def test_matrix_to_images(self):
# def matrix_to_images(data_matrix, mask):
for img in self.imgs:
imgmask = ants.image_clone( img > img.mean(), pixeltype = 'float' )
data = img[imgmask]
dataflat = data.reshape(1,-1)
mat = np.vstack([dataflat,dataflat]).astype('float32')
imglist = ants.matrix_to_images(mat, imgmask)
nptest.assert_allclose((img*imgmask).numpy(), imglist[0].numpy())
nptest.assert_allclose((img*imgmask).numpy(), imglist[1].numpy())
self.assertTrue(ants.image_physical_space_consistency(img,imglist[0]))
self.assertTrue(ants.image_physical_space_consistency(img,imglist[1]))
# go back to matrix
mat2 = ants.images_to_matrix(imglist, imgmask)
nptest.assert_allclose(mat, mat2)
# test with matrix.ndim > 2
img = img.clone()
img.set_direction(img.direction*2)
imgmask = ants.image_clone( img > img.mean(), pixeltype = 'float' )
arr = (img*imgmask).numpy()
arr = arr[arr>=0.5]
arr2 = arr.copy()
mat = np.stack([arr,arr2])
imglist = ants.matrix_to_images(mat, imgmask)
for im in imglist:
self.assertTrue(ants.allclose(im, imgmask*img))
self.assertTrue(ants.image_physical_space_consistency(im, imgmask))
def test_images_to_matrix(self):
# def images_to_matrix(image_list, mask=None, sigma=None, epsilon=0):
for img in self.imgs:
mask = ants.image_clone( img > img.mean(), pixeltype = 'float' )
imglist = [img.clone(),img.clone(),img.clone()]
imgmat = ants.images_to_matrix(imglist, mask=mask)
self.assertTrue(imgmat.shape[0] == len(imglist))
self.assertTrue(imgmat.shape[1] == (mask>0).sum())
# go back to images
imglist2 = ants.matrix_to_images(imgmat, mask)
for i1,i2 in zip(imglist,imglist2):
self.assertTrue(ants.image_physical_space_consistency(i1,i2))
nptest.assert_allclose(i1.numpy()*mask.numpy(),i2.numpy())
if img.dimension == 2:
# with sigma
mask = ants.image_clone( img > img.mean(), pixeltype = 'float' )
imglist = [img.clone(),img.clone(),img.clone()]
imgmat = ants.images_to_matrix(imglist, mask=mask, sigma=2.)
# with no mask
mask = ants.image_clone( img > img.mean(), pixeltype = 'float' )
imglist = [img.clone(),img.clone(),img.clone()]
imgmat = ants.images_to_matrix(imglist)
# with mask of different shape
s = [65]*img.dimension
mask2 = ants.from_numpy(np.random.randn(*s))
mask2 = mask2 > mask2.mean()
imgmat = ants.images_to_matrix(imglist, mask=mask2)
def cortical_thickness(t1,
antsxnet_cache_directory=None,
verbose=False):
"""
Perform KellyKapowski cortical thickness using deep_atropos for
segmentation. Description concerning implementaiton and evaluation:
https://www.medrxiv.org/content/10.1101/2020.10.19.20215392v1
Arguments
---------
t1 : ANTsImage
input 3-D unprocessed T1-weighted brain image.
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
-------
Cortical thickness image and segmentation probability images.
Example
-------
>>> image = ants.image_read("t1w_image.nii.gz")
>>> kk = cortical_thickness(image)
"""
from ..utilities import deep_atropos
if t1.dimension != 3:
raise ValueError("Image dimension must be 3.")
atropos = deep_atropos(t1, do_preprocessing=True,
antsxnet_cache_directory=antsxnet_cache_directory, verbose=True)
# Kelly Kapowski cortical thickness
kk_segmentation = ants.image_clone(atropos['segmentation_image'])
kk_segmentation[kk_segmentation == 4] = 3
gray_matter = atropos['probability_images'][2]
white_matter = (atropos['probability_images'][3] + atropos['probability_images'][4])
kk = ants.kelly_kapowski(s=kk_segmentation, g=gray_matter, w=white_matter,
its=45, r=0.025, m=1.5, x=0, verbose=int(verbose))
return_dict = {'thickness_image' : kk,
'segmentation_image' : atropos['segmentation_image'],
'csf_probability_image' : atropos['probability_images'][1],
'gray_matter_probability_image' : atropos['probability_images'][2],
'white_matter_probability_image' : atropos['probability_images'][3],
'deep_gray_matter_probability_image' : atropos['probability_images'][4],
'brain_stem_probability_image' : atropos['probability_images'][5],
'cerebellum_probability_image' : atropos['probability_images'][6]
}
return(return_dict)
def test_n4_bias_field_correction(self):
#def n4_bias_field_correction(image, mask=None, shrink_factor=4,
# convergence={'iters':[50,50,50,50], 'tol':1e-07},
# spline_param=200, verbose=False, weight_mask=None):
for img in self.imgs:
image_n4 = ants.n4_bias_field_correction(img)
mask = ants.image_clone(img > img.mean(), pixeltype='float')
image_n4 = ants.n4_bias_field_correction(img, mask=mask)
def mi_img(y_true, y_pred, mask=None, metric_type="MattesMutualInformation"):
"""Compute the mutual information (MI) between two images.
Parameters
----------
y_true : np.array
Image 1. Either (h, w) of (N, h, w). If (N, h, w), the decorator `multiple_images_decorator` takes care of
the sample dimension.
y_pred : np.array
Image 2. Either (h, w) of (N, h, w). If (N, h, w), the decorator `multiple_images_decorator` takes care of
the sample dimension.
mask: np.array, optional
Optional, can be specified to have the computation carried out on a precise area.
metric_type: str, {'MattesMutualInformation', 'JointHistogramMutualInformation'}
Type of mutual information computation.
Returns
-------
mi : float
The mutual information (MI) metric. Similarity metric, the higher the more similar the images are.
"""
y_true_ants = ants.image_clone(ants.from_numpy(y_true), pixeltype="float")
y_pred_ants = ants.image_clone(ants.from_numpy(y_pred), pixeltype="float")
if mask is None:
mi = ants.image_similarity(y_true_ants,
y_pred_ants,
metric_type=metric_type)
else:
mask_ants = ants.image_clone(ants.from_numpy(mask.astype(float)),
pixeltype="float")
mi = ants.image_similarity(
y_true_ants,
y_pred_ants,
fixed_mask=mask_ants,
moving_mask=mask_ants,
metric_type=metric_type,
)
return -mi
def cross_correlation_img(y_true, y_pred, mask=None):
"""Compute the cross correlation metric between two images.
Parameters
----------
y_true : np.array
Image 1. Either (h, w) of (N, h, w). If (N, h, w), the decorator `multiple_images_decorator` takes care of
the sample dimension.
y_pred : np.array
Image 2. Either (h, w) of (N, h, w). If (N, h, w), the decorator `multiple_images_decorator` takes care of
the sample dimension.
mask: np.array, optional
Optional, can be specified to have the computation carried out on a precise area.
Returns
-------
cc : float
The Cross-Correlation value. Similarity metric, the higher the more similar the images are.
"""
y_true_ants = ants.image_clone(ants.from_numpy(y_true), pixeltype="float")
y_pred_ants = ants.image_clone(ants.from_numpy(y_pred), pixeltype="float")
if mask is None:
cc = ants.image_similarity(y_true_ants,
y_pred_ants,
metric_type="Correlation")
else:
mask_ants = ants.image_clone(ants.from_numpy(mask.astype(float)),
pixeltype="float")
cc = ants.image_similarity(
y_true_ants,
y_pred_ants,
fixed_mask=mask_ants,
moving_mask=mask_ants,
metric_type="Correlation",
)
return -cc
def test_image_clone(self):
for img in self.imgs:
img = ants.image_clone(img, 'unsigned char')
orig_ptype = img.pixeltype
for ptype in self.pixeltypes:
imgcloned = ants.image_clone(img, ptype)
self.assertTrue(ants.image_physical_space_consistency(img,imgcloned))
nptest.assert_allclose(img.numpy(), imgcloned.numpy())
self.assertEqual(imgcloned.pixeltype, ptype)
self.assertEqual(img.pixeltype, orig_ptype)
for img in self.vecimgs:
img = img.clone('unsigned char')
orig_ptype = img.pixeltype
for ptype in self.pixeltypes:
imgcloned = ants.image_clone(img, ptype)
self.assertTrue(ants.image_physical_space_consistency(img,imgcloned))
self.assertEqual(imgcloned.components, img.components)
nptest.assert_allclose(img.numpy(), imgcloned.numpy())
self.assertEqual(imgcloned.pixeltype, ptype)
self.assertEqual(img.pixeltype, orig_ptype)
def test_make_image(self):
self.setUp()
for arr in self.arrs:
voxval = 6.
img = ants.make_image(arr.shape, voxval=voxval)
self.assertTrue(img.dimension, arr.ndim)
self.assertTrue(img.shape, arr.shape)
nptest.assert_allclose(img.mean(), voxval)
new_origin = tuple([6.9] * arr.ndim)
new_spacing = tuple([3.6] * arr.ndim)
new_direction = np.eye(arr.ndim) * 9.6
img2 = ants.make_image(arr.shape,
voxval=voxval,
origin=new_origin,
spacing=new_spacing,
direction=new_direction)
self.assertTrue(img2.dimension, arr.ndim)
self.assertTrue(img2.shape, arr.shape)
nptest.assert_allclose(img2.mean(), voxval)
self.assertEqual(img2.origin, new_origin)
self.assertEqual(img2.spacing, new_spacing)
nptest.assert_allclose(img2.direction, new_direction)
for ptype in self.pixeltypes:
img = ants.make_image(arr.shape, voxval=1., pixeltype=ptype)
self.assertEqual(img.pixeltype, ptype)
# test with components
img = ants.make_image((69, 70, 4), has_components=True)
self.assertEqual(img.components, 4)
self.assertEqual(img.dimension, 2)
nptest.assert_allclose(img.mean(), 0.)
img = ants.make_image((69, 70, 71, 4), has_components=True)
self.assertEqual(img.components, 4)
self.assertEqual(img.dimension, 3)
nptest.assert_allclose(img.mean(), 0.)
# set from image
for img in self.imgs:
mask = ants.image_clone(img > img.mean(), pixeltype='float')
arr = img[mask]
img2 = ants.make_image(mask, voxval=arr)
nptest.assert_allclose(img2.numpy(), (img * mask).numpy())
self.assertTrue(ants.image_physical_space_consistency(img2, mask))
# set with arr.ndim > 1
img2 = ants.make_image(mask, voxval=np.expand_dims(arr, -1))
nptest.assert_allclose(img2.numpy(), (img * mask).numpy())
self.assertTrue(ants.image_physical_space_consistency(img2, mask))
def test_n4_bias_field_correction_example(self):
image = ants.image_read(ants.get_ants_data('r16'))
image_n4 = ants.n4_bias_field_correction(image)
# spline param list
image_n4 = ants.n4_bias_field_correction(image, spline_param=(10, 10))
# image not float
image_ui = image.clone('unsigned int')
image_n4 = ants.n4_bias_field_correction(image_ui)
# weight mask
mask = ants.image_clone(image > image.mean(), pixeltype='float')
ants.n4_bias_field_correction(image, weight_mask=mask)
# len(spline_param) != img.dimension
with self.assertRaises(Exception):
ants.n4_bias_field_correction(image, spline_param=(10, 10, 10))
# weight mask not ANTsImage
with self.assertRaises(Exception):
ants.n4_bias_field_correction(image, weight_mask=0.4)
def create_ROIs(path: str, extension: str = '.nii.gz'):
"""
This ROI generator follows the structure FILE/FILE+extension
and generates a ROI file with the name FILE/FILE+_ROI+extension
Parameters
----------
path Images Path
Returns Generated ROI images
-------
"""
for scan_id in os.listdir(path):
print('Creating ROI for: ', scan_id)
scan = ants.image_read(os.path.join(path, scan_id,
scan_id + extension))
brainmask = ants.image_clone(scan).apply(mask_image)
brainmask.to_filename(
os.path.join(path, scan_id, scan_id + '_ROI' + extension))
return
def crop_image_center(image, crop_size):
"""
Crop the center of an image.
Arguments
---------
image : ANTsImage
Input image
crop_size: n-D tuple (depending on dimensionality).
Width, height, depth (if 3-D), and time (if 4-D) of crop region.
Returns
-------
A list (or array) of patches.
Example
-------
>>> import ants
>>> image = ants.image_read(ants.get_ants_data('r16'))
>>> cropped_image = crop_image_center(image, crop_size=(64, 64))
"""
image_size = np.array(image.shape)
if len(image_size) != len(crop_size):
raise ValueError("crop_size does not match image size.")
if (np.asarray(crop_size) > np.asarray(image_size)).any():
raise ValueError("A crop_size dimension is larger than image_size.")
start_index = (np.floor(
0.5 * (np.asarray(image_size) - np.asarray(crop_size)))).astype(int)
end_index = start_index + np.asarray(crop_size).astype(int)
cropped_image = ants.crop_indices(
ants.image_clone(image) * 1, start_index, end_index)
return (cropped_image)
def preprocess_brain_image(image,
truncate_intensity=(0.01, 0.99),
brain_extraction_modality=None,
template_transform_type=None,
template="biobank",
do_bias_correction=True,
return_bias_field=False,
do_denoising=True,
intensity_matching_type=None,
reference_image=None,
intensity_normalization_type=None,
antsxnet_cache_directory=None,
verbose=True):
"""
Basic preprocessing pipeline for T1-weighted brain MRI
Standard preprocessing steps that have been previously described
in various papers including the cortical thickness pipeline:
https://www.ncbi.nlm.nih.gov/pubmed/24879923
Arguments
---------
image : ANTsImage
input image
truncate_intensity : 2-length tuple
Defines the quantile threshold for truncating the image intensity
brain_extraction_modality : string or None
Perform brain extraction using antspynet tools. One of "t1", "t1v0",
"t1nobrainer", "t1combined", "flair", "t2", "bold", "fa", "t1infant",
"t2infant", or None.
template_transform_type : string
See details in help for ants.registration. Typically "Rigid" or
"Affine".
template : ANTs image (not skull-stripped)
Alternatively, one can specify the default "biobank" or "croppedMni152"
to download and use premade templates.
do_bias_correction : boolean
Perform N4 bias field correction.
return_bias_field : boolean
If True, return bias field as an additional output *without* bias
correcting the preprocessed image.
do_denoising : boolean
Perform non-local means denoising.
intensity_matching_type : string
Either "regression" or "histogram". Only is performed if reference_image
is not None.
reference_image : ANTs image
Reference image for intensity matching.
intensity_normalization_type : string
Either rescale the intensities to [0,1] (i.e., "01") or zero-mean, unit variance
(i.e., "0mean"). If None normalization is not performed.
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 with preprocessing information ANTs image (i.e., source_image) matched to the
(reference_image).
Example
-------
>>> import ants
>>> image = ants.image_read(ants.get_ants_data('r16'))
>>> preprocessed_image = preprocess_brain_image(image, do_brain_extraction=False)
"""
from ..utilities import brain_extraction
from ..utilities import regression_match_image
from ..utilities import get_antsxnet_data
preprocessed_image = ants.image_clone(image)
if antsxnet_cache_directory == None:
antsxnet_cache_directory = "ANTsXNet"
# Truncate intensity
if truncate_intensity is not None:
quantiles = (image.quantile(truncate_intensity[0]), image.quantile(truncate_intensity[1]))
if verbose == True:
print("Preprocessing: truncate intensities ( low =", quantiles[0], ", high =", quantiles[1], ").")
preprocessed_image[image < quantiles[0]] = quantiles[0]
preprocessed_image[image > quantiles[1]] = quantiles[1]
# Brain extraction
mask = None
if brain_extraction_modality is not None:
if verbose == True:
print("Preprocessing: brain extraction.")
probability_mask = brain_extraction(preprocessed_image, modality=brain_extraction_modality,
antsxnet_cache_directory=antsxnet_cache_directory, verbose=verbose)
mask = ants.threshold_image(probability_mask, 0.5, 1, 1, 0)
mask = ants.morphology(mask,"close",6).iMath_fill_holes()
# Template normalization
transforms = None
if template_transform_type is not None:
template_image = None
if isinstance(template, str):
template_file_name_path = get_antsxnet_data(template, antsxnet_cache_directory=antsxnet_cache_directory)
template_image = ants.image_read(template_file_name_path)
else:
template_image = template
if mask is None:
registration = ants.registration(fixed=template_image, moving=preprocessed_image,
type_of_transform=template_transform_type, verbose=verbose)
preprocessed_image = registration['warpedmovout']
transforms = dict(fwdtransforms=registration['fwdtransforms'],
invtransforms=registration['invtransforms'])
else:
template_probability_mask = brain_extraction(template_image, modality=brain_extraction_modality,
antsxnet_cache_directory=antsxnet_cache_directory, verbose=verbose)
template_mask = ants.threshold_image(template_probability_mask, 0.5, 1, 1, 0)
template_brain_image = template_mask * template_image
preprocessed_brain_image = preprocessed_image * mask
registration = ants.registration(fixed=template_brain_image, moving=preprocessed_brain_image,
type_of_transform=template_transform_type, verbose=verbose)
transforms = dict(fwdtransforms=registration['fwdtransforms'],
invtransforms=registration['invtransforms'])
preprocessed_image = ants.apply_transforms(fixed = template_image, moving = preprocessed_image,
transformlist=registration['fwdtransforms'], interpolator="linear", verbose=verbose)
mask = ants.apply_transforms(fixed = template_image, moving = mask,
transformlist=registration['fwdtransforms'], interpolator="genericLabel", verbose=verbose)
# Do bias correction
bias_field = None
if do_bias_correction == True:
if verbose == True:
print("Preprocessing: brain correction.")
n4_output = None
if mask is None:
n4_output = ants.n4_bias_field_correction(preprocessed_image, shrink_factor=4, return_bias_field=return_bias_field, verbose=verbose)
else:
n4_output = ants.n4_bias_field_correction(preprocessed_image, mask, shrink_factor=4, return_bias_field=return_bias_field, verbose=verbose)
if return_bias_field == True:
bias_field = n4_output
else:
preprocessed_image = n4_output
# Denoising
if do_denoising == True:
if verbose == True:
print("Preprocessing: denoising.")
if mask is None:
preprocessed_image = ants.denoise_image(preprocessed_image, shrink_factor=1)
else:
preprocessed_image = ants.denoise_image(preprocessed_image, mask, shrink_factor=1)
# Image matching
if reference_image is not None and intensity_matching_type is not None:
if verbose == True:
print("Preprocessing: intensity matching.")
if intensity_matching_type == "regression":
preprocessed_image = regression_match_image(preprocessed_image, reference_image)
elif intensity_matching_type == "histogram":
preprocessed_image = ants.histogram_match_image(preprocessed_image, reference_image)
else:
raise ValueError("Unrecognized intensity_matching_type.")
# Intensity normalization
if intensity_normalization_type is not None:
if verbose == True:
print("Preprocessing: intensity normalization.")
if intensity_normalization_type == "01":
preprocessed_image = (preprocessed_image - preprocessed_image.min())/(preprocessed_image.max() - preprocessed_image.min())
elif intensity_normalization_type == "0mean":
preprocessed_image = (preprocessed_image - preprocessed_image.mean())/preprocessed_image.std()
else:
raise ValueError("Unrecognized intensity_normalization_type.")
return_dict = {'preprocessed_image' : preprocessed_image}
if mask is not None:
return_dict['brain_mask'] = mask
if bias_field is not None:
return_dict['bias_field'] = bias_field
if transforms is not None:
return_dict['template_transforms'] = transforms
return(return_dict)
def el_bicho(ventilation_image,
mask,
use_coarse_slices_only=True,
antsxnet_cache_directory=None,
verbose=False):
"""
Perform functional lung segmentation using hyperpolarized gases.
https://pubmed.ncbi.nlm.nih.gov/30195415/
Arguments
---------
ventilation_image : ANTsImage
input ventilation image.
mask : ANTsImage
input mask.
use_coarse_slices_only : boolean
If True, apply network only in the dimension of greatest slice thickness.
If False, apply to all dimensions and average the results.
antsxnet_cache_directory : string
Destination directory for storing the downloaded template and model weights.
Since these can be resused, if is None, these data will be downloaded to a
~/.keras/ANTsXNet/.
verbose : boolean
Print progress to the screen.
Returns
-------
Ventilation segmentation and corresponding probability images
Example
-------
>>> image = ants.image_read("ventilation.nii.gz")
>>> mask = ants.image_read("mask.nii.gz")
>>> lung_seg = el_bicho(image, mask, use_coarse_slices=True, verbose=False)
"""
from ..architectures import create_unet_model_2d
from ..utilities import get_pretrained_network
from ..utilities import pad_or_crop_image_to_size
if ventilation_image.dimension != 3:
raise ValueError("Image dimension must be 3.")
if ventilation_image.shape != mask.shape:
raise ValueError(
"Ventilation image and mask size are not the same size.")
if antsxnet_cache_directory == None:
antsxnet_cache_directory = "ANTsXNet"
################################
#
# Preprocess image
#
################################
template_size = (256, 256)
classes = (0, 1, 2, 3, 4)
number_of_classification_labels = len(classes)
image_modalities = ("Ventilation", "Mask")
channel_size = len(image_modalities)
preprocessed_image = (ventilation_image -
ventilation_image.mean()) / ventilation_image.std()
ants.set_direction(preprocessed_image, np.identity(3))
mask_identity = ants.image_clone(mask)
ants.set_direction(mask_identity, np.identity(3))
################################
#
# Build models and load weights
#
################################
unet_model = create_unet_model_2d(
(*template_size, channel_size),
number_of_outputs=number_of_classification_labels,
number_of_layers=4,
number_of_filters_at_base_layer=32,
dropout_rate=0.0,
convolution_kernel_size=(3, 3),
deconvolution_kernel_size=(2, 2),
weight_decay=1e-5,
additional_options=("attentionGating"))
if verbose == True:
print("El Bicho: retrieving model weights.")
weights_file_name = get_pretrained_network(
"elBicho", antsxnet_cache_directory=antsxnet_cache_directory)
unet_model.load_weights(weights_file_name)
################################
#
# Extract slices
#
################################
spacing = ants.get_spacing(preprocessed_image)
dimensions_to_predict = (spacing.index(max(spacing)), )
if use_coarse_slices_only == False:
dimensions_to_predict = list(range(3))
total_number_of_slices = 0
for d in range(len(dimensions_to_predict)):
total_number_of_slices += preprocessed_image.shape[
dimensions_to_predict[d]]
batchX = np.zeros((total_number_of_slices, *template_size, channel_size))
slice_count = 0
for d in range(len(dimensions_to_predict)):
number_of_slices = preprocessed_image.shape[dimensions_to_predict[d]]
if verbose == True:
print("Extracting slices for dimension ", dimensions_to_predict[d],
".")
for i in range(number_of_slices):
ventilation_slice = pad_or_crop_image_to_size(
ants.slice_image(preprocessed_image, dimensions_to_predict[d],
i), template_size)
batchX[slice_count, :, :, 0] = ventilation_slice.numpy()
mask_slice = pad_or_crop_image_to_size(
ants.slice_image(mask_identity, dimensions_to_predict[d], i),
template_size)
batchX[slice_count, :, :, 1] = mask_slice.numpy()
slice_count += 1
################################
#
# Do prediction and then restack into the image
#
################################
if verbose == True:
print("Prediction.")
prediction = unet_model.predict(batchX, verbose=verbose)
permutations = list()
permutations.append((0, 1, 2))
permutations.append((1, 0, 2))
permutations.append((1, 2, 0))
probability_images = list()
for l in range(number_of_classification_labels):
probability_images.append(ants.image_clone(mask) * 0)
current_start_slice = 0
for d in range(len(dimensions_to_predict)):
current_end_slice = current_start_slice + preprocessed_image.shape[
dimensions_to_predict[d]] - 1
which_batch_slices = range(current_start_slice, current_end_slice)
for l in range(number_of_classification_labels):
prediction_per_dimension = prediction[which_batch_slices, :, :, l]
prediction_array = np.transpose(
np.squeeze(prediction_per_dimension),
permutations[dimensions_to_predict[d]])
prediction_image = ants.copy_image_info(
ventilation_image,
pad_or_crop_image_to_size(ants.from_numpy(prediction_array),
ventilation_image.shape))
probability_images[l] = probability_images[l] + (
prediction_image - probability_images[l]) / (d + 1)
current_start_slice = current_end_slice + 1
################################
#
# Convert probability images to segmentation
#
################################
image_matrix = ants.image_list_to_matrix(
probability_images[1:(len(probability_images))], mask * 0 + 1)
background_foreground_matrix = np.stack([
ants.image_list_to_matrix([probability_images[0]], mask * 0 + 1),
np.expand_dims(np.sum(image_matrix, axis=0), axis=0)
])
foreground_matrix = np.argmax(background_foreground_matrix, axis=0)
segmentation_matrix = (np.argmax(image_matrix, axis=0) +
1) * foreground_matrix
segmentation_image = ants.matrix_to_images(
np.expand_dims(segmentation_matrix, axis=0), mask * 0 + 1)[0]
return_dict = {
'segmentation_image': segmentation_image,
'probability_images': probability_images
}
return (return_dict)
def arterial_lesion_segmentation(image,
antsxnet_cache_directory=None,
verbose=False):
"""
Perform arterial lesion segmentation using U-net.
Arguments
---------
image : ANTsImage
input image
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
-------
Foreground probability image.
Example
-------
>>> output = arterial_lesion_segmentation(histology_image)
"""
from ..architectures import create_unet_model_2d
from ..utilities import get_pretrained_network
if image.dimension != 2:
raise ValueError("Image dimension must be 2.")
if antsxnet_cache_directory == None:
antsxnet_cache_directory = "ANTsXNet"
channel_size = 1
weights_file_name = get_pretrained_network(
"arterialLesionWeibinShi",
antsxnet_cache_directory=antsxnet_cache_directory)
resampled_image_size = (512, 512)
unet_model = create_unet_model_2d(
(*resampled_image_size, channel_size),
number_of_outputs=1,
mode="sigmoid",
number_of_filters=(64, 96, 128, 256, 512),
convolution_kernel_size=(3, 3),
deconvolution_kernel_size=(2, 2),
dropout_rate=0.0,
weight_decay=0,
additional_options=("initialConvolutionKernelSize[5]",
"attentionGating"))
unet_model.load_weights(weights_file_name)
if verbose == True:
print("Preprocessing: Resampling and N4 bias correction.")
preprocessed_image = ants.image_clone(image)
preprocessed_image = preprocessed_image / preprocessed_image.max()
preprocessed_image = ants.resample_image(preprocessed_image,
resampled_image_size,
use_voxels=True,
interp_type=0)
mask = ants.image_clone(preprocessed_image) * 0 + 1
preprocessed_image = ants.n4_bias_field_correction(preprocessed_image,
mask=mask,
shrink_factor=2,
return_bias_field=False,
verbose=verbose)
batchX = np.expand_dims(preprocessed_image.numpy(), axis=0)
batchX = np.expand_dims(batchX, axis=-1)
batchX = (batchX - batchX.min()) / (batchX.max() - batchX.min())
predicted_data = unet_model.predict(batchX, verbose=int(verbose))
origin = preprocessed_image.origin
spacing = preprocessed_image.spacing
direction = preprocessed_image.direction
foreground_probability_image = ants.from_numpy(np.squeeze(
predicted_data[0, :, :, 0]),
origin=origin,
spacing=spacing,
direction=direction)
if verbose == True:
print("Post-processing: resampling to original space.")
foreground_probability_image = ants.resample_image_to_target(
foreground_probability_image, image)
return (foreground_probability_image)
def antspy_registration(
fixed_img,
moving_img,
registration_type="SyN",
reg_iterations=(40, 20, 0),
aff_metric="mattes",
syn_metric="mattes",
verbose=False,
initial_transform=None,
path=GLOBAL_CACHE_FOLDER,
):
"""Register images using ANTsPY.
Parameters
----------
fixed_img: np.ndarray
Fixed image.
moving_img: np.ndarray
Moving image to register.
registration_type: {'Translation', 'Rigid', 'Similarity', 'QuickRigid', 'DenseRigid', 'BOLDRigid', 'Affine',
'AffineFast', 'BOLDAffine', 'TRSAA', 'ElasticSyN', 'SyN', 'SyNRA', 'SyNOnly', 'SyNCC', 'SyNabp',
'SyNBold', 'SyNBoldAff', 'SyNAggro', 'TVMSQ', 'TVMSQC'}, default 'SyN'
Optimization algorithm to use to register (more info: https://antspy.readthedocs.io/en/latest/registration.
html?highlight=registration#ants.registration)
reg_iterations: tuple, default (40, 20, 0)
Vector of iterations for SyN.
aff_metric: {'GC', 'mattes', 'meansquares'}, default 'mattes'
The metric for the affine part.
syn_metric: {'CC', 'mattes', 'meansquares', 'demons'}, default 'mattes'
The metric for the SyN part.
verbose : bool, default False
If True, then the inner solver prints convergence related information in standard output.
path : str
Path to a folder to where to save the `.nii.gz` file representing the composite transform.
initial_transform : list or None
Transforms to prepend the before the registration.
Returns
-------
df: DisplacementField
Displacement field between the moving and the fixed image
meta : dict
Contains relevant images and paths.
"""
path = str(path)
path += "" if path[-1] == "/" else "/"
fixed_ants_image = ants.image_clone(ants.from_numpy(fixed_img),
pixeltype="float")
moving_ants_image = ants.image_clone(ants.from_numpy(moving_img),
pixeltype="float")
meta = ants.registration(
fixed_ants_image,
moving_ants_image,
registration_type,
reg_iterations=reg_iterations,
aff_metric=aff_metric,
syn_metric=syn_metric,
verbose=verbose,
initial_transform=initial_transform,
syn_sampling=32,
aff_sampling=32,
)
filename = ants.apply_transforms(
fixed_ants_image,
moving_ants_image,
meta["fwdtransforms"],
compose=path + "final_transform",
)
df = nib.load(filename)
data = df.get_fdata()
data = data.squeeze()
dx = data[:, :, 1]
dy = data[:, :, 0]
df_final = DisplacementField(dx, dy)
return df_final, meta
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 deep_flash(t1,
do_preprocessing=True,
output_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.
output_directory : string
Destination directory for storing the downloaded template and model weights.
Since these can be resused, if is None, these data will be downloaded to a
tempfile.
verbose : boolean
Print progress to the screen.
Returns
-------
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 categorical_focal_loss
from ..utilities import preprocess_brain_image
from ..utilities import crop_image_center
from ..utilities import pad_or_crop_image_to_size
if t1.dimension != 3:
raise ValueError("Image dimension must be 3.")
################################
#
# 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,
output_directory=output_directory,
verbose=verbose)
t1_preprocessed = t1_preprocessing[
"preprocessed_image"] * t1_preprocessing['brain_mask']
################################
#
# Build model and load weights
#
################################
template_size = (160, 192, 160)
labels = (0, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18)
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 = None
if output_directory is not None:
weights_file_name = output_directory + "/deepFlashWeights.h5"
if not os.path.exists(weights_file_name):
if verbose == True:
print("Deep Flash: downloading model weights.")
weights_file_name = get_pretrained_network("deepFlashWeights",
weights_file_name)
else:
weights_file_name = get_pretrained_network("deepFlash")
unet_model.load_weights(weights_file_name)
################################
#
# Do prediction and normalize to native space
#
################################
if verbose == True:
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
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:
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)
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]
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_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 histogram_warp_image_intensities(image,
break_points=(0.25, 0.5, 0.75),
displacements=None,
clamp_end_points=(False, False),
sd_displacements=0.05,
transform_domain_size=20):
"""
Transform image intensities based on histogram mapping.
Apply B-spline 1-D maps to an input image for intensity warping.
Arguments
---------
image : ANTsImage
Input image.
break_points : integer or tuple
Parametric points at which the intensity transform displacements
are specified between [0, 1]. Alternatively, a single number can
be given and the sequence is linearly spaced in [0, 1].
displacements : tuple
displacements to define intensity warping. Length must be equal to the
breakPoints. Alternatively, if None random displacements are chosen
(random normal: mean = 0, sd = sd_displacements).
sd_displacements : float
Characterize the randomness of the intensity displacement.
clamp_end_points : 2-element tuple of booleans
Specify non-zero intensity change at the ends of the histogram.
transform_domain_size : integer
Defines the sampling resolution of the B-spline warping.
Returns
-------
ANTs image
Example
-------
>>> import ants
>>> image = ants.image_read(ants.get_ants_data("r64"))
>>> transformed_image = histogram_warp_image_intensities( image )
"""
if not len(clamp_end_points) == 2:
raise ValueError(
"clamp_end_points must be a boolean tuple of length 2.")
if not isinstance(break_points, int):
if any(b < 0 for b in break_points) and any(b > 1
for b in break_points):
raise ValueError(
"If specifying break_points as a vector, values must be in the range [0, 1]"
)
parametric_points = None
number_of_nonzero_displacements = 1
if not isinstance(break_points, int):
parametric_points = break_points
number_of_nonzero_displacements = len(break_points)
if clamp_end_points[0] is True:
parametric_points = (0, *parametric_points)
if clamp_end_points[1] is True:
parametric_points = (*parametric_points, 1)
else:
total_number_of_break_points = break_points
if clamp_end_points[0] is True:
total_number_of_break_points += 1
if clamp_end_points[1] is True:
total_number_of_break_points += 1
parametric_points = np.linspace(0, 1, total_number_of_break_points)
number_of_nonzero_displacements = break_points
if displacements is None:
displacements = np.random.normal(loc=0.0,
scale=sd_displacements,
size=number_of_nonzero_displacements)
weights = np.ones(len(displacements))
if clamp_end_points[0] is True:
displacements = (0, *displacements)
weights = np.concatenate((1000 * np.ones(1), weights))
if clamp_end_points[1] is True:
displacements = (*displacements, 0)
weights = np.concatenate((weights, 1000 * np.ones(1)))
if not len(displacements) == len(parametric_points):
raise ValueError(
"Length of displacements does not match the length of the break points."
)
scattered_data = np.reshape(displacements, (len(displacements), 1))
parametric_data = np.reshape(parametric_points,
(len(parametric_points), 1))
transform_domain_origin = 0
transform_domain_spacing = (1.0 - transform_domain_origin) / (
transform_domain_size - 1)
bspline_histogram_transform = ants.fit_bspline_object_to_scattered_data(
scattered_data,
parametric_data, [transform_domain_origin], [transform_domain_spacing],
[transform_domain_size],
data_weights=weights)
transform_domain = np.linspace(0, 1, transform_domain_size)
normalized_image = ants.iMath(ants.image_clone(image), "Normalize")
transformed_array = normalized_image.numpy()
normalized_array = normalized_image.numpy()
for i in range(len(transform_domain) - 1):
indices = np.where((normalized_array >= transform_domain[i])
& (normalized_array < transform_domain[i + 1]))
intensities = normalized_array[indices]
alpha = (intensities - transform_domain[i]) / (
transform_domain[i + 1] - transform_domain[i])
xfrm = alpha * (
bspline_histogram_transform[i + 1] -
bspline_histogram_transform[i]) + bspline_histogram_transform[i]
transformed_array[indices] = intensities + xfrm
transformed_image = (ants.from_numpy(transformed_array,
origin=image.origin,
spacing=image.spacing,
direction=image.direction) *
(image.max() - image.min())) + image.min()
return (transformed_image)
arrC = imagCine.view()
arrC = arrC[23,:,:,:]
ED_img = ants.from_numpy(arrC,origin=imagCine.origin,spacing = imagCine.spacing,direction = imagCine.direction)
# ED_img = ants.resample_image(ED_img,[1.1429,1.1429,1.1429])
# ED_Re = ants.to_nibabel(ED_img)
# ED_Re.set_data_dtype('uint16')
# nib.save(ED_Re,'ED/ED_resampled.nii')
ED_imgCH = ED_img.view()
#Registration
regED_vl = ants.registration(ED_img,images_vLong,type_of_transform = "Rigid",aff_metric = 'mattes')
regED_hl = ants.registration(ED_img,images_hLong,type_of_transform = "Rigid",aff_metric = 'mattes')
cloneVL = ants.image_clone(images_vLong)
cloneHL = ants.image_clone(images_hLong)
roi_vl = overROI(cloneVL,regED_vl['fwdtransforms'],ED_img)
roi_hl = overROI(cloneHL,regED_hl['fwdtransforms'],ED_img)
roi_hlArr = roi_hl.view()
roi_hlArr = roi_hlArr == 1
roi_vlArr = roi_vl.view()
roi_vlArr = roi_vlArr == 1
print('ROI extraction Done')
newvl = ants.to_nibabel(regED_vl['warpedmovout'])
newvl.set_data_dtype('int16')
newhl = ants.to_nibabel(regED_hl['warpedmovout'])
newhl.set_data_dtype('int16')
nib.save(newvl,path+'/ED_1301.nii')
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 deep_atropos(t1,
do_preprocessing=True,
output_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.
output_directory : string
Destination directory for storing the downloaded template and model weights.
Since these can be resused, if is None, these data will be downloaded to a
tempfile.
verbose : boolean
Print progress to the screen.
Returns
-------
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 categorical_focal_loss
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.")
################################
#
# 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,
output_directory=output_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")
labels = (0, 1, 2, 3, 4, 5, 6)
unet_model = create_unet_model_3d((*patch_size, 1),
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
if output_directory is not None:
weights_file_name = output_directory + "/sixTissueOctantSegmentationWeights.h5"
if not os.path.exists(weights_file_name):
if verbose == True:
print("Deep Atropos: downloading model weights.")
weights_file_name = get_pretrained_network(
"sixTissueBrainSegmentation", weights_file_name)
else:
weights_file_name = get_pretrained_network(
"sixTissueOctantBrainSegmentation")
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.expand_dims(image_patches, axis=-1)
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)
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]
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 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 longitudinal_cortical_thickness(t1s,
initial_template="oasis",
number_of_iterations=1,
refinement_transform="antsRegistrationSyNQuick[a]",
antsxnet_cache_directory=None,
verbose=False):
"""
Perform KellyKapowski cortical thickness longitudinally using \code{deepAtropos}
for segmentation of the derived single-subject template. It takes inspiration from
the work described here:
https://pubmed.ncbi.nlm.nih.gov/31356207/
Arguments
---------
t1s : list of ANTsImage
Input list of 3-D unprocessed t1-weighted brain images from a single subject.
initial_template : string or ANTsImage
Input image to define the orientation of the SST. Can be a string (see
get_antsxnet_data) or a specified template. This allows the user to create a
SST outside of this routine.
number_of_iterations : int
Defines the number of iterations for refining the SST.
refinement_transform : string
Transform for defining the refinement registration transform. See options in
ants.registration.
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
-------
Cortical thickness image and segmentation probability images.
Example
-------
>>> t1s = list()
>>> t1s.append(ants.image_read("t1w_image.nii.gz"))
>>> kk = longitudinal_cortical_thickness(image)
"""
from ..utilities import get_antsxnet_data
from ..utilities import preprocess_brain_image
from ..utilities import deep_atropos
###################
#
# Initial SST + optional affine refinement
#
##################
sst = None
if isinstance(initial_template, str):
template_file_name_path = get_antsxnet_data(initial_template, antsxnet_cache_directory=antsxnet_cache_directory)
sst = ants.image_read(template_file_name_path)
else:
sst = initial_template
for s in range(number_of_iterations):
if verbose:
print("Refinement iteration", s, "( out of", number_of_iterations, ")")
sst_tmp = ants.image_clone(sst) * 0
for i in range(len(t1s)):
if verbose:
print("***************************")
print( "SST processing image", i, "( out of", len(t1s), ")")
print( "***************************" )
transform_type = "antsRegistrationSyNQuick[r]"
if s > 0:
transform_type = refinement_transform
t1_preprocessed = preprocess_brain_image(t1s[i],
truncate_intensity=(0.01, 0.99),
do_brain_extraction=False,
template=sst,
template_transform_type=transform_type,
do_bias_correction=False,
do_denoising=False,
intensity_normalization_type="01",
antsxnet_cache_directory=antsxnet_cache_directory,
verbose=verbose)
sst_tmp += t1_preprocessed['preprocessed_image']
sst = sst_tmp / len(t1s)
###################
#
# Preprocessing and affine transform to final SST
#
##################
t1s_preprocessed = list()
for i in range(len(t1s)):
if verbose:
print("***************************")
print( "Final processing image", i, "( out of", len(t1s), ")")
print( "***************************" )
t1_preprocessed = preprocess_brain_image(t1s[i],
truncate_intensity=(0.01, 0.99),
do_brain_extraction=True,
template=sst,
template_transform_type="antsRegistrationSyNQuick[a]",
do_bias_correction=True,
do_denoising=True,
intensity_normalization_type="01",
antsxnet_cache_directory=antsxnet_cache_directory,
verbose=verbose)
t1s_preprocessed.append(t1_preprocessed)
###################
#
# Deep Atropos of SST for priors
#
##################
sst_atropos = deep_atropos(sst, do_preprocessing=True,
antsxnet_cache_directory=antsxnet_cache_directory, verbose=verbose)
###################
#
# Traditional Atropos + KK for each iamge
#
##################
return_list = list()
for i in range(len(t1s_preprocessed)):
if verbose:
print("Atropos for image", i, "( out of", len(t1s), ")")
atropos_output = ants.atropos(t1s_preprocessed[i]['preprocessed_image'],
x=t1s_preprocessed[i]['brain_mask'], i=sst_atropos['probability_images'][1:7],
m="[0.1,1x1x1]", c="[5,0]", priorweight=0.5, p="Socrates[1]", verbose=int(verbose))
kk_segmentation = ants.image_clone(atropos_output['segmentation'])
kk_segmentation[kk_segmentation == 4] = 3
gray_matter = atropos_output['probabilityimages'][1]
white_matter = atropos_output['probabilityimages'][2] + atropos_output['probabilityimages'][3]
kk = ants.kelly_kapowski(s=kk_segmentation, g=gray_matter, w=white_matter,
its=45, r=0.025, m=1.5, x=0, verbose=int(verbose))
t1_dict = {'preprocessed_image' : t1s_preprocessed[i]['preprocessed_image'],
'thickness_image' : kk,
'segmentation_image' : atropos_output['segmentation'],
'csf_probability_image' : atropos_output['probabilityimages'][0],
'gray_matter_probability_image' : atropos_output['probabilityimages'][1],
'white_matter_probability_image' : atropos_output['probabilityimages'][2],
'deep_gray_matter_probability_image' : atropos_output['probabilityimages'][3],
'brain_stem_probability_image' : atropos_output['probabilityimages'][4],
'cerebellum_probability_image' : atropos_output['probabilityimages'][5],
'template_transforms' : t1s_preprocessed[i]['template_transforms']
}
return_list.append(t1_dict)
return_list.append(sst)
return(return_list)
def mainRegScript(patientPath,SA_name,LA_4CH_name,LA_2CH_name,pathSave,typeRe):
#Load initial images
SA = ants.image_read(patientPath+SA_name)
SA = ants.resample_image(SA,[min(SA.spacing),min(SA.spacing),min(SA.spacing)])
LA_4CH = ants.image_read(patientPath+LA_4CH_name)
LA_2CH = ants.image_read(patientPath+LA_2CH_name)
#Registration
regSA_4CH = ants.registration(SA,LA_4CH,type_of_transform = typeRe,aff_metric = 'mattes')
regSA_2CH = ants.registration(SA,LA_2CH,type_of_transform = typeRe,aff_metric = 'mattes')
print('Registration Done')
#ROI extraction
clone4CH = ants.image_clone(LA_4CH)
clone2CH = ants.image_clone(LA_2CH)
roi_4CH = overROI(clone4CH,regSA_4CH['fwdtransforms'],SA)
roi_2CH = overROI(clone2CH,regSA_2CH['fwdtransforms'],SA)
roi_4CHArr = roi_4CH.view()
roi_4CHArr = roi_4CHArr == 1
roi_2CHArr = roi_2CH.view()
roi_2CHArr = roi_2CHArr == 1
print('ROI extraction Done')
new4CH = ants.to_nibabel(regSA_4CH['warpedmovout'])
new4CH.set_data_dtype('int16')
new2CH = ants.to_nibabel(regSA_2CH['warpedmovout'])
new2CH.set_data_dtype('int16')
nib.save(new4CH,pathSave + '4CH.nii')
nib.save(new2CH,pathSave + '2CH.nii')
#Normalization
new4CH_CH = regSA_4CH['warpedmovout'].view()
new2CH_CH = regSA_2CH['warpedmovout'].view()
short_CH = SA.view()
for t in range(0,new2CH_CH.shape[2]):
short_CH[:,:,t] = normOver(short_CH[:,:,t],roi_4CHArr[:,:,t]+roi_2CHArr[:,:,t])
new4CH_CH[:,:,t] = normOver(new4CH_CH[:,:,t],roi_4CHArr[:,:,t])
new2CH_CH[:,:,t] = normOver(new2CH_CH[:,:,t],roi_2CHArr[:,:,t])
new4CH = ants.to_nibabel(regSA_4CH['warpedmovout'])
new4CH.set_data_dtype('int16')
new2CH = ants.to_nibabel(regSA_2CH['warpedmovout'])
new2CH.set_data_dtype('int16')
shortNorm = ants.to_nibabel(SA)
shortNorm.set_data_dtype('int16')
nib.save(new4CH,pathSave + 'norm_4CH.nii')
nib.save(new2CH,pathSave + 'norm_2CH.nii')
nib.save(shortNorm,pathSave + 'norm_SA.nii')
print('Normalization Done')
#Checkboard
ch_4CH = np.zeros(short_CH.shape)
ch_2CH =np.zeros(short_CH.shape)
for t in np.arange(0,short_CH.shape[2]):
ch_4CH[:,:,t] = cheBoard(short_CH[:,:,t],new4CH_CH[:,:,t],16,roi_4CHArr[:,:,t])
ch_2CH[:,:,t] = cheBoard(short_CH[:,:,t],new2CH_CH[:,:,t],16,roi_2CHArr[:,:,t])
chest_4CH = nib.Nifti1Image(ch_4CH,new4CH.affine,new4CH.header)
chest_2CH = nib.Nifti1Image(ch_2CH,new2CH.affine,new2CH.header)
print("Checkboard filter applied")
nib.save(chest_4CH,pathSave + 'chest_4CH.nii')
nib.save(chest_2CH,pathSave + 'chest_2CH.nii')
print("Done")
final = (SA,regSA_4CH,regSA_2CH)
return final
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 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 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 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)
