def test_label_clusters_example(self):
image = ants.image_read(ants.get_ants_data('r16'))
timageFully = ants.label_clusters(image, 10, 128, 150, True)
timageFace = ants.label_clusters(image, 10, 128, 150, False)
def test_get_centroids_example(self):
image = ants.image_read(ants.get_ants_data("r16"))
image = ants.threshold_image(image, 90, 120)
image = ants.label_clusters(image, 10)
cents = ants.get_centroids(image)
# Create initial segmentation image
print(" Initial step 6: prediction.")
start_time = time.time()
data_initial_stage = image_resampled.numpy()
data_initial_stage = np.expand_dims(data_initial_stage, 0)
data_initial_stage = np.expand_dims(data_initial_stage, -1)
prediction_initial_stage = np.squeeze(
model_initial_stage.predict(data_initial_stage))
prediction_initial_stage[np.where(prediction_initial_stage >= 0.5)] = 1
prediction_initial_stage[np.where(prediction_initial_stage < 0.5)] = 0
mask_initial_stage = ants.from_numpy(prediction_initial_stage,
origin=image_resampled.origin,
spacing=image_resampled.spacing,
direction=image_resampled.direction)
mask_initial_stage = ants.label_clusters(mask_initial_stage,
min_cluster_size=10)
mask_initial_stage = ants.threshold_image(mask_initial_stage, 1, 2, 1, 0)
mask_initial_stage_original_space = ants.resample_image(
mask_initial_stage, image_n4.shape, True, 1)
end_time = time.time()
elapsed_time = end_time - start_time
print(" (elapsed time: ", elapsed_time, " seconds)")
#########################################
#
# Perform initial (stage 2) segmentation
#
print("")
print("")
print("************* Refine stage segmentation ***************")
image_n4 = ants.image_math(image_n4, 'Normalize') * 255.0
print("Preprocessing: thresholding")
image_n4_array = ((image_n4.numpy()).flatten())
image_n4_nonzero = image_n4_array[(image_n4_array > 0).nonzero()]
image_robust_range = np.quantile(image_n4_nonzero, (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_n4, -10000, threshold_value, 0,
1)
thresholded_image = image_n4 * thresholded_mask
print("Preprocessing: resampling")
image_resampled = ants.resample_image(thresholded_image, (256, 256, 256), True)
batchX = np.expand_dims(image_resampled.numpy(), axis=0)
batchX = np.expand_dims(batchX, axis=-1)
print("Prediction and write to disk.")
brain_mask_array = model.predict(batchX, verbose=0)
brain_mask_resampled = ants.from_numpy(np.squeeze(brain_mask_array[0, :, :, :,
0]),
origin=image_resampled.origin,
spacing=image_resampled.spacing,
direction=image_resampled.direction)
brain_mask_image = ants.resample_image(brain_mask_resampled, image.shape, True,
1)
minimum_brain_volume = round(649933.7)
brain_mask_labeled = ants.label_clusters(brain_mask_image,
minimum_brain_volume)
ants.image_write(brain_mask_labeled, "brain_mask.nii.gz")
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)
