def test_ndimage_to_list(self):
image = ants.image_read(ants.get_ants_data('r16'))
image2 = ants.image_read(ants.get_ants_data('r64'))
ants.set_spacing(image, (2, 2))
ants.set_spacing(image2, (2, 2))
imageTar = ants.make_image((*image2.shape, 2))
ants.set_spacing(imageTar, (2, 2, 2))
image3 = ants.list_to_ndimage(imageTar, [image, image2])
self.assertEqual(image3.dimension, 3)
ants.set_direction(image3, np.eye(3) * 2)
images_unmerged = ants.ndimage_to_list(image3)
self.assertEqual(len(images_unmerged), 2)
self.assertEqual(images_unmerged[0].dimension, 2)
output_file_name = args[2]
start_time_total = time.time()
print("Reading ", input_file_name)
start_time = time.time()
input_image = ants.image_read(input_file_name)
end_time = time.time()
elapsed_time = end_time - start_time
print(" (elapsed time: ", elapsed_time, " seconds)")
dimension = len(input_image.shape)
input_image_list = list()
if dimension == 4:
input_image_list = ants.ndimage_to_list(input_image)
elif dimension == 2:
raise ValueError("Model for 3-D or 4-D images only.")
elif dimension == 3:
input_image_list.append(input_image)
model = antspynet.create_deep_back_projection_network_model_3d(
(*input_image_list[0].shape, 1),
number_of_outputs=1,
number_of_base_filters=64,
number_of_feature_filters=256,
number_of_back_projection_stages=7,
convolution_kernel_size=(3, 3, 3),
strides=(2, 2, 2),
number_of_loss_functions=1)
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.")
networks = powers_areal_mni_itk['SystemName'].unique()
dfnpts = np.where( powers_areal_mni_itk['SystemName'] == networks[5] )
dfnImg = ants.mask_image( ptImg, ptImg, level = dfnpts[0].tolist(), binarize=False )
# plot( und, ptImg, axis=3, window.overlay = range( ptImg ) )
bold2ch2 = ants.apply_transforms( ch2, und, concatx2, whichtoinvert = ( True, False, True, False ) )
# Extracting canonical functional network maps
## preprocessing
csfAndWM = ( ants.threshold_image( boldseg, 1, 1 ) +
ants.threshold_image( boldseg, 3, 3 ) ).morphology("erode",1)
bold = ants.image_read( boldfnsR )
boldList = ants.ndimage_to_list( bold )
avgBold = ants.get_average_of_timeseries( bold, range( 5 ) )
boldUndTX = ants.registration( und, avgBold, "SyN", regIterations = (15,4),
synMetric = "CC", synSampling = 2, verbose = False )
boldUndTS = ants.apply_transforms( und, bold, boldUndTX['fwdtransforms'], imagetype = 3 )
motCorr = ants.motion_correction( boldUndTS, avgBold,
type_of_transform="Rigid", verbose = True )
tr = ants.get_spacing( bold )[3]
highMotionTimes = np.where( motCorr['FD'] >= 0.5 )
goodtimes = np.where( motCorr['FD'] < 0.5 )
avgBold = ants.get_average_of_timeseries( motCorr['motion_corrected'], range( 5 ) )
#######################
nt = len(motCorr['FD'])
plt.plot( range( nt ), motCorr['FD'] )
plt.show()
#################################################
def desikan_killiany_tourville_labeling(t1,
do_preprocessing=True,
return_probability_images=False,
antsxnet_cache_directory=None,
verbose=False):
"""
Cortical and deep gray matter labeling using Desikan-Killiany-Tourville
Perform DKT labeling using deep learning
The labeling is as follows:
Inner labels:
Label 0: background
Label 4: left lateral ventricle
Label 5: left inferior lateral ventricle
Label 6: left cerebellem exterior
Label 7: left cerebellum white matter
Label 10: left thalamus proper
Label 11: left caudate
Label 12: left putamen
Label 13: left pallidium
Label 15: 4th ventricle
Label 16: brain stem
Label 17: left hippocampus
Label 18: left amygdala
Label 24: CSF
Label 25: left lesion
Label 26: left accumbens area
Label 28: left ventral DC
Label 30: left vessel
Label 43: right lateral ventricle
Label 44: right inferior lateral ventricle
Label 45: right cerebellum exterior
Label 46: right cerebellum white matter
Label 49: right thalamus proper
Label 50: right caudate
Label 51: right putamen
Label 52: right palladium
Label 53: right hippocampus
Label 54: right amygdala
Label 57: right lesion
Label 58: right accumbens area
Label 60: right ventral DC
Label 62: right vessel
Label 72: 5th ventricle
Label 85: optic chasm
Label 91: left basal forebrain
Label 92: right basal forebrain
Label 630: cerebellar vermal lobules I-V
Label 631: cerebellar vermal lobules VI-VII
Label 632: cerebellar vermal lobules VIII-X
Outer labels:
Label 1002: left caudal anterior cingulate
Label 1003: left caudal middle frontal
Label 1005: left cuneus
Label 1006: left entorhinal
Label 1007: left fusiform
Label 1008: left inferior parietal
Label 1009: left inferior temporal
Label 1010: left isthmus cingulate
Label 1011: left lateral occipital
Label 1012: left lateral orbitofrontal
Label 1013: left lingual
Label 1014: left medial orbitofrontal
Label 1015: left middle temporal
Label 1016: left parahippocampal
Label 1017: left paracentral
Label 1018: left pars opercularis
Label 1019: left pars orbitalis
Label 1020: left pars triangularis
Label 1021: left pericalcarine
Label 1022: left postcentral
Label 1023: left posterior cingulate
Label 1024: left precentral
Label 1025: left precuneus
Label 1026: left rostral anterior cingulate
Label 1027: left rostral middle frontal
Label 1028: left superior frontal
Label 1029: left superior parietal
Label 1030: left superior temporal
Label 1031: left supramarginal
Label 1034: left transverse temporal
Label 1035: left insula
Label 2002: right caudal anterior cingulate
Label 2003: right caudal middle frontal
Label 2005: right cuneus
Label 2006: right entorhinal
Label 2007: right fusiform
Label 2008: right inferior parietal
Label 2009: right inferior temporal
Label 2010: right isthmus cingulate
Label 2011: right lateral occipital
Label 2012: right lateral orbitofrontal
Label 2013: right lingual
Label 2014: right medial orbitofrontal
Label 2015: right middle temporal
Label 2016: right parahippocampal
Label 2017: right paracentral
Label 2018: right pars opercularis
Label 2019: right pars orbitalis
Label 2020: right pars triangularis
Label 2021: right pericalcarine
Label 2022: right postcentral
Label 2023: right posterior cingulate
Label 2024: right precentral
Label 2025: right precuneus
Label 2026: right rostral anterior cingulate
Label 2027: right rostral middle frontal
Label 2028: right superior frontal
Label 2029: right superior parietal
Label 2030: right superior temporal
Label 2031: right supramarginal
Label 2034: right transverse temporal
Label 2035: right insula
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.
return_probability_images : boolean
Whether to return the two sets of probability images for the inner and outer
labels.
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 = desikan_killiany_tourville_labeling(image)
"""
from ..architectures import create_unet_model_3d
from ..utilities import get_pretrained_network
from ..utilities import get_antsxnet_data
from ..utilities import categorical_focal_loss
from ..utilities import preprocess_brain_image
from ..utilities import crop_image_center
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),
do_brain_extraction=True,
template="croppedMni152",
template_transform_type="AffineFast",
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']
################################
#
# Download spatial priors for outer model
#
################################
spatial_priors_file_name_path = get_antsxnet_data(
"priorDktLabels", 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)
################################
#
# Build outer model and load weights
#
################################
template_size = (96, 112, 96)
labels = (0, 1002, 1003, *tuple(range(1005, 1032)), 1034, 1035, 2002, 2003,
*tuple(range(2005, 2032)), 2034, 2035)
channel_size = 1 + len(priors_image_list)
unet_model = create_unet_model_3d((*template_size, channel_size),
number_of_outputs=len(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,
add_attention_gating=True)
weights_file_name = None
weights_file_name = get_pretrained_network(
"dktOuterWithSpatialPriors",
antsxnet_cache_directory=antsxnet_cache_directory)
unet_model.load_weights(weights_file_name)
################################
#
# Do prediction and normalize to native space
#
################################
if verbose == True:
print("Outer model Prediction.")
downsampled_image = ants.resample_image(t1_preprocessed,
template_size,
use_voxels=True,
interp_type=0)
image_array = downsampled_image.numpy()
image_array = (image_array - image_array.mean()) / image_array.std()
batchX = np.zeros((1, *template_size, channel_size))
batchX[0, :, :, :, 0] = image_array
for i in range(len(priors_image_list)):
resampled_prior_image = ants.resample_image(priors_image_list[i],
template_size,
use_voxels=True,
interp_type=0)
batchX[0, :, :, :, i + 1] = resampled_prior_image.numpy()
predicted_data = unet_model.predict(batchX, verbose=verbose)
origin = downsampled_image.origin
spacing = downsampled_image.spacing
direction = downsampled_image.direction
inner_probability_images = list()
for i in range(len(labels)):
probability_image = \
ants.from_numpy(np.squeeze(predicted_data[0, :, :, :, i]),
origin=origin, spacing=spacing, direction=direction)
resampled_image = ants.resample_image(probability_image,
t1_preprocessed.shape,
use_voxels=True,
interp_type=0)
if do_preprocessing == True:
inner_probability_images.append(
ants.apply_transforms(
fixed=t1,
moving=resampled_image,
transformlist=t1_preprocessing['template_transforms']
['invtransforms'],
whichtoinvert=[True],
interpolator="linear",
verbose=verbose))
else:
inner_probability_images.append(resampled_image)
image_matrix = ants.image_list_to_matrix(inner_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]
dkt_label_image = ants.image_clone(segmentation_image)
for i in range(len(labels)):
dkt_label_image[segmentation_image == i] = labels[i]
################################
#
# Build inner model and load weights
#
################################
template_size = (160, 192, 160)
labels = (0, 4, 6, 7, 10, 11, 12, 13, 14, 15, 16, 17, 18, 24, 26, 28, 30,
43, 44, 45, 46, 49, 50, 51, 52, 53, 54, 58, 60, 91, 92, 630, 631,
632)
unet_model = create_unet_model_3d((*template_size, 1),
number_of_outputs=len(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,
add_attention_gating=True)
weights_file_name = get_pretrained_network(
"dktInner", antsxnet_cache_directory=antsxnet_cache_directory)
unet_model.load_weights(weights_file_name)
################################
#
# Do prediction and normalize to native space
#
################################
if verbose == True:
print("Prediction.")
cropped_image = ants.crop_indices(t1_preprocessed, (12, 14, 0),
(172, 206, 160))
batchX = np.expand_dims(cropped_image.numpy(), axis=0)
batchX = np.expand_dims(batchX, axis=-1)
batchX = (batchX - batchX.mean()) / batchX.std()
predicted_data = unet_model.predict(batchX, verbose=verbose)
origin = cropped_image.origin
spacing = cropped_image.spacing
direction = cropped_image.direction
outer_probability_images = list()
for i in range(len(labels)):
probability_image = \
ants.from_numpy(np.squeeze(predicted_data[0, :, :, :, i]),
origin=origin, spacing=spacing, direction=direction)
if i > 0:
decropped_image = ants.decrop_image(probability_image,
t1_preprocessed * 0)
else:
decropped_image = ants.decrop_image(probability_image,
t1_preprocessed * 0 + 1)
if do_preprocessing == True:
outer_probability_images.append(
ants.apply_transforms(
fixed=t1,
moving=decropped_image,
transformlist=t1_preprocessing['template_transforms']
['invtransforms'],
whichtoinvert=[True],
interpolator="linear",
verbose=verbose))
else:
outer_probability_images.append(decropped_image)
image_matrix = ants.image_list_to_matrix(outer_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]
################################
#
# Incorporate the inner model results into the final label image.
# Note that we purposely prioritize the inner label results.
#
################################
for i in range(len(labels)):
if labels[i] > 0:
dkt_label_image[segmentation_image == i] = labels[i]
if return_probability_images == True:
return_dict = {
'segmentation_image': dkt_label_image,
'inner_probability_images': inner_probability_images,
'outer_probability_images': outer_probability_images
}
return (return_dict)
else:
return (dkt_label_image)
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 deep_flash_deprecated(t1,
do_preprocessing=True,
do_per_hemisphere=True,
which_hemisphere_models="new",
antsxnet_cache_directory=None,
verbose=False):
"""
Hippocampal/Enthorhinal segmentation using "Deep Flash"
Perform hippocampal/entorhinal segmentation in T1 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/CA3
Label 14: right DG/CA3
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,
* 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.
do_per_hemisphere : boolean
If True, do prediction based on separate networks per hemisphere. Otherwise,
use the single network trained for both hemispheres.
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_flash(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 pad_or_crop_image_to_size
print("This function is deprecated. Please update to deep_flash().")
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:
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']
probability_images = list()
labels = (0, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18)
################################
#
# Process left/right in same network
#
################################
if do_per_hemisphere == False:
################################
#
# Build model and load weights
#
################################
template_size = (160, 192, 160)
unet_model = create_unet_model_3d(
(*template_size, 1),
number_of_outputs=len(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,
additional_options=("attentionGating", ))
if verbose:
print("DeepFlash: retrieving model weights.")
weights_file_name = get_pretrained_network(
"deepFlash", antsxnet_cache_directory=antsxnet_cache_directory)
unet_model.load_weights(weights_file_name)
################################
#
# Do prediction and normalize to native space
#
################################
if verbose:
print("Prediction.")
cropped_image = pad_or_crop_image_to_size(t1_preprocessed,
template_size)
batchX = np.expand_dims(cropped_image.numpy(), axis=0)
batchX = np.expand_dims(batchX, axis=-1)
batchX = (batchX - batchX.mean()) / batchX.std()
predicted_data = unet_model.predict(batchX, verbose=verbose)
origin = cropped_image.origin
spacing = cropped_image.spacing
direction = cropped_image.direction
for i in range(len(labels)):
probability_image = \
ants.from_numpy(np.squeeze(predicted_data[0, :, :, :, i]),
origin=origin, spacing=spacing, direction=direction)
if i > 0:
decropped_image = ants.decrop_image(probability_image,
t1_preprocessed * 0)
else:
decropped_image = ants.decrop_image(probability_image,
t1_preprocessed * 0 + 1)
if do_preprocessing:
probability_images.append(
ants.apply_transforms(
fixed=t1,
moving=decropped_image,
transformlist=t1_preprocessing['template_transforms']
['invtransforms'],
whichtoinvert=[True],
interpolator="linear",
verbose=verbose))
else:
probability_images.append(decropped_image)
################################
#
# Process left/right in split networks
#
################################
else:
################################
#
# Left: download spatial priors
#
################################
spatial_priors_left_file_name_path = get_antsxnet_data(
"priorDeepFlashLeftLabels",
antsxnet_cache_directory=antsxnet_cache_directory)
spatial_priors_left = ants.image_read(
spatial_priors_left_file_name_path)
priors_image_left_list = ants.ndimage_to_list(spatial_priors_left)
################################
#
# Left: build model and load weights
#
################################
template_size = (64, 96, 96)
labels_left = (0, 5, 7, 9, 11, 13, 15, 17)
channel_size = 1 + len(labels_left)
number_of_filters = 16
network_name = ''
if which_hemisphere_models == "old":
network_name = "deepFlashLeft16"
elif which_hemisphere_models == "new":
network_name = "deepFlashLeft16new"
else:
raise ValueError("network_name must be \"old\" or \"new\".")
unet_model = create_unet_model_3d(
(*template_size, channel_size),
number_of_outputs=len(labels_left),
number_of_layers=4,
number_of_filters_at_base_layer=number_of_filters,
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:
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).")
cropped_image = ants.crop_indices(t1_preprocessed, (30, 51, 0),
(94, 147, 96))
image_array = cropped_image.numpy()
image_array = (image_array - image_array.mean()) / image_array.std()
batchX = np.zeros((1, *template_size, channel_size))
batchX[0, :, :, :, 0] = image_array
for i in range(len(priors_image_left_list)):
cropped_prior = ants.crop_indices(priors_image_left_list[i],
(30, 51, 0), (94, 147, 96))
batchX[0, :, :, :, i + 1] = cropped_prior.numpy()
predicted_data = unet_model.predict(batchX, verbose=verbose)
origin = cropped_image.origin
spacing = cropped_image.spacing
direction = cropped_image.direction
probability_images_left = list()
for i in range(len(labels_left)):
probability_image = \
ants.from_numpy(np.squeeze(predicted_data[0, :, :, :, i]),
origin=origin, spacing=spacing, direction=direction)
if i > 0:
decropped_image = ants.decrop_image(probability_image,
t1_preprocessed * 0)
else:
decropped_image = ants.decrop_image(probability_image,
t1_preprocessed * 0 + 1)
if do_preprocessing:
probability_images_left.append(
ants.apply_transforms(
fixed=t1,
moving=decropped_image,
transformlist=t1_preprocessing['template_transforms']
['invtransforms'],
whichtoinvert=[True],
interpolator="linear",
verbose=verbose))
else:
probability_images_left.append(decropped_image)
################################
#
# Right: download spatial priors
#
################################
spatial_priors_right_file_name_path = get_antsxnet_data(
"priorDeepFlashRightLabels",
antsxnet_cache_directory=antsxnet_cache_directory)
spatial_priors_right = ants.image_read(
spatial_priors_right_file_name_path)
priors_image_right_list = ants.ndimage_to_list(spatial_priors_right)
################################
#
# Right: build model and load weights
#
################################
template_size = (64, 96, 96)
labels_right = (0, 6, 8, 10, 12, 14, 16, 18)
channel_size = 1 + len(labels_right)
number_of_filters = 16
network_name = ''
if which_hemisphere_models == "old":
network_name = "deepFlashRight16"
elif which_hemisphere_models == "new":
network_name = "deepFlashRight16new"
else:
raise ValueError("network_name must be \"old\" or \"new\".")
unet_model = create_unet_model_3d(
(*template_size, channel_size),
number_of_outputs=len(labels_right),
number_of_layers=4,
number_of_filters_at_base_layer=number_of_filters,
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(
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).")
cropped_image = ants.crop_indices(t1_preprocessed, (88, 51, 0),
(152, 147, 96))
image_array = cropped_image.numpy()
image_array = (image_array - image_array.mean()) / image_array.std()
batchX = np.zeros((1, *template_size, channel_size))
batchX[0, :, :, :, 0] = image_array
for i in range(len(priors_image_right_list)):
cropped_prior = ants.crop_indices(priors_image_right_list[i],
(88, 51, 0), (152, 147, 96))
batchX[0, :, :, :, i + 1] = cropped_prior.numpy()
predicted_data = unet_model.predict(batchX, verbose=verbose)
origin = cropped_image.origin
spacing = cropped_image.spacing
direction = cropped_image.direction
probability_images_right = list()
for i in range(len(labels_right)):
probability_image = \
ants.from_numpy(np.squeeze(predicted_data[0, :, :, :, i]),
origin=origin, spacing=spacing, direction=direction)
if i > 0:
decropped_image = ants.decrop_image(probability_image,
t1_preprocessed * 0)
else:
decropped_image = ants.decrop_image(probability_image,
t1_preprocessed * 0 + 1)
if do_preprocessing:
probability_images_right.append(
ants.apply_transforms(
fixed=t1,
moving=decropped_image,
transformlist=t1_preprocessing['template_transforms']
['invtransforms'],
whichtoinvert=[True],
interpolator="linear",
verbose=verbose))
else:
probability_images_right.append(decropped_image)
################################
#
# 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]
return_dict = {
'segmentation_image': relabeled_image,
'probability_images': probability_images
}
return (return_dict)
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)