=None,
verbose=False):
"""
Perform proton or ct lung extraction using U-net.
Arguments
---------
image : ANTsImage
input image
modality : string
Modality image type. Options include "ct", "proton", "protonLobes",
"maskLobes", and "ventilation".
antsxnet_cache_directory : string
Destination directory for storing the downloaded template and model weights.
Since these can be resused, if is None, these data will be downloaded to a
~/.keras/ANTsXNet/.
verbose : boolean
Print progress to the screen.
Returns
-------
Dictionary of ANTs segmentation and probability images.
Example
-------
>>> output = lung_extraction(lung_image, modality="proton")
"""
from ..architectures import create_unet_model_2d
from ..architectures import create_unet_model_3d
from ..utilities import get_pretrained_network
from ..utilities import get_antsxnet_data
from ..utilities import pad_or_crop_image_to_size
if image.dimension != 3:
raise ValueError( "Image dimension must be 3." )
if antsxnet_cache_directory == None:
antsxnet_cache_directory = "ANTsXNet"
image_mods = [modality]
channel_size = len(image_mods)
weights_file_name = None
unet_model = None
if modality == "proton":
weights_file_name = get_pretrained_network("protonLungMri",
antsxnet_cache_directory=antsxnet_cache_directory)
classes = ("background", "left_lung", "right_lung")
number_of_classification_labels = len(classes)
reorient_template_file_name_path = get_antsxnet_data("protonLungTemplate",
antsxnet_cache_directory=antsxnet_cache_directory)
reorient_template = ants.image_read(reorient_template_file_name_path)
resampled_image_size = reorient_template.shape
unet_model = create_unet_model_3d((*resampled_image_size, channel_size),
number_of_outputs=number_of_classification_labels,
number_of_layers=4, number_of_filters_at_base_layer=16, dropout_rate=0.0,
convolution_kernel_size=(7, 7, 5), deconvolution_kernel_size=(7, 7, 5))
unet_model.load_weights(weights_file_name)
if verbose == True:
print("Lung extraction: normalizing image to the template.")
center_of_mass_template = ants.get_center_of_mass(reorient_template * 0 + 1)
center_of_mass_image = ants.get_center_of_mass(image * 0 + 1)
translation = np.asarray(center_of_mass_image) - np.asarray(center_of_mass_template)
xfrm = ants.create_ants_transform(transform_type="Euler3DTransform",
center=np.asarray(center_of_mass_template), translation=translation)
warped_image = ants.apply_ants_transform_to_image(xfrm, image, reorient_template)
batchX = np.expand_dims(warped_image.numpy(), axis=0)
batchX = np.expand_dims(batchX, axis=-1)
batchX = (batchX - batchX.mean()) / batchX.std()
predicted_data = unet_model.predict(batchX, verbose=int(verbose))
origin = warped_image.origin
spacing = warped_image.spacing
direction = warped_image.direction
probability_images_array = list()
for i in range(number_of_classification_labels):
probability_images_array.append(
ants.from_numpy(np.squeeze(predicted_data[0, :, :, :, i]),
origin=origin, spacing=spacing, direction=direction))
if verbose == True:
print("Lung extraction: renormalize probability mask to native space.")
for i in range(number_of_classification_labels):
probability_images_array[i] = ants.apply_ants_transform_to_image(
ants.invert_ants_transform(xfrm), probability_images_array[i], image)
image_matrix = ants.image_list_to_matrix(probability_images_array, image * 0 + 1)
segmentation_matrix = np.argmax(image_matrix, axis=0)
segmentation_image = ants.matrix_to_images(
np.expand_dims(segmentation_matrix, axis=0), image * 0 + 1)[0]
return_dict = {'segmentation_image' : segmentation_image,
'probability_images' : probability_images_array}
return(return_dict)
if modality == "protonLobes" or modality == "maskLobes":
reorient_template_file_name_path = get_antsxnet_data("protonLungTemplate",
antsxnet_cache_directory=antsxnet_cache_directory)
reorient_template = ants.image_read(reorient_template_file_name_path)
resampled_image_size = reorient_template.shape
spatial_priors_file_name_path = get_antsxnet_data("protonLobePriors",
antsxnet_cache_directory=antsxnet_cache_directory)
spatial_priors = ants.image_read(spatial_priors_file_name_path)
priors_image_list = ants.ndimage_to_list(spatial_priors)
channel_size = 1 + len(priors_image_list)
number_of_classification_labels = 1 + len(priors_image_list)
unet_model = create_unet_model_3d((*resampled_image_size, channel_size),
number_of_outputs=number_of_classification_labels, mode="classification",
number_of_filters_at_base_layer=16, number_of_layers=4,
convolution_kernel_size=(3, 3, 3), deconvolution_kernel_size=(2, 2, 2),
dropout_rate=0.0, weight_decay=0, additional_options=("attentionGating",))
if modality == "protonLobes":
penultimate_layer = unet_model.layers[-2].output
outputs2 = Conv3D(filters=1,
kernel_size=(1, 1, 1),
activation='sigmoid',
kernel_regularizer=regularizers.l2(0.0))(penultimate_layer)
unet_model = Model(inputs=unet_model.input, outputs=[unet_model.output, outputs2])
weights_file_name = get_pretrained_network("protonLobes",
antsxnet_cache_directory=antsxnet_cache_directory)
else:
weights_file_name = get_pretrained_network("maskLobes",
antsxnet_cache_directory=antsxnet_cache_directory)
unet_model.load_weights(weights_file_name)
if verbose == True:
print("Lung extraction: normalizing image to the template.")
center_of_mass_template = ants.get_center_of_mass(reorient_template * 0 + 1)
center_of_mass_image = ants.get_center_of_mass(image * 0 + 1)
translation = np.asarray(center_of_mass_image) - np.asarray(center_of_mass_template)
xfrm = ants.create_ants_transform(transform_type="Euler3DTransform",
center=np.asarray(center_of_mass_template), translation=translation)
warped_image = ants.apply_ants_transform_to_image(xfrm, image, reorient_template)
warped_array = warped_image.numpy()
if modality == "protonLobes":
warped_array = (warped_array - warped_array.mean()) / warped_array.std()
else:
warped_array[warped_array != 0] = 1
batchX = np.zeros((1, *warped_array.shape, channel_size))
batchX[0,:,:,:,0] = warped_array
for i in range(len(priors_image_list)):
batchX[0,:,:,:,i+1] = priors_image_list[i].numpy()
predicted_data = unet_model.predict(batchX, verbose=int(verbose))
origin = warped_image.origin
spacing = warped_image.spacing
direction = warped_image.direction
probability_images_array = list()
for i in range(number_of_classification_labels):
if modality == "protonLobes":
probability_images_array.append(
ants.from_numpy(np.squeeze(predicted_data[0][0, :, :, :, i]),
origin=origin, spacing=spacing, direction=direction))
else:
probability_images_array.append(
ants.from_numpy(np.squeeze(predicted_data[0, :, :, :, i]),
origin=origin, spacing=spacing, direction=direction))
if verbose == True:
print("Lung extraction: renormalize probability images to native space.")
for i in range(number_of_classification_labels):
probability_images_array[i] = ants.apply_ants_transform_to_image(
ants.invert_ants_transform(xfrm), probability_images_array[i], image)
image_matrix = ants.image_list_to_matrix(probability_images_array, image * 0 + 1)
segmentation_matrix = np.argmax(image_matrix, axis=0)
segmentation_image = ants.matrix_to_images(
np.expand_dims(segmentation_matrix, axis=0), image * 0 + 1)[0]
if modality == "protonLobes":
whole_lung_mask = ants.from_numpy(np.squeeze(predicted_data[1][0, :, :, :, 0]),
origin=origin, spacing=spacing, direction=direction)
whole_lung_mask = ants.apply_ants_transform_to_image(
ants.invert_ants_transform(xfrm), whole_lung_mask, image)
return_dict = {'segmentation_image' : segmentation_image,
'probability_images' : probability_images_array,
'whole_lung_mask_image' : whole_lung_mask}
return(return_dict)
else:
return_dict = {'segmentation_image' : segmentation_image,
'probability_images' : probability_images_array}
return(return_dict)
elif modality == "ct":
################################
#
# Preprocess image
#
################################
if verbose == True:
print("Preprocess CT image.")
def closest_simplified_direction_matrix(direction):
closest = np.floor(np.abs(direction) + 0.5)
closest[direction < 0] *= -1.0
return closest
simplified_direction = closest_simplified_direction_matrix(image.direction)
reference_image_size = (128, 128, 128)
ct_preprocessed = ants.resample_image(image, reference_image_size, use_voxels=True, interp_type=0)
ct_preprocessed[ct_preprocessed < -1000] = -1000
ct_preprocessed[ct_preprocessed > 400] = 400
ct_preprocessed.set_direction(simplified_direction)
ct_preprocessed.set_origin((0, 0, 0))
ct_preprocessed.set_spacing((1, 1, 1))
################################
#
# Reorient image
#
################################
reference_image = ants.make_image(reference_image_size,
voxval=0,
spacing=(1, 1, 1),
origin=(0, 0, 0),
direction=np.identity(3))
center_of_mass_reference = np.floor(ants.get_center_of_mass(reference_image * 0 + 1))
center_of_mass_image = np.floor(ants.get_center_of_mass(ct_preprocessed * 0 + 1))
translation = np.asarray(center_of_mass_image) - np.asarray(center_of_mass_reference)
xfrm = ants.create_ants_transform(transform_type="Euler3DTransform",
center=np.asarray(center_of_mass_reference), translation=translation)
ct_preprocessed = ((ct_preprocessed - ct_preprocessed.min()) /
(ct_preprocessed.max() - ct_preprocessed.min()))
ct_preprocessed_warped = ants.apply_ants_transform_to_image(
xfrm, ct_preprocessed, reference_image, interpolation="nearestneighbor")
ct_preprocessed_warped = ((ct_preprocessed_warped - ct_preprocessed_warped.min()) /
(ct_preprocessed_warped.max() - ct_preprocessed_warped.min())) - 0.5
################################
#
# Build models and load weights
#
################################
if verbose == True:
print("Build model and load weights.")
weights_file_name = get_pretrained_network("lungCtWithPriorsSegmentationWeights",
antsxnet_cache_directory=antsxnet_cache_directory)
classes = ("background", "left lung", "right lung", "airways")
number_of_classification_labels = len(classes)
luna16_priors = ants.ndimage_to_list(ants.image_read(get_antsxnet_data("luna16LungPriors")))
for i in range(len(luna16_priors)):
luna16_priors[i] = ants.resample_image(luna16_priors[i], reference_image_size, use_voxels=True)
channel_size = len(luna16_priors) + 1
unet_model = create_unet_model_3d((*reference_image_size, channel_size),
number_of_outputs=number_of_classification_labels, mode="classification",
number_of_layers=4, number_of_filters_at_base_layer=16, dropout_rate=0.0,
convolution_kernel_size=(3, 3, 3), deconvolution_kernel_size=(2, 2, 2),
weight_decay=1e-5, additional_options=("attentionGating",))
unet_model.load_weights(weights_file_name)
################################
#
# Do prediction and normalize to native space
#
################################
if verbose == True:
print("Prediction.")
batchX = np.zeros((1, *reference_image_size, channel_size))
batchX[:,:,:,:,0] = ct_preprocessed_warped.numpy()
for i in range(len(luna16_priors)):
batchX[:,:,:,:,i+1] = luna16_priors[i].numpy() - 0.5
predicted_data = unet_model.predict(batchX, verbose=verbose)
probability_images = list()
for i in range(number_of_classification_labels):
if verbose == True:
print("Reconstructing image", classes[i])
probability_image = ants.from_numpy(np.squeeze(predicted_data[:,:,:,:,i]),
origin=ct_preprocessed_warped.origin, spacing=ct_preprocessed_warped.spacing,
direction=ct_preprocessed_warped.direction)
probability_image = ants.apply_ants_transform_to_image(
ants.invert_ants_transform(xfrm), probability_image, ct_preprocessed)
probability_image = ants.resample_image(probability_image,
resample_params=image.shape, use_voxels=True, interp_type=0)
probability_image = ants.copy_image_info(image, probability_image)
probability_images.append(probability_image)
image_matrix = ants.image_list_to_matrix(probability_images, image * 0 + 1)
segmentation_matrix = np.argmax(image_matrix, axis=0)
segmentation_image = ants.matrix_to_images(
np.expand_dims(segmentation_matrix, axis=0), image * 0 + 1)[0]
return_dict = {'segmentation_image' : segmentation_image,
'probability_images' : probability_images}
return(return_dict)
elif modality == "ventilation":
################################
#
# Preprocess image
#
################################
if verbose == True:
print("Preprocess ventilation image.")
template_size = (256, 256)
image_modalities = ("Ventilation",)
channel_size = len(image_modalities)
preprocessed_image = (image - image.mean()) / image.std()
ants.set_direction(preprocessed_image, np.identity(3))
################################
#
# Build models and load weights
#
################################
unet_model = create_unet_model_2d((*template_size, channel_size),
number_of_outputs=1, mode='sigmoid',
number_of_layers=4, number_of_filters_at_base_layer=32, dropout_rate=0.0,
convolution_kernel_size=(3, 3), deconvolution_kernel_size=(2, 2),
weight_decay=0)
if verbose == True:
print("Whole lung mask: retrieving model weights.")
weights_file_name = get_pretrained_network("wholeLungMaskFromVentilation",
antsxnet_cache_directory=antsxnet_cache_directory)
unet_model.load_weights(weights_file_name)
################################
#
# Extract slices
#
################################
spacing = ants.get_spacing(preprocessed_image)
dimensions_to_predict = (spacing.index(max(spacing)),)
total_number_of_slices = 0
for d in range(len(dimensions_to_predict)):
total_number_of_slices += preprocessed_image.shape[dimensions_to_predict[d]]
batchX = np.zeros((total_number_of_slices, *template_size, channel_size))
slice_count = 0
for d in range(len(dimensions_to_predict)):
number_of_slices = preprocessed_image.shape[dimensions_to_predict[d]]
if verbose == True:
print("Extracting slices for dimension ", dimensions_to_predict[d], ".")
for i in range(number_of_slices):
ventilation_slice = pad_or_crop_image_to_size(ants.slice_image(preprocessed_image, dimensions_to_predict[d], i), template_size)
batchX[slice_count,:,:,0] = ventilation_slice.numpy()
slice_count += 1
################################
#
# Do prediction and then restack into the image
#
################################
if verbose == True:
print("Prediction.")
prediction = unet_model.predict(batchX, verbose=verbose)
permutations = list()
permutations.append((0, 1, 2))
permutations.append((1, 0, 2))
permutations.append((1, 2, 0))
probability_image = ants.image_clone(image) * 0
current_start_slice = 0
for d in range(len(dimensions_to_predict)):
current_end_slice = current_start_slice + preprocessed_image.shape[dimensions_to_predict[d]] - 1
which_batch_slices = range(current_start_slice, current_end_slice)
prediction_per_dimension = prediction[which_batch_slices,:,:,0]
prediction_array = np.transpose(np.squeeze(prediction_per_dimension), permutations[dimensions_to_predict[d]])
prediction_image = ants.copy_image_info(image,
pad_or_crop_image_to_size(ants.from_numpy(prediction_array),
image.shape))
probability_image = probability_image + (prediction_image - probability_image) / (d + 1)
current_start_slice = current_end_slice + 1
return(probability_image)
else:
return ValueError("Unrecognized modality.")
def sysu_media_wmh_segmentation(flair,
t1=None,
do_preprocessing=True,
use_ensemble=True,
use_axial_slices_only=True,
antsxnet_cache_directory=None,
verbose=False):
"""
Perform WMH segmentation using the winning submission in the MICCAI
2017 challenge by the sysu_media team using FLAIR or T1/FLAIR. The
MICCAI challenge is discussed in
https://pubmed.ncbi.nlm.nih.gov/30908194/
with the sysu_media's team entry is discussed in
https://pubmed.ncbi.nlm.nih.gov/30125711/
with the original implementation available here:
https://github.com/hongweilibran/wmh_ibbmTum
Arguments
---------
flair : ANTsImage
input 3-D FLAIR brain image (not skull-stripped).
t1 : ANTsImage
input 3-D T1 brain image (not skull-stripped).
do_preprocessing : boolean
perform n4 bias correction?
use_ensemble : boolean
check whether to use all 3 sets of weights.
use_axial_slices_only : boolean
If True, use original implementation which was trained on axial slices.
If False, use ANTsXNet variant implementation which applies the slice-by-slice
models to all 3 dimensions and averages the results.
antsxnet_cache_directory : string
Destination directory for storing the downloaded template and model weights.
Since these can be resused, if is None, these data will be downloaded to a
~/.keras/ANTsXNet/.
verbose : boolean
Print progress to the screen.
Returns
-------
WMH segmentation probability image
Example
-------
>>> image = ants.image_read("flair.nii.gz")
>>> probability_mask = sysu_media_wmh_segmentation(image)
"""
from ..architectures import create_sysu_media_unet_model_2d
from ..utilities import brain_extraction
from ..utilities import crop_image_center
from ..utilities import get_pretrained_network
from ..utilities import preprocess_brain_image
from ..utilities import pad_or_crop_image_to_size
if flair.dimension != 3:
raise ValueError("Image dimension must be 3.")
if antsxnet_cache_directory == None:
antsxnet_cache_directory = "ANTsXNet"
################################
#
# Preprocess images
#
################################
flair_preprocessed = flair
if do_preprocessing == True:
flair_preprocessing = preprocess_brain_image(
flair,
truncate_intensity=(0.01, 0.99),
do_brain_extraction=False,
do_bias_correction=True,
do_denoising=False,
antsxnet_cache_directory=antsxnet_cache_directory,
verbose=verbose)
flair_preprocessed = flair_preprocessing["preprocessed_image"]
number_of_channels = 1
if t1 is not None:
t1_preprocessed = t1
if do_preprocessing == True:
t1_preprocessing = preprocess_brain_image(
t1,
truncate_intensity=(0.01, 0.99),
do_brain_extraction=False,
do_bias_correction=True,
do_denoising=False,
antsxnet_cache_directory=antsxnet_cache_directory,
verbose=verbose)
t1_preprocessed = t1_preprocessing["preprocessed_image"]
number_of_channels = 2
################################
#
# Estimate mask
#
################################
brain_mask = None
if verbose == True:
print("Estimating brain mask.")
if t1 is not None:
brain_mask = brain_extraction(t1, modality="t1")
else:
brain_mask = brain_extraction(flair, modality="flair")
reference_image = ants.make_image((200, 200, 200),
voxval=1,
spacing=(1, 1, 1),
origin=(0, 0, 0),
direction=np.identity(3))
center_of_mass_reference = ants.get_center_of_mass(reference_image)
center_of_mass_image = ants.get_center_of_mass(brain_mask)
translation = np.asarray(center_of_mass_image) - np.asarray(
center_of_mass_reference)
xfrm = ants.create_ants_transform(
transform_type="Euler3DTransform",
center=np.asarray(center_of_mass_reference),
translation=translation)
flair_preprocessed_warped = ants.apply_ants_transform_to_image(
xfrm, flair_preprocessed, reference_image)
brain_mask_warped = ants.threshold_image(
ants.apply_ants_transform_to_image(xfrm, brain_mask, reference_image),
0.5, 1.1, 1, 0)
if t1 is not None:
t1_preprocessed_warped = ants.apply_ants_transform_to_image(
xfrm, t1_preprocessed, reference_image)
################################
#
# Gaussian normalize intensity based on brain mask
#
################################
mean_flair = flair_preprocessed_warped[brain_mask_warped > 0].mean()
std_flair = flair_preprocessed_warped[brain_mask_warped > 0].std()
flair_preprocessed_warped = (flair_preprocessed_warped -
mean_flair) / std_flair
if number_of_channels == 2:
mean_t1 = t1_preprocessed_warped[brain_mask_warped > 0].mean()
std_t1 = t1_preprocessed_warped[brain_mask_warped > 0].std()
t1_preprocessed_warped = (t1_preprocessed_warped - mean_t1) / std_t1
################################
#
# Build models and load weights
#
################################
number_of_models = 1
if use_ensemble == True:
number_of_models = 3
unet_models = list()
for i in range(number_of_models):
if number_of_channels == 1:
weights_file_name = get_pretrained_network(
"sysuMediaWmhFlairOnlyModel" + str(i),
antsxnet_cache_directory=antsxnet_cache_directory)
else:
weights_file_name = get_pretrained_network(
"sysuMediaWmhFlairT1Model" + str(i),
antsxnet_cache_directory=antsxnet_cache_directory)
unet_models.append(
create_sysu_media_unet_model_2d((200, 200, number_of_channels)))
unet_models[i].load_weights(weights_file_name)
################################
#
# Extract slices
#
################################
dimensions_to_predict = [2]
if use_axial_slices_only == False:
dimensions_to_predict = list(range(3))
total_number_of_slices = 0
for d in range(len(dimensions_to_predict)):
total_number_of_slices += flair_preprocessed_warped.shape[
dimensions_to_predict[d]]
batchX = np.zeros((total_number_of_slices, 200, 200, number_of_channels))
slice_count = 0
for d in range(len(dimensions_to_predict)):
number_of_slices = flair_preprocessed_warped.shape[
dimensions_to_predict[d]]
if verbose == True:
print("Extracting slices for dimension ", dimensions_to_predict[d],
".")
for i in range(number_of_slices):
flair_slice = pad_or_crop_image_to_size(
ants.slice_image(flair_preprocessed_warped,
dimensions_to_predict[d], i), (200, 200))
batchX[slice_count, :, :, 0] = flair_slice.numpy()
if number_of_channels == 2:
t1_slice = pad_or_crop_image_to_size(
ants.slice_image(t1_preprocessed_warped,
dimensions_to_predict[d], i), (200, 200))
batchX[slice_count, :, :, 1] = t1_slice.numpy()
slice_count += 1
################################
#
# Do prediction and then restack into the image
#
################################
if verbose == True:
print("Prediction.")
prediction = unet_models[0].predict(batchX, verbose=verbose)
if number_of_models > 1:
for i in range(1, number_of_models, 1):
prediction += unet_models[i].predict(batchX, verbose=verbose)
prediction /= number_of_models
permutations = list()
permutations.append((0, 1, 2))
permutations.append((1, 0, 2))
permutations.append((1, 2, 0))
prediction_image_average = ants.image_clone(flair_preprocessed_warped) * 0
current_start_slice = 0
for d in range(len(dimensions_to_predict)):
current_end_slice = current_start_slice + flair_preprocessed_warped.shape[
dimensions_to_predict[d]] - 1
which_batch_slices = range(current_start_slice, current_end_slice)
prediction_per_dimension = prediction[which_batch_slices, :, :, :]
prediction_array = np.transpose(np.squeeze(prediction_per_dimension),
permutations[dimensions_to_predict[d]])
prediction_image = ants.copy_image_info(
flair_preprocessed_warped,
pad_or_crop_image_to_size(ants.from_numpy(prediction_array),
flair_preprocessed_warped.shape))
prediction_image_average = prediction_image_average + (
prediction_image - prediction_image_average) / (d + 1)
current_start_slice = current_end_slice + 1
probability_image = ants.apply_ants_transform_to_image(
ants.invert_ants_transform(xfrm), prediction_image_average, flair)
return (probability_image)
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 deep_flash(t1,
t2=None,
do_preprocessing=True,
use_rank_intensity=True,
antsxnet_cache_directory=None,
verbose=False):
"""
Hippocampal/Enthorhinal segmentation using "Deep Flash"
Perform hippocampal/entorhinal segmentation in T1 and T1/T2 images using
labels from Mike Yassa's lab
https://faculty.sites.uci.edu/myassa/
The labeling is as follows:
Label 0 : background
Label 5 : left aLEC
Label 6 : right aLEC
Label 7 : left pMEC
Label 8 : right pMEC
Label 9 : left perirhinal
Label 10: right perirhinal
Label 11: left parahippocampal
Label 12: right parahippocampal
Label 13: left DG/CA2/CA3/CA4
Label 14: right DG/CA2/CA3/CA4
Label 15: left CA1
Label 16: right CA1
Label 17: left subiculum
Label 18: right subiculum
Preprocessing on the training data consisted of:
* n4 bias correction,
* affine registration to the "deep flash" template.
which is performed on the input images if do_preprocessing = True.
Arguments
---------
t1 : ANTsImage
raw or preprocessed 3-D T1-weighted brain image.
t2 : ANTsImage
Optional 3-D T2-weighted brain image. If specified, it is assumed to be
pre-aligned to the t1.
do_preprocessing : boolean
See description above.
use_rank_intensity : boolean
If false, use histogram matching with cropped template ROI. Otherwise,
use a rank intensity transform on the cropped ROI.
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 and foreground.
Example
-------
>>> image = ants.image_read("t1.nii.gz")
>>> flash = deep_flash(image)
"""
from ..architectures import create_unet_model_3d
from ..utilities import get_pretrained_network
from ..utilities import get_antsxnet_data
from ..utilities import brain_extraction
if t1.dimension != 3:
raise ValueError("Image dimension must be 3.")
if antsxnet_cache_directory == None:
antsxnet_cache_directory = "ANTsXNet"
################################
#
# Options temporarily taken from the user
#
################################
# use_hierarchical_parcellation : boolean
# If True, use u-net model with additional outputs of the medial temporal lobe
# region, hippocampal, and entorhinal/perirhinal/parahippocampal regions. Otherwise
# the only additional output is the medial temporal lobe.
#
# use_contralaterality : boolean
# Use both hemispherical models to also predict the corresponding contralateral
# segmentation and use both sets of priors to produce the results. Mainly used
# for debugging.
use_hierarchical_parcellation = True
use_contralaterality = True
################################
#
# Preprocess images
#
################################
t1_preprocessed = t1
t1_mask = None
t1_preprocessed_flipped = None
t1_template = ants.image_read(
get_antsxnet_data("deepFlashTemplateT1SkullStripped"))
template_transforms = None
if do_preprocessing:
if verbose == True:
print("Preprocessing T1.")
# Brain extraction
probability_mask = brain_extraction(
t1_preprocessed,
modality="t1",
antsxnet_cache_directory=antsxnet_cache_directory,
verbose=verbose)
t1_mask = ants.threshold_image(probability_mask, 0.5, 1, 1, 0)
t1_preprocessed = t1_preprocessed * t1_mask
# Do bias correction
t1_preprocessed = ants.n4_bias_field_correction(t1_preprocessed,
t1_mask,
shrink_factor=4,
verbose=verbose)
# Warp to template
registration = ants.registration(
fixed=t1_template,
moving=t1_preprocessed,
type_of_transform="antsRegistrationSyNQuickRepro[a]",
verbose=verbose)
template_transforms = dict(fwdtransforms=registration['fwdtransforms'],
invtransforms=registration['invtransforms'])
t1_preprocessed = registration['warpedmovout']
if use_contralaterality:
t1_preprocessed_array = t1_preprocessed.numpy()
t1_preprocessed_array_flipped = np.flip(t1_preprocessed_array, axis=0)
t1_preprocessed_flipped = ants.from_numpy(
t1_preprocessed_array_flipped,
origin=t1_preprocessed.origin,
spacing=t1_preprocessed.spacing,
direction=t1_preprocessed.direction)
t2_preprocessed = t2
t2_preprocessed_flipped = None
t2_template = None
if t2 is not None:
t2_template = ants.image_read(
get_antsxnet_data("deepFlashTemplateT2SkullStripped"))
t2_template = ants.copy_image_info(t1_template, t2_template)
if do_preprocessing:
if verbose == True:
print("Preprocessing T2.")
# Brain extraction
t2_preprocessed = t2_preprocessed * t1_mask
# Do bias correction
t2_preprocessed = ants.n4_bias_field_correction(t2_preprocessed,
t1_mask,
shrink_factor=4,
verbose=verbose)
# Warp to template
t2_preprocessed = ants.apply_transforms(
fixed=t1_template,
moving=t2_preprocessed,
transformlist=template_transforms['fwdtransforms'],
verbose=verbose)
if use_contralaterality:
t2_preprocessed_array = t2_preprocessed.numpy()
t2_preprocessed_array_flipped = np.flip(t2_preprocessed_array,
axis=0)
t2_preprocessed_flipped = ants.from_numpy(
t2_preprocessed_array_flipped,
origin=t2_preprocessed.origin,
spacing=t2_preprocessed.spacing,
direction=t2_preprocessed.direction)
probability_images = list()
labels = (0, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18)
image_size = (64, 64, 96)
################################
#
# Process left/right in split networks
#
################################
################################
#
# Download spatial priors
#
################################
spatial_priors_file_name_path = get_antsxnet_data(
"deepFlashPriors", 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)
for i in range(len(priors_image_list)):
priors_image_list[i] = ants.copy_image_info(t1_preprocessed,
priors_image_list[i])
labels_left = labels[1::2]
priors_image_left_list = priors_image_list[1::2]
probability_images_left = list()
foreground_probability_images_left = list()
lower_bound_left = (76, 74, 56)
upper_bound_left = (140, 138, 152)
tmp_cropped = ants.crop_indices(t1_preprocessed, lower_bound_left,
upper_bound_left)
origin_left = tmp_cropped.origin
spacing = tmp_cropped.spacing
direction = tmp_cropped.direction
t1_template_roi_left = ants.crop_indices(t1_template, lower_bound_left,
upper_bound_left)
t1_template_roi_left = (t1_template_roi_left - t1_template_roi_left.min(
)) / (t1_template_roi_left.max() - t1_template_roi_left.min()) * 2.0 - 1.0
t2_template_roi_left = None
if t2_template is not None:
t2_template_roi_left = ants.crop_indices(t2_template, lower_bound_left,
upper_bound_left)
t2_template_roi_left = (t2_template_roi_left -
t2_template_roi_left.min()) / (
t2_template_roi_left.max() -
t2_template_roi_left.min()) * 2.0 - 1.0
labels_right = labels[2::2]
priors_image_right_list = priors_image_list[2::2]
probability_images_right = list()
foreground_probability_images_right = list()
lower_bound_right = (20, 74, 56)
upper_bound_right = (84, 138, 152)
tmp_cropped = ants.crop_indices(t1_preprocessed, lower_bound_right,
upper_bound_right)
origin_right = tmp_cropped.origin
t1_template_roi_right = ants.crop_indices(t1_template, lower_bound_right,
upper_bound_right)
t1_template_roi_right = (
t1_template_roi_right - t1_template_roi_right.min()
) / (t1_template_roi_right.max() - t1_template_roi_right.min()) * 2.0 - 1.0
t2_template_roi_right = None
if t2_template is not None:
t2_template_roi_right = ants.crop_indices(t2_template,
lower_bound_right,
upper_bound_right)
t2_template_roi_right = (t2_template_roi_right -
t2_template_roi_right.min()) / (
t2_template_roi_right.max() -
t2_template_roi_right.min()) * 2.0 - 1.0
################################
#
# Create model
#
################################
channel_size = 1 + len(labels_left)
if t2 is not None:
channel_size += 1
number_of_classification_labels = 1 + len(labels_left)
unet_model = create_unet_model_3d(
(*image_size, channel_size),
number_of_outputs=number_of_classification_labels,
mode="classification",
number_of_filters=(32, 64, 96, 128, 256),
convolution_kernel_size=(3, 3, 3),
deconvolution_kernel_size=(2, 2, 2),
dropout_rate=0.0,
weight_decay=0)
penultimate_layer = unet_model.layers[-2].output
# medial temporal lobe
output1 = Conv3D(
filters=1,
kernel_size=(1, 1, 1),
activation='sigmoid',
kernel_regularizer=regularizers.l2(0.0))(penultimate_layer)
if use_hierarchical_parcellation:
# EC, perirhinal, and parahippo.
output2 = Conv3D(
filters=1,
kernel_size=(1, 1, 1),
activation='sigmoid',
kernel_regularizer=regularizers.l2(0.0))(penultimate_layer)
# Hippocampus
output3 = 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, output1, output2, output3])
else:
unet_model = Model(inputs=unet_model.input,
outputs=[unet_model.output, output1])
################################
#
# Left: build model and load weights
#
################################
network_name = 'deepFlashLeftT1'
if t2 is not None:
network_name = 'deepFlashLeftBoth'
if use_hierarchical_parcellation:
network_name += "Hierarchical"
if use_rank_intensity:
network_name += "_ri"
if verbose:
print("DeepFlash: retrieving model weights (left).")
weights_file_name = get_pretrained_network(
network_name, antsxnet_cache_directory=antsxnet_cache_directory)
unet_model.load_weights(weights_file_name)
################################
#
# Left: do prediction and normalize to native space
#
################################
if verbose:
print("Prediction (left).")
batchX = None
if use_contralaterality:
batchX = np.zeros((2, *image_size, channel_size))
else:
batchX = np.zeros((1, *image_size, channel_size))
t1_cropped = ants.crop_indices(t1_preprocessed, lower_bound_left,
upper_bound_left)
if use_rank_intensity:
t1_cropped = ants.rank_intensity(t1_cropped)
else:
t1_cropped = ants.histogram_match_image(t1_cropped,
t1_template_roi_left, 255, 64,
False)
batchX[0, :, :, :, 0] = t1_cropped.numpy()
if use_contralaterality:
t1_cropped = ants.crop_indices(t1_preprocessed_flipped,
lower_bound_left, upper_bound_left)
if use_rank_intensity:
t1_cropped = ants.rank_intensity(t1_cropped)
else:
t1_cropped = ants.histogram_match_image(t1_cropped,
t1_template_roi_left, 255,
64, False)
batchX[1, :, :, :, 0] = t1_cropped.numpy()
if t2 is not None:
t2_cropped = ants.crop_indices(t2_preprocessed, lower_bound_left,
upper_bound_left)
if use_rank_intensity:
t2_cropped = ants.rank_intensity(t2_cropped)
else:
t2_cropped = ants.histogram_match_image(t2_cropped,
t2_template_roi_left, 255,
64, False)
batchX[0, :, :, :, 1] = t2_cropped.numpy()
if use_contralaterality:
t2_cropped = ants.crop_indices(t2_preprocessed_flipped,
lower_bound_left, upper_bound_left)
if use_rank_intensity:
t2_cropped = ants.rank_intensity(t2_cropped)
else:
t2_cropped = ants.histogram_match_image(
t2_cropped, t2_template_roi_left, 255, 64, False)
batchX[1, :, :, :, 1] = t2_cropped.numpy()
for i in range(len(priors_image_left_list)):
cropped_prior = ants.crop_indices(priors_image_left_list[i],
lower_bound_left, upper_bound_left)
for j in range(batchX.shape[0]):
batchX[j, :, :, :, i +
(channel_size - len(labels_left))] = cropped_prior.numpy()
predicted_data = unet_model.predict(batchX, verbose=verbose)
for i in range(1 + len(labels_left)):
for j in range(predicted_data[0].shape[0]):
probability_image = \
ants.from_numpy(np.squeeze(predicted_data[0][j, :, :, :, i]),
origin=origin_left, spacing=spacing, direction=direction)
if i > 0:
probability_image = ants.decrop_image(probability_image,
t1_preprocessed * 0)
else:
probability_image = ants.decrop_image(probability_image,
t1_preprocessed * 0 + 1)
if j == 1: # flipped
probability_array_flipped = np.flip(probability_image.numpy(),
axis=0)
probability_image = ants.from_numpy(
probability_array_flipped,
origin=probability_image.origin,
spacing=probability_image.spacing,
direction=probability_image.direction)
if do_preprocessing:
probability_image = ants.apply_transforms(
fixed=t1,
moving=probability_image,
transformlist=template_transforms['invtransforms'],
whichtoinvert=[True],
interpolator="linear",
verbose=verbose)
if j == 0: # not flipped
probability_images_left.append(probability_image)
else: # flipped
probability_images_right.append(probability_image)
################################
#
# Left: do prediction of mtl, hippocampal, and ec regions and normalize to native space
#
################################
for i in range(1, len(predicted_data)):
for j in range(predicted_data[i].shape[0]):
probability_image = \
ants.from_numpy(np.squeeze(predicted_data[i][j, :, :, :, 0]),
origin=origin_left, spacing=spacing, direction=direction)
probability_image = ants.decrop_image(probability_image,
t1_preprocessed * 0)
if j == 1: # flipped
probability_array_flipped = np.flip(probability_image.numpy(),
axis=0)
probability_image = ants.from_numpy(
probability_array_flipped,
origin=probability_image.origin,
spacing=probability_image.spacing,
direction=probability_image.direction)
if do_preprocessing:
probability_image = ants.apply_transforms(
fixed=t1,
moving=probability_image,
transformlist=template_transforms['invtransforms'],
whichtoinvert=[True],
interpolator="linear",
verbose=verbose)
if j == 0: # not flipped
foreground_probability_images_left.append(probability_image)
else:
foreground_probability_images_right.append(probability_image)
################################
#
# Right: build model and load weights
#
################################
network_name = 'deepFlashRightT1'
if t2 is not None:
network_name = 'deepFlashRightBoth'
if use_hierarchical_parcellation:
network_name += "Hierarchical"
if use_rank_intensity:
network_name += "_ri"
if verbose:
print("DeepFlash: retrieving model weights (right).")
weights_file_name = get_pretrained_network(
network_name, antsxnet_cache_directory=antsxnet_cache_directory)
unet_model.load_weights(weights_file_name)
################################
#
# Right: do prediction and normalize to native space
#
################################
if verbose:
print("Prediction (right).")
batchX = None
if use_contralaterality:
batchX = np.zeros((2, *image_size, channel_size))
else:
batchX = np.zeros((1, *image_size, channel_size))
t1_cropped = ants.crop_indices(t1_preprocessed, lower_bound_right,
upper_bound_right)
if use_rank_intensity:
t1_cropped = ants.rank_intensity(t1_cropped)
else:
t1_cropped = ants.histogram_match_image(t1_cropped,
t1_template_roi_right, 255, 64,
False)
batchX[0, :, :, :, 0] = t1_cropped.numpy()
if use_contralaterality:
t1_cropped = ants.crop_indices(t1_preprocessed_flipped,
lower_bound_right, upper_bound_right)
if use_rank_intensity:
t1_cropped = ants.rank_intensity(t1_cropped)
else:
t1_cropped = ants.histogram_match_image(t1_cropped,
t1_template_roi_right, 255,
64, False)
batchX[1, :, :, :, 0] = t1_cropped.numpy()
if t2 is not None:
t2_cropped = ants.crop_indices(t2_preprocessed, lower_bound_right,
upper_bound_right)
if use_rank_intensity:
t2_cropped = ants.rank_intensity(t2_cropped)
else:
t2_cropped = ants.histogram_match_image(t2_cropped,
t2_template_roi_right, 255,
64, False)
batchX[0, :, :, :, 1] = t2_cropped.numpy()
if use_contralaterality:
t2_cropped = ants.crop_indices(t2_preprocessed_flipped,
lower_bound_right,
upper_bound_right)
if use_rank_intensity:
t2_cropped = ants.rank_intensity(t2_cropped)
else:
t2_cropped = ants.histogram_match_image(
t2_cropped, t2_template_roi_right, 255, 64, False)
batchX[1, :, :, :, 1] = t2_cropped.numpy()
for i in range(len(priors_image_right_list)):
cropped_prior = ants.crop_indices(priors_image_right_list[i],
lower_bound_right, upper_bound_right)
for j in range(batchX.shape[0]):
batchX[j, :, :, :, i +
(channel_size - len(labels_right))] = cropped_prior.numpy()
predicted_data = unet_model.predict(batchX, verbose=verbose)
for i in range(1 + len(labels_right)):
for j in range(predicted_data[0].shape[0]):
probability_image = \
ants.from_numpy(np.squeeze(predicted_data[0][j, :, :, :, i]),
origin=origin_right, spacing=spacing, direction=direction)
if i > 0:
probability_image = ants.decrop_image(probability_image,
t1_preprocessed * 0)
else:
probability_image = ants.decrop_image(probability_image,
t1_preprocessed * 0 + 1)
if j == 1: # flipped
probability_array_flipped = np.flip(probability_image.numpy(),
axis=0)
probability_image = ants.from_numpy(
probability_array_flipped,
origin=probability_image.origin,
spacing=probability_image.spacing,
direction=probability_image.direction)
if do_preprocessing:
probability_image = ants.apply_transforms(
fixed=t1,
moving=probability_image,
transformlist=template_transforms['invtransforms'],
whichtoinvert=[True],
interpolator="linear",
verbose=verbose)
if j == 0: # not flipped
if use_contralaterality:
probability_images_right[i] = (
probability_images_right[i] + probability_image) / 2
else:
probability_images_right.append(probability_image)
else: # flipped
probability_images_left[i] = (probability_images_left[i] +
probability_image) / 2
################################
#
# Right: do prediction of mtl, hippocampal, and ec regions and normalize to native space
#
################################
for i in range(1, len(predicted_data)):
for j in range(predicted_data[i].shape[0]):
probability_image = \
ants.from_numpy(np.squeeze(predicted_data[i][j, :, :, :, 0]),
origin=origin_right, spacing=spacing, direction=direction)
probability_image = ants.decrop_image(probability_image,
t1_preprocessed * 0)
if j == 1: # flipped
probability_array_flipped = np.flip(probability_image.numpy(),
axis=0)
probability_image = ants.from_numpy(
probability_array_flipped,
origin=probability_image.origin,
spacing=probability_image.spacing,
direction=probability_image.direction)
if do_preprocessing:
probability_image = ants.apply_transforms(
fixed=t1,
moving=probability_image,
transformlist=template_transforms['invtransforms'],
whichtoinvert=[True],
interpolator="linear",
verbose=verbose)
if j == 0: # not flipped
if use_contralaterality:
foreground_probability_images_right[
i - 1] = (foreground_probability_images_right[i - 1] +
probability_image) / 2
else:
foreground_probability_images_right.append(
probability_image)
else:
foreground_probability_images_left[
i - 1] = (foreground_probability_images_left[i - 1] +
probability_image) / 2
################################
#
# Combine priors
#
################################
probability_background_image = ants.image_clone(t1) * 0
for i in range(1, len(probability_images_left)):
probability_background_image += probability_images_left[i]
for i in range(1, len(probability_images_right)):
probability_background_image += probability_images_right[i]
probability_images.append(probability_background_image * -1 + 1)
for i in range(1, len(probability_images_left)):
probability_images.append(probability_images_left[i])
probability_images.append(probability_images_right[i])
################################
#
# Convert probability images to segmentation
#
################################
# image_matrix = ants.image_list_to_matrix(probability_images, t1 * 0 + 1)
# segmentation_matrix = np.argmax(image_matrix, axis=0)
# segmentation_image = ants.matrix_to_images(
# np.expand_dims(segmentation_matrix, axis=0), t1 * 0 + 1)[0]
image_matrix = ants.image_list_to_matrix(
probability_images[1:(len(probability_images))], t1 * 0 + 1)
background_foreground_matrix = np.stack([
ants.image_list_to_matrix([probability_images[0]], t1 * 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), t1 * 0 + 1)[0]
relabeled_image = ants.image_clone(segmentation_image)
for i in range(len(labels)):
relabeled_image[segmentation_image == i] = labels[i]
foreground_probability_images = list()
for i in range(len(foreground_probability_images_left)):
foreground_probability_images.append(
foreground_probability_images_left[i] +
foreground_probability_images_right[i])
return_dict = None
if use_hierarchical_parcellation:
return_dict = {
'segmentation_image':
relabeled_image,
'probability_images':
probability_images,
'medial_temporal_lobe_probability_image':
foreground_probability_images[0],
'other_region_probability_image':
foreground_probability_images[1],
'hippocampal_probability_image':
foreground_probability_images[2]
}
else:
return_dict = {
'segmentation_image':
relabeled_image,
'probability_images':
probability_images,
'medial_temporal_lobe_probability_image':
foreground_probability_images[0]
}
return (return_dict)
def claustrum_segmentation(t1,
do_preprocessing=True,
use_ensemble=True,
antsxnet_cache_directory=None,
verbose=False):
"""
Claustrum segmentation
Described here:
https://arxiv.org/abs/2008.03465
with the implementation available at:
https://github.com/hongweilibran/claustrum_multi_view
Arguments
---------
t1 : ANTsImage
input 3-D T1 brain image.
do_preprocessing : boolean
perform n4 bias correction.
use_ensemble : boolean
check whether to use all 3 sets of weights.
antsxnet_cache_directory : string
Destination directory for storing the downloaded template and model weights.
Since these can be resused, if is None, these data will be downloaded to a
~/.keras/ANTsXNet/.
verbose : boolean
Print progress to the screen.
Returns
-------
Claustrum segmentation probability image
Example
-------
>>> image = ants.image_read("t1.nii.gz")
>>> probability_mask = claustrum_segmentation(image)
"""
from ..architectures import create_sysu_media_unet_model_2d
from ..utilities import brain_extraction
from ..utilities import get_pretrained_network
from ..utilities import preprocess_brain_image
from ..utilities import pad_or_crop_image_to_size
if t1.dimension != 3:
raise ValueError("Image dimension must be 3.")
if antsxnet_cache_directory == None:
antsxnet_cache_directory = "ANTsXNet"
image_size = (180, 180)
################################
#
# Preprocess images
#
################################
number_of_channels = 1
t1_preprocessed = ants.image_clone(t1)
brain_mask = ants.threshold_image(t1, 0, 0, 0, 1)
if do_preprocessing == True:
t1_preprocessing = preprocess_brain_image(
t1,
truncate_intensity=(0.01, 0.99),
brain_extraction_modality="t1",
do_bias_correction=True,
do_denoising=True,
antsxnet_cache_directory=antsxnet_cache_directory,
verbose=verbose)
t1_preprocessed = t1_preprocessing["preprocessed_image"]
brain_mask = t1_preprocessing["brain_mask"]
reference_image = ants.make_image((170, 256, 256),
voxval=1,
spacing=(1, 1, 1),
origin=(0, 0, 0),
direction=np.identity(3))
center_of_mass_reference = ants.get_center_of_mass(reference_image)
center_of_mass_image = ants.get_center_of_mass(brain_mask)
translation = np.asarray(center_of_mass_image) - np.asarray(
center_of_mass_reference)
xfrm = ants.create_ants_transform(
transform_type="Euler3DTransform",
center=np.asarray(center_of_mass_reference),
translation=translation)
t1_preprocessed_warped = ants.apply_ants_transform_to_image(
xfrm, t1_preprocessed, reference_image)
brain_mask_warped = ants.threshold_image(
ants.apply_ants_transform_to_image(xfrm, brain_mask, reference_image),
0.5, 1.1, 1, 0)
################################
#
# Gaussian normalize intensity based on brain mask
#
################################
mean_t1 = t1_preprocessed_warped[brain_mask_warped > 0].mean()
std_t1 = t1_preprocessed_warped[brain_mask_warped > 0].std()
t1_preprocessed_warped = (t1_preprocessed_warped - mean_t1) / std_t1
t1_preprocessed_warped = t1_preprocessed_warped * brain_mask_warped
################################
#
# Build models and load weights
#
################################
number_of_models = 1
if use_ensemble == True:
number_of_models = 3
if verbose == True:
print("Claustrum: retrieving axial model weights.")
unet_axial_models = list()
for i in range(number_of_models):
weights_file_name = get_pretrained_network(
"claustrum_axial_" + str(i),
antsxnet_cache_directory=antsxnet_cache_directory)
unet_axial_models.append(
create_sysu_media_unet_model_2d((*image_size, number_of_channels),
anatomy="claustrum"))
unet_axial_models[i].load_weights(weights_file_name)
if verbose == True:
print("Claustrum: retrieving coronal model weights.")
unet_coronal_models = list()
for i in range(number_of_models):
weights_file_name = get_pretrained_network(
"claustrum_coronal_" + str(i),
antsxnet_cache_directory=antsxnet_cache_directory)
unet_coronal_models.append(
create_sysu_media_unet_model_2d((*image_size, number_of_channels),
anatomy="claustrum"))
unet_coronal_models[i].load_weights(weights_file_name)
################################
#
# Extract slices
#
################################
dimensions_to_predict = [1, 2]
batch_coronal_X = np.zeros(
(t1_preprocessed_warped.shape[1], *image_size, number_of_channels))
batch_axial_X = np.zeros(
(t1_preprocessed_warped.shape[2], *image_size, number_of_channels))
for d in range(len(dimensions_to_predict)):
number_of_slices = t1_preprocessed_warped.shape[
dimensions_to_predict[d]]
if verbose == True:
print("Extracting slices for dimension ", dimensions_to_predict[d],
".")
for i in range(number_of_slices):
t1_slice = pad_or_crop_image_to_size(
ants.slice_image(t1_preprocessed_warped,
dimensions_to_predict[d], i), image_size)
if dimensions_to_predict[d] == 1:
batch_coronal_X[i, :, :, 0] = np.rot90(t1_slice.numpy(), k=-1)
else:
batch_axial_X[i, :, :, 0] = np.rot90(t1_slice.numpy())
################################
#
# Do prediction and then restack into the image
#
################################
if verbose == True:
print("Coronal prediction.")
prediction_coronal = unet_coronal_models[0].predict(batch_coronal_X,
verbose=verbose)
if number_of_models > 1:
for i in range(1, number_of_models, 1):
prediction_coronal += unet_coronal_models[i].predict(
batch_coronal_X, verbose=verbose)
prediction_coronal /= number_of_models
for i in range(t1_preprocessed_warped.shape[1]):
prediction_coronal[i, :, :, 0] = np.rot90(
np.squeeze(prediction_coronal[i, :, :, 0]))
if verbose == True:
print("Axial prediction.")
prediction_axial = unet_axial_models[0].predict(batch_axial_X,
verbose=verbose)
if number_of_models > 1:
for i in range(1, number_of_models, 1):
prediction_axial += unet_axial_models[i].predict(batch_axial_X,
verbose=verbose)
prediction_axial /= number_of_models
for i in range(t1_preprocessed_warped.shape[2]):
prediction_axial[i, :, :,
0] = np.rot90(np.squeeze(prediction_axial[i, :, :,
0]),
k=-1)
if verbose == True:
print("Restack image and transform back to native space.")
permutations = list()
permutations.append((0, 1, 2))
permutations.append((1, 0, 2))
permutations.append((1, 2, 0))
prediction_image_average = ants.image_clone(t1_preprocessed_warped) * 0
for d in range(len(dimensions_to_predict)):
which_batch_slices = range(
t1_preprocessed_warped.shape[dimensions_to_predict[d]])
prediction_per_dimension = None
if dimensions_to_predict[d] == 1:
prediction_per_dimension = prediction_coronal[
which_batch_slices, :, :, :]
else:
prediction_per_dimension = prediction_axial[
which_batch_slices, :, :, :]
prediction_array = np.transpose(np.squeeze(prediction_per_dimension),
permutations[dimensions_to_predict[d]])
prediction_image = ants.copy_image_info(
t1_preprocessed_warped,
pad_or_crop_image_to_size(ants.from_numpy(prediction_array),
t1_preprocessed_warped.shape))
prediction_image_average = prediction_image_average + (
prediction_image - prediction_image_average) / (d + 1)
probability_image = ants.apply_ants_transform_to_image(
ants.invert_ants_transform(xfrm), prediction_image_average,
t1) * ants.threshold_image(brain_mask, 0.5, 1, 1, 0)
return (probability_image)
def 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 apply_super_resolution_model_to_image(
image,
model,
target_range=(-127.5, 127.5),
batch_size=32,
regression_order=None,
verbose=False,
):
"""
Apply a pretrained deep back projection model for super resolution.
Helper function for applying a pretrained deep back projection model.
Apply a patch-wise trained network to perform super-resolution. Can be applied
to variable sized inputs. Warning: This function may be better used on CPU
unless the GPU can accommodate the full image size. Warning 2: The global
intensity range (min to max) of the output will match the input where the
range is taken over all channels.
Arguments
---------
image : ANTs image
input image.
model : keras object or string
pretrained keras model or filename.
target_range : 2-element tuple
a tuple or array defining the (min, max) of the input image
(e.g., -127.5, 127.5). Output images will be scaled back to original
intensity. This range should match the mapping used in the training
of the network.
batch_size : integer
Batch size used for the prediction call.
regression_order : integer
If specified, Apply the function regression_match_image with
poly_order=regression_order.
verbose : boolean
If True, show status messages.
Returns
-------
Super-resolution image upscaled to resolution specified by the network.
Example
-------
>>> import ants
>>> image = ants.image_read(ants.get_ants_data('r16'))
>>> image_sr = apply_super_resolution_model_to_image(image, get_pretrained_network("dbpn4x"))
"""
tflite_flag = False
channel_axis = 0
if K.image_data_format() == "channels_last":
channel_axis = -1
if target_range[0] > target_range[1]:
target_range = target_range[::-1]
start_time = time.time()
if isinstance(model, str):
if path.isfile(model):
if verbose:
print("Load model.")
if path.splitext(model)[1] == '.tflite':
interpreter = tf.lite.Interpreter(model)
interpreter.allocate_tensors()
input_details = interpreter.get_input_details()
output_details = interpreter.get_output_details()
shape_length = len(interpreter.get_input_details()[0]['shape'])
else:
model = load_model(model)
shape_length = len(model.input_shape)
if verbose:
elapsed_time = time.time() - start_time
print(" (elapsed time: ", elapsed_time, ")")
else:
raise ValueError("Model not found.")
else:
shape_length = len(model.input_shape)
if shape_length < 4 | shape_length > 5:
raise ValueError("Unexpected input shape.")
else:
if shape_length == 5 & image.dimension != 3:
raise ValueError("Expecting 3D input for this model.")
elif shape_length == 4 & image.dimension != 2:
raise ValueError("Expecting 2D input for this model.")
if channel_axis == -1:
channel_axis < shape_length
if tflite_flag:
channel_size = interpreter.get_input_details()[0]['shape'][channel_axis]
else:
channel_size = model.input_shape[channel_axis]
if channel_size != image.components:
raise ValueError(
"Channel size of model",
str(channel_size),
"does not match ncomponents=",
str(image.components),
"of the input image.",
)
image_patches = extract_image_patches(
image,
patch_size=image.shape,
max_number_of_patches=1,
stride_length=image.shape,
return_as_array=True,
)
if image.components == 1:
image_patches = np.expand_dims(image_patches, axis=-1)
image_patches = image_patches - image_patches.min()
image_patches = (
image_patches / image_patches.max() * (target_range[1] - target_range[0])
+ target_range[0]
)
if verbose:
print("Prediction")
start_time = time.time()
if tflite_flag:
image_patches = image_patches.astype('float32')
interpreter.set_tensor(input_details[0]['index'], image_patches)
interpreter.invoke()
out = interpreter.tensor(output_details[0]['index'])
prediction = out()
else:
prediction = model.predict(image_patches, batch_size=batch_size)
if verbose:
elapsed_time = time.time() - start_time
print(" (elapsed time: ", elapsed_time, ")")
if verbose:
print("Reconstruct intensities")
intensity_range = image.range()
prediction = prediction - prediction.min()
prediction = (
prediction / prediction.max() * (intensity_range[1] - intensity_range[0])
+ intensity_range[0]
)
def slice_array_channel(input_array, slice, channel_axis=-1):
if channel_axis == 0:
if shape_length == 4:
return input_array[slice, :, :, :]
else:
return input_array[slice, :, :, :, :]
else:
if shape_length == 4:
return input_array[:, :, :, slice]
else:
return input_array[:, :, :, :, slice]
expansion_factor = np.asarray(prediction.shape) / np.asarray(image_patches.shape)
if channel_axis == 0:
FIXME
expansion_factor = expansion_factor[1 : (len(expansion_factor) - 1)]
if verbose:
print("ExpansionFactor:", str(expansion_factor))
if image.components == 1:
image_array = slice_array_channel(prediction, 0, channel_axis)
prediction_image = ants.make_image(
(np.asarray(image.shape) * np.asarray(expansion_factor)).astype(int),
image_array,
)
if regression_order is not None:
reference_image = ants.resample_image_to_target(image, prediction_image)
prediction_image = regression_match_image(
prediction_image, reference_image, poly_order=regression_order
)
else:
image_component_list = list()
for k in range(image.components):
image_array = slice_array_channel(prediction, k, channel_axis)
image_component_list.append(
ants.make_image(
(np.asarray(image.shape) * np.asarray(expansion_factor)).astype(
int
),
image_array,
)
)
prediction_image = ants.merge_channels(image_component_list)
prediction_image = ants.copy_image_info(image, prediction_image)
ants.set_spacing(
prediction_image,
tuple(np.asarray(image.spacing) / np.asarray(expansion_factor)),
)
return prediction_image
def deep_atropos(t1,
do_preprocessing=True,
use_spatial_priors=1,
antsxnet_cache_directory=None,
verbose=False):
"""
Six-tissue segmentation.
Perform Atropos-style six tissue segmentation using deep learning.
The labeling is as follows:
Label 0 : background
Label 1 : CSF
Label 2 : gray matter
Label 3 : white matter
Label 4 : deep gray matter
Label 5 : brain stem
Label 6 : cerebellum
Preprocessing on the training data consisted of:
* n4 bias correction,
* denoising,
* 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.
use_spatial_priors : integer
Use MNI spatial tissue priors (0 or 1). Currently, only '0' (no priors) and '1'
(cerebellar prior only) are the only two options. Default is 1.
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")
>>> flash = deep_atropos(image)
"""
from ..architectures import create_unet_model_3d
from ..utilities import get_pretrained_network
from ..utilities import get_antsxnet_data
from ..utilities import preprocess_brain_image
from ..utilities import extract_image_patches
from ..utilities import reconstruct_image_from_patches
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']
################################
#
# 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", "csf", "gray matter", "white matter",
"deep gray matter", "brain stem", "cerebellum")
mni_priors = None
channel_size = 1
if use_spatial_priors != 0:
mni_priors = ants.ndimage_to_list(
ants.image_read(
get_antsxnet_data(
"croppedMni152Priors",
antsxnet_cache_directory=antsxnet_cache_directory)))
for i in range(len(mni_priors)):
mni_priors[i] = ants.copy_image_info(t1_preprocessed,
mni_priors[i])
channel_size = 2
unet_model = create_unet_model_3d((*patch_size, channel_size),
number_of_outputs=len(classes),
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"))
if verbose == True:
print("DeepAtropos: retrieving model weights.")
weights_file_name = ''
if use_spatial_priors == 0:
weights_file_name = get_pretrained_network(
"sixTissueOctantBrainSegmentation",
antsxnet_cache_directory=antsxnet_cache_directory)
elif use_spatial_priors == 1:
weights_file_name = get_pretrained_network(
"sixTissueOctantBrainSegmentationWithPriors1",
antsxnet_cache_directory=antsxnet_cache_directory)
else:
raise ValueError("use_spatial_priors must be a 0 or 1")
unet_model.load_weights(weights_file_name)
################################
#
# Do prediction and normalize to native space
#
################################
if verbose == True:
print("Prediction.")
t1_preprocessed = (t1_preprocessed -
t1_preprocessed.mean()) / t1_preprocessed.std()
image_patches = extract_image_patches(t1_preprocessed,
patch_size=patch_size,
max_number_of_patches="all",
stride_length=stride_length,
return_as_array=True)
batchX = np.zeros((*image_patches.shape, channel_size))
batchX[:, :, :, :, 0] = image_patches
if channel_size > 1:
prior_patches = extract_image_patches(mni_priors[6],
patch_size=patch_size,
max_number_of_patches="all",
stride_length=stride_length,
return_as_array=True)
batchX[:, :, :, :, 1] = prior_patches
predicted_data = unet_model.predict(batchX, verbose=verbose)
probability_images = list()
for i in range(len(classes)):
if verbose == True:
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)
image_matrix = ants.image_list_to_matrix(probability_images, t1 * 0 + 1)
segmentation_matrix = np.argmax(image_matrix, axis=0)
segmentation_image = ants.matrix_to_images(
np.expand_dims(segmentation_matrix, axis=0), t1 * 0 + 1)[0]
return_dict = {
'segmentation_image': segmentation_image,
'probability_images': probability_images
}
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)
def hippmapp3r_segmentation(t1,
do_preprocessing=True,
antsxnet_cache_directory=None,
verbose=False):
"""
Perform HippMapp3r (hippocampal) segmentation described in
https://www.ncbi.nlm.nih.gov/pubmed/31609046
with models and architecture ported from
https://github.com/mgoubran/HippMapp3r
Additional documentation and attribution resources found at
https://hippmapp3r.readthedocs.io/en/latest/
Preprocessing consists of:
* n4 bias correction and
* brain extraction
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
input image
do_preprocessing : boolean
See description above.
antsxnet_cache_directory : string
Destination directory for storing the downloaded template and model weights.
Since these can be resused, if is None, these data will be downloaded to a
~/.keras/ANTsXNet/.
verbose : boolean
Print progress to the screen.
Returns
-------
ANTs labeled hippocampal image.
Example
-------
>>> mask = hippmapp3r_segmentation(t1)
"""
from ..architectures import create_hippmapp3r_unet_model_3d
from ..utilities import preprocess_brain_image
from ..utilities import get_pretrained_network
from ..utilities import get_antsxnet_data
if t1.dimension != 3:
raise ValueError("Image dimension must be 3.")
if antsxnet_cache_directory == None:
antsxnet_cache_directory = "ANTsXNet"
if verbose == True:
print("************* Preprocessing ***************")
print("")
t1_preprocessed = t1
if do_preprocessing == True:
t1_preprocessing = preprocess_brain_image(
t1,
truncate_intensity=None,
brain_extraction_modality="t1",
template=None,
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']
if verbose == True:
print("************* Initial stage segmentation ***************")
print("")
# Normalize to mprage_hippmapp3r space
if verbose == True:
print(" HippMapp3r: template normalization.")
template_file_name_path = get_antsxnet_data(
"mprage_hippmapp3r", antsxnet_cache_directory=antsxnet_cache_directory)
template_image = ants.image_read(template_file_name_path)
registration = ants.registration(
fixed=template_image,
moving=t1_preprocessed,
type_of_transform="antsRegistrationSyNQuickRepro[t]",
verbose=verbose)
image = registration['warpedmovout']
transforms = dict(fwdtransforms=registration['fwdtransforms'],
invtransforms=registration['invtransforms'])
# Threshold at 10th percentile of non-zero voxels in "robust range (fslmaths)"
if verbose == True:
print(" HippMapp3r: threshold.")
image_array = image.numpy()
image_robust_range = np.quantile(image_array[np.where(image_array != 0)],
(0.02, 0.98))
threshold_value = 0.10 * (image_robust_range[1] -
image_robust_range[0]) + image_robust_range[0]
thresholded_mask = ants.threshold_image(image, -10000, threshold_value, 0,
1)
thresholded_image = image * thresholded_mask
# Standardize image
if verbose == True:
print(" HippMapp3r: standardize.")
mean_image = np.mean(thresholded_image[thresholded_mask == 1])
sd_image = np.std(thresholded_image[thresholded_mask == 1])
image_normalized = (image - mean_image) / sd_image
image_normalized = image_normalized * thresholded_mask
# Trim and resample image
if verbose == True:
print(" HippMapp3r: trim and resample to (160, 160, 128).")
image_cropped = ants.crop_image(image_normalized, thresholded_mask, 1)
shape_initial_stage = (160, 160, 128)
image_resampled = ants.resample_image(image_cropped,
shape_initial_stage,
use_voxels=True,
interp_type=1)
if verbose == True:
print(" HippMapp3r: generate first network and download weights.")
model_initial_stage = create_hippmapp3r_unet_model_3d(
(*shape_initial_stage, 1), do_first_network=True)
initial_stage_weights_file_name = get_pretrained_network(
"hippMapp3rInitial", antsxnet_cache_directory=antsxnet_cache_directory)
model_initial_stage.load_weights(initial_stage_weights_file_name)
if verbose == True:
print(" HippMapp3r: prediction.")
data_initial_stage = np.expand_dims(image_resampled.numpy(), axis=0)
data_initial_stage = np.expand_dims(data_initial_stage, axis=-1)
mask_array = model_initial_stage.predict(data_initial_stage,
verbose=verbose)
mask_image_resampled = ants.copy_image_info(
image_resampled, ants.from_numpy(np.squeeze(mask_array)))
mask_image = ants.resample_image(mask_image_resampled,
image.shape,
use_voxels=True,
interp_type=0)
mask_image[mask_image >= 0.5] = 1
mask_image[mask_image < 0.5] = 0
#########################################
#
# Perform refined (stage 2) segmentation
#
if verbose == True:
print("")
print("")
print("************* Refine stage segmentation ***************")
print("")
mask_array = np.squeeze(mask_array)
centroid_indices = np.where(mask_array == 1)
centroid = np.zeros((3, ))
centroid[0] = centroid_indices[0].mean()
centroid[1] = centroid_indices[1].mean()
centroid[2] = centroid_indices[2].mean()
shape_refine_stage = (112, 112, 64)
lower = (np.floor(centroid - 0.5 * np.array(shape_refine_stage)) -
1).astype(int)
upper = (lower + np.array(shape_refine_stage)).astype(int)
image_trimmed = ants.crop_indices(image_resampled, lower.astype(int),
upper.astype(int))
if verbose == True:
print(" HippMapp3r: generate second network and download weights.")
model_refine_stage = create_hippmapp3r_unet_model_3d(
(*shape_refine_stage, 1), do_first_network=False)
refine_stage_weights_file_name = get_pretrained_network(
"hippMapp3rRefine", antsxnet_cache_directory=antsxnet_cache_directory)
model_refine_stage.load_weights(refine_stage_weights_file_name)
data_refine_stage = np.expand_dims(image_trimmed.numpy(), axis=0)
data_refine_stage = np.expand_dims(data_refine_stage, axis=-1)
if verbose == True:
print(" HippMapp3r: Monte Carlo iterations (SpatialDropout).")
number_of_mci_iterations = 30
prediction_refine_stage = np.zeros(shape_refine_stage)
for i in range(number_of_mci_iterations):
tf.random.set_seed(i)
if verbose == True:
print(" Monte Carlo iteration", i + 1, "out of",
number_of_mci_iterations)
prediction_refine_stage = \
(np.squeeze(model_refine_stage.predict(data_refine_stage, verbose=verbose)) + \
i * prediction_refine_stage ) / (i + 1)
prediction_refine_stage_array = np.zeros(image_resampled.shape)
prediction_refine_stage_array[lower[0]:upper[0], lower[1]:upper[1],
lower[2]:upper[2]] = prediction_refine_stage
probability_mask_refine_stage_resampled = ants.copy_image_info(
image_resampled, ants.from_numpy(prediction_refine_stage_array))
segmentation_image_resampled = ants.label_clusters(ants.threshold_image(
probability_mask_refine_stage_resampled, 0.0, 0.5, 0, 1),
min_cluster_size=10)
segmentation_image_resampled[segmentation_image_resampled > 2] = 0
geom = ants.label_geometry_measures(segmentation_image_resampled)
if len(geom['VolumeInMillimeters']) < 2:
raise ValueError("Error: left and right hippocampus not found.")
if geom['Centroid_x'][0] < geom['Centroid_x'][1]:
segmentation_image_resampled[segmentation_image_resampled == 1] = 3
segmentation_image_resampled[segmentation_image_resampled == 2] = 1
segmentation_image_resampled[segmentation_image_resampled == 3] = 2
segmentation_image = ants.apply_transforms(
fixed=t1,
moving=segmentation_image_resampled,
transformlist=transforms['invtransforms'],
whichtoinvert=[True],
interpolator="genericLabel",
verbose=verbose)
return (segmentation_image)
def brain_extraction(image,
modality="t1",
antsxnet_cache_directory=None,
verbose=False):
"""
Perform brain extraction using U-net and ANTs-based training data. "NoBrainer"
is also possible where brain extraction uses U-net and FreeSurfer training data
ported from the
https://github.com/neuronets/nobrainer-models
Arguments
---------
image : ANTsImage
input image (or list of images for multi-modal scenarios).
modality : string
Modality image type. Options include:
* "t1": T1-weighted MRI---ANTs-trained. Update from "t1v0".
* "t1v0": T1-weighted MRI---ANTs-trained.
* "t1nobrainer": T1-weighted MRI---FreeSurfer-trained: h/t Satra Ghosh and Jakub Kaczmarzyk.
* "t1combined": Brian's combination of "t1" and "t1nobrainer". One can also specify
"t1combined[X]" where X is the morphological radius. X = 12 by default.
* "flair": FLAIR MRI.
* "t2": T2 MRI.
* "bold": 3-D BOLD MRI.
* "fa": Fractional anisotropy.
* "t1t2infant": Combined T1-w/T2-w infant MRI h/t Martin Styner.
* "t1infant": T1-w infant MRI h/t Martin Styner.
* "t2infant": T2-w infant MRI h/t Martin Styner.
antsxnet_cache_directory : string
Destination directory for storing the downloaded template and model weights.
Since these can be resused, if is None, these data will be downloaded to a
~/.keras/ANTsXNet/.
verbose : boolean
Print progress to the screen.
Returns
-------
ANTs probability brain mask image.
Example
-------
>>> probability_brain_mask = brain_extraction(brain_image, modality="t1")
"""
from ..architectures import create_unet_model_3d
from ..utilities import get_pretrained_network
from ..utilities import get_antsxnet_data
from ..architectures import create_nobrainer_unet_model_3d
classes = ("background", "brain")
number_of_classification_labels = len(classes)
channel_size = 1
if isinstance(image, list):
channel_size = len(image)
if antsxnet_cache_directory == None:
antsxnet_cache_directory = "ANTsXNet"
input_images = list()
if channel_size == 1:
input_images.append(image)
else:
input_images = image
if input_images[0].dimension != 3:
raise ValueError("Image dimension must be 3.")
if "t1combined" in modality:
brain_extraction_t1 = brain_extraction(
image,
modality="t1",
antsxnet_cache_directory=antsxnet_cache_directory,
verbose=verbose)
brain_mask = ants.iMath_get_largest_component(
ants.threshold_image(brain_extraction_t1, 0.5, 10000))
# Need to change with voxel resolution
morphological_radius = 12
if '[' in modality and ']' in modality:
morphological_radius = int(modality.split("[")[1].split("]")[0])
brain_extraction_t1nobrainer = brain_extraction(
image * ants.iMath_MD(brain_mask, radius=morphological_radius),
modality="t1nobrainer",
antsxnet_cache_directory=antsxnet_cache_directory,
verbose=verbose)
brain_extraction_combined = ants.iMath_fill_holes(
ants.iMath_get_largest_component(brain_extraction_t1nobrainer *
brain_mask))
brain_extraction_combined = brain_extraction_combined + ants.iMath_ME(
brain_mask, morphological_radius) + brain_mask
return (brain_extraction_combined)
if modality != "t1nobrainer":
#####################
#
# ANTs-based
#
#####################
weights_file_name_prefix = None
if modality == "t1v0":
weights_file_name_prefix = "brainExtraction"
elif modality == "t1":
weights_file_name_prefix = "brainExtractionT1"
elif modality == "t2":
weights_file_name_prefix = "brainExtractionT2"
elif modality == "flair":
weights_file_name_prefix = "brainExtractionFLAIR"
elif modality == "bold":
weights_file_name_prefix = "brainExtractionBOLD"
elif modality == "fa":
weights_file_name_prefix = "brainExtractionFA"
elif modality == "t1t2infant":
weights_file_name_prefix = "brainExtractionInfantT1T2"
elif modality == "t1infant":
weights_file_name_prefix = "brainExtractionInfantT1"
elif modality == "t2infant":
weights_file_name_prefix = "brainExtractionInfantT2"
else:
raise ValueError("Unknown modality type.")
weights_file_name = get_pretrained_network(
weights_file_name_prefix,
antsxnet_cache_directory=antsxnet_cache_directory)
if verbose == True:
print("Brain extraction: retrieving template.")
reorient_template_file_name_path = get_antsxnet_data(
"S_template3", antsxnet_cache_directory=antsxnet_cache_directory)
reorient_template = ants.image_read(reorient_template_file_name_path)
resampled_image_size = reorient_template.shape
if modality == "t1":
classes = ("background", "head", "brain")
number_of_classification_labels = len(classes)
unet_model = create_unet_model_3d(
(*resampled_image_size, channel_size),
number_of_outputs=number_of_classification_labels,
number_of_layers=4,
number_of_filters_at_base_layer=8,
dropout_rate=0.0,
convolution_kernel_size=(3, 3, 3),
deconvolution_kernel_size=(2, 2, 2),
weight_decay=1e-5)
unet_model.load_weights(weights_file_name)
if verbose == True:
print("Brain extraction: normalizing image to the template.")
center_of_mass_template = ants.get_center_of_mass(reorient_template)
center_of_mass_image = ants.get_center_of_mass(input_images[0])
translation = np.asarray(center_of_mass_image) - np.asarray(
center_of_mass_template)
xfrm = ants.create_ants_transform(
transform_type="Euler3DTransform",
center=np.asarray(center_of_mass_template),
translation=translation)
batchX = np.zeros((1, *resampled_image_size, channel_size))
for i in range(len(input_images)):
warped_image = ants.apply_ants_transform_to_image(
xfrm, input_images[i], reorient_template)
warped_array = warped_image.numpy()
batchX[0, :, :, :, i] = (warped_array -
warped_array.mean()) / warped_array.std()
if verbose == True:
print("Brain extraction: prediction and decoding.")
predicted_data = unet_model.predict(batchX, verbose=verbose)
origin = reorient_template.origin
spacing = reorient_template.spacing
direction = reorient_template.direction
probability_images_array = list()
probability_images_array.append(
ants.from_numpy(np.squeeze(predicted_data[0, :, :, :, 0]),
origin=origin,
spacing=spacing,
direction=direction))
probability_images_array.append(
ants.from_numpy(np.squeeze(predicted_data[0, :, :, :, 1]),
origin=origin,
spacing=spacing,
direction=direction))
if modality == "t1":
probability_images_array.append(
ants.from_numpy(np.squeeze(predicted_data[0, :, :, :, 2]),
origin=origin,
spacing=spacing,
direction=direction))
if verbose == True:
print(
"Brain extraction: renormalize probability mask to native space."
)
probability_image = ants.apply_ants_transform_to_image(
ants.invert_ants_transform(xfrm),
probability_images_array[number_of_classification_labels - 1],
input_images[0])
return (probability_image)
else:
#####################
#
# NoBrainer
#
#####################
if verbose == True:
print("NoBrainer: generating network.")
model = create_nobrainer_unet_model_3d((None, None, None, 1))
weights_file_name = get_pretrained_network(
"brainExtractionNoBrainer",
antsxnet_cache_directory=antsxnet_cache_directory)
model.load_weights(weights_file_name)
if verbose == True:
print(
"NoBrainer: preprocessing (intensity truncation and resampling)."
)
image_array = image.numpy()
image_robust_range = np.quantile(
image_array[np.where(image_array != 0)], (0.02, 0.98))
threshold_value = 0.10 * (image_robust_range[1] - image_robust_range[0]
) + image_robust_range[0]
thresholded_mask = ants.threshold_image(image, -10000, threshold_value,
0, 1)
thresholded_image = image * thresholded_mask
image_resampled = ants.resample_image(thresholded_image,
(256, 256, 256),
use_voxels=True)
image_array = np.expand_dims(image_resampled.numpy(), axis=0)
image_array = np.expand_dims(image_array, axis=-1)
if verbose == True:
print("NoBrainer: predicting mask.")
brain_mask_array = np.squeeze(
model.predict(image_array, verbose=verbose))
brain_mask_resampled = ants.copy_image_info(
image_resampled, ants.from_numpy(brain_mask_array))
brain_mask_image = ants.resample_image(brain_mask_resampled,
image.shape,
use_voxels=True,
interp_type=1)
spacing = ants.get_spacing(image)
spacing_product = spacing[0] * spacing[1] * spacing[2]
minimum_brain_volume = round(649933.7 / spacing_product)
brain_mask_labeled = ants.label_clusters(brain_mask_image,
minimum_brain_volume)
return (brain_mask_labeled)
